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hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Heterogeneous Memory Management (HMM)
=====================================
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Provide infrastructure and helpers to integrate non-conventional memory (device
memory like GPU on board memory) into regular kernel path, with the cornerstone
of this being specialized struct page for such memory (see sections 5 to 7 of
this document).
HMM also provides optional helpers for SVM (Share Virtual Memory), i.e.,
allowing a device to transparently access program addresses coherently with
the CPU meaning that any valid pointer on the CPU is also a valid pointer
for the device. This is becoming mandatory to simplify the use of advanced
heterogeneous computing where GPU, DSP, or FPGA are used to perform various
computations on behalf of a process.
This document is divided as follows: in the first section I expose the problems
related to using device specific memory allocators. In the second section, I
expose the hardware limitations that are inherent to many platforms. The third
section gives an overview of the HMM design. The fourth section explains how
CPU page-table mirroring works and the purpose of HMM in this context. The
fifth section deals with how device memory is represented inside the kernel.
Finally, the last section presents a new migration helper that allows
leveraging the device DMA engine.
.. contents:: :local:
Problems of using a device specific memory allocator
====================================================
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Devices with a large amount of on board memory (several gigabytes) like GPUs
have historically managed their memory through dedicated driver specific APIs.
This creates a disconnect between memory allocated and managed by a device
driver and regular application memory (private anonymous, shared memory, or
regular file backed memory). From here on I will refer to this aspect as split
address space. I use shared address space to refer to the opposite situation:
i.e., one in which any application memory region can be used by a device
transparently.
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Split address space happens because devices can only access memory allocated
through a device specific API. This implies that all memory objects in a program
are not equal from the device point of view which complicates large programs
that rely on a wide set of libraries.
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Concretely, this means that code that wants to leverage devices like GPUs needs
to copy objects between generically allocated memory (malloc, mmap private, mmap
share) and memory allocated through the device driver API (this still ends up
with an mmap but of the device file).
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
For flat data sets (array, grid, image, ...) this isn't too hard to achieve but
for complex data sets (list, tree, ...) it's hard to get right. Duplicating a
complex data set needs to re-map all the pointer relations between each of its
elements. This is error prone and programs get harder to debug because of the
duplicate data set and addresses.
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Split address space also means that libraries cannot transparently use data
they are getting from the core program or another library and thus each library
might have to duplicate its input data set using the device specific memory
allocator. Large projects suffer from this and waste resources because of the
various memory copies.
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Duplicating each library API to accept as input or output memory allocated by
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
each device specific allocator is not a viable option. It would lead to a
combinatorial explosion in the library entry points.
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Finally, with the advance of high level language constructs (in C++ but in
other languages too) it is now possible for the compiler to leverage GPUs and
other devices without programmer knowledge. Some compiler identified patterns
are only doable with a shared address space. It is also more reasonable to use
a shared address space for all other patterns.
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
I/O bus, device memory characteristics
======================================
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
I/O buses cripple shared address spaces due to a few limitations. Most I/O
buses only allow basic memory access from device to main memory; even cache
coherency is often optional. Access to device memory from a CPU is even more
limited. More often than not, it is not cache coherent.
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
If we only consider the PCIE bus, then a device can access main memory (often
through an IOMMU) and be cache coherent with the CPUs. However, it only allows
a limited set of atomic operations from the device on main memory. This is worse
in the other direction: the CPU can only access a limited range of the device
memory and cannot perform atomic operations on it. Thus device memory cannot
be considered the same as regular memory from the kernel point of view.
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Another crippling factor is the limited bandwidth (~32GBytes/s with PCIE 4.0
and 16 lanes). This is 33 times less than the fastest GPU memory (1 TBytes/s).
The final limitation is latency. Access to main memory from the device has an
order of magnitude higher latency than when the device accesses its own memory.
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Some platforms are developing new I/O buses or additions/modifications to PCIE
to address some of these limitations (OpenCAPI, CCIX). They mainly allow
two-way cache coherency between CPU and device and allow all atomic operations the
architecture supports. Sadly, not all platforms are following this trend and
some major architectures are left without hardware solutions to these problems.
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
So for shared address space to make sense, not only must we allow devices to
access any memory but we must also permit any memory to be migrated to device
memory while the device is using it (blocking CPU access while it happens).
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Shared address space and migration
==================================
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
HMM intends to provide two main features. The first one is to share the address
space by duplicating the CPU page table in the device page table so the same
address points to the same physical memory for any valid main memory address in
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
the process address space.
To achieve this, HMM offers a set of helpers to populate the device page table
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
while keeping track of CPU page table updates. Device page table updates are
not as easy as CPU page table updates. To update the device page table, you must
allocate a buffer (or use a pool of pre-allocated buffers) and write GPU
specific commands in it to perform the update (unmap, cache invalidations, and
flush, ...). This cannot be done through common code for all devices. Hence
why HMM provides helpers to factor out everything that can be while leaving the
hardware specific details to the device driver.
The second mechanism HMM provides is a new kind of ZONE_DEVICE memory that
allows allocating a struct page for each page of device memory. Those pages
are special because the CPU cannot map them. However, they allow migrating
main memory to device memory using existing migration mechanisms and everything
looks like a page that is swapped out to disk from the CPU point of view. Using a
struct page gives the easiest and cleanest integration with existing mm
mechanisms. Here again, HMM only provides helpers, first to hotplug new ZONE_DEVICE
memory for the device memory and second to perform migration. Policy decisions
of what and when to migrate is left to the device driver.
Note that any CPU access to a device page triggers a page fault and a migration
back to main memory. For example, when a page backing a given CPU address A is
migrated from a main memory page to a device page, then any CPU access to
address A triggers a page fault and initiates a migration back to main memory.
With these two features, HMM not only allows a device to mirror process address
space and keeps both CPU and device page tables synchronized, but also
leverages device memory by migrating the part of the data set that is actively being
used by the device.
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Address space mirroring implementation and API
==============================================
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Address space mirroring's main objective is to allow duplication of a range of
CPU page table into a device page table; HMM helps keep both synchronized. A
device driver that wants to mirror a process address space must start with the
registration of a mmu_interval_notifier::
int mmu_interval_notifier_insert(struct mmu_interval_notifier *interval_sub,
struct mm_struct *mm, unsigned long start,
unsigned long length,
const struct mmu_interval_notifier_ops *ops);
During the ops->invalidate() callback the device driver must perform the
update action to the range (mark range read only, or fully unmap, etc.). The
device must complete the update before the driver callback returns.
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
When the device driver wants to populate a range of virtual addresses, it can
use::
int hmm_range_fault(struct hmm_range *range);
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
It will trigger a page fault on missing or read-only entries if write access is
requested (see below). Page faults use the generic mm page fault code path just
like a CPU page fault. The usage pattern is::
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
int driver_populate_range(...)
{
struct hmm_range range;
...
range.notifier = &interval_sub;
range.start = ...;
range.end = ...;
mm/hmm: remove the customizable pfn format from hmm_range_fault Presumably the intent here was that hmm_range_fault() could put the data into some HW specific format and thus avoid some work. However, nothing actually does that, and it isn't clear how anything actually could do that as hmm_range_fault() provides CPU addresses which must be DMA mapped. Perhaps there is some special HW that does not need DMA mapping, but we don't have any examples of this, and the theoretical performance win of avoiding an extra scan over the pfns array doesn't seem worth the complexity. Plus pfns needs to be scanned anyhow to sort out any DEVICE_PRIVATE pages. This version replaces the uint64_t with an usigned long containing a pfn and fixed flags. On input flags is filled with the HMM_PFN_REQ_* values, on successful output it is filled with HMM_PFN_* values, describing the state of the pages. amdgpu is simple to convert, it doesn't use snapshot and doesn't use per-page flags. nouveau uses only 16 hmm_pte entries at most (ie fits in a few cache lines), and it sweeps over its pfns array a couple of times anyhow. It also has a nasty call chain before it reaches the dma map and hardware suggesting performance isn't important: nouveau_svm_fault(): args.i.m.method = NVIF_VMM_V0_PFNMAP nouveau_range_fault() nvif_object_ioctl() client->driver->ioctl() struct nvif_driver nvif_driver_nvkm: .ioctl = nvkm_client_ioctl nvkm_ioctl() nvkm_ioctl_path() nvkm_ioctl_v0[type].func(..) nvkm_ioctl_mthd() nvkm_object_mthd() struct nvkm_object_func nvkm_uvmm: .mthd = nvkm_uvmm_mthd nvkm_uvmm_mthd() nvkm_uvmm_mthd_pfnmap() nvkm_vmm_pfn_map() nvkm_vmm_ptes_get_map() func == gp100_vmm_pgt_pfn struct nvkm_vmm_desc_func gp100_vmm_desc_spt: .pfn = gp100_vmm_pgt_pfn nvkm_vmm_iter() REF_PTES == func == gp100_vmm_pgt_pfn() dma_map_page() Link: https://lore.kernel.org/r/5-v2-b4e84f444c7d+24f57-hmm_no_flags_jgg@mellanox.com Acked-by: Felix Kuehling <Felix.Kuehling@amd.com> Tested-by: Ralph Campbell <rcampbell@nvidia.com> Signed-off-by: Christoph Hellwig <hch@lst.de> Reviewed-by: Christoph Hellwig <hch@lst.de> Signed-off-by: Jason Gunthorpe <jgg@mellanox.com>
2020-05-01 18:20:48 +00:00
range.hmm_pfns = ...;
if (!mmget_not_zero(interval_sub->notifier.mm))
return -EFAULT;
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
again:
range.notifier_seq = mmu_interval_read_begin(&interval_sub);
mmap_read_lock(mm);
ret = hmm_range_fault(&range);
if (ret) {
mmap_read_unlock(mm);
if (ret == -EBUSY)
goto again;
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
return ret;
}
mmap_read_unlock(mm);
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
take_lock(driver->update);
if (mmu_interval_read_retry(&ni, range.notifier_seq) {
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
release_lock(driver->update);
goto again;
}
/* Use pfns array content to update device page table,
* under the update lock */
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
release_lock(driver->update);
return 0;
}
The driver->update lock is the same lock that the driver takes inside its
invalidate() callback. That lock must be held before calling
mmu_interval_read_retry() to avoid any race with a concurrent CPU page table
update.
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Leverage default_flags and pfn_flags_mask
=========================================
The hmm_range struct has 2 fields, default_flags and pfn_flags_mask, that specify
fault or snapshot policy for the whole range instead of having to set them
for each entry in the pfns array.
mm/hmm: remove the customizable pfn format from hmm_range_fault Presumably the intent here was that hmm_range_fault() could put the data into some HW specific format and thus avoid some work. However, nothing actually does that, and it isn't clear how anything actually could do that as hmm_range_fault() provides CPU addresses which must be DMA mapped. Perhaps there is some special HW that does not need DMA mapping, but we don't have any examples of this, and the theoretical performance win of avoiding an extra scan over the pfns array doesn't seem worth the complexity. Plus pfns needs to be scanned anyhow to sort out any DEVICE_PRIVATE pages. This version replaces the uint64_t with an usigned long containing a pfn and fixed flags. On input flags is filled with the HMM_PFN_REQ_* values, on successful output it is filled with HMM_PFN_* values, describing the state of the pages. amdgpu is simple to convert, it doesn't use snapshot and doesn't use per-page flags. nouveau uses only 16 hmm_pte entries at most (ie fits in a few cache lines), and it sweeps over its pfns array a couple of times anyhow. It also has a nasty call chain before it reaches the dma map and hardware suggesting performance isn't important: nouveau_svm_fault(): args.i.m.method = NVIF_VMM_V0_PFNMAP nouveau_range_fault() nvif_object_ioctl() client->driver->ioctl() struct nvif_driver nvif_driver_nvkm: .ioctl = nvkm_client_ioctl nvkm_ioctl() nvkm_ioctl_path() nvkm_ioctl_v0[type].func(..) nvkm_ioctl_mthd() nvkm_object_mthd() struct nvkm_object_func nvkm_uvmm: .mthd = nvkm_uvmm_mthd nvkm_uvmm_mthd() nvkm_uvmm_mthd_pfnmap() nvkm_vmm_pfn_map() nvkm_vmm_ptes_get_map() func == gp100_vmm_pgt_pfn struct nvkm_vmm_desc_func gp100_vmm_desc_spt: .pfn = gp100_vmm_pgt_pfn nvkm_vmm_iter() REF_PTES == func == gp100_vmm_pgt_pfn() dma_map_page() Link: https://lore.kernel.org/r/5-v2-b4e84f444c7d+24f57-hmm_no_flags_jgg@mellanox.com Acked-by: Felix Kuehling <Felix.Kuehling@amd.com> Tested-by: Ralph Campbell <rcampbell@nvidia.com> Signed-off-by: Christoph Hellwig <hch@lst.de> Reviewed-by: Christoph Hellwig <hch@lst.de> Signed-off-by: Jason Gunthorpe <jgg@mellanox.com>
2020-05-01 18:20:48 +00:00
For instance if the device driver wants pages for a range with at least read
permission, it sets::
mm/hmm: remove the customizable pfn format from hmm_range_fault Presumably the intent here was that hmm_range_fault() could put the data into some HW specific format and thus avoid some work. However, nothing actually does that, and it isn't clear how anything actually could do that as hmm_range_fault() provides CPU addresses which must be DMA mapped. Perhaps there is some special HW that does not need DMA mapping, but we don't have any examples of this, and the theoretical performance win of avoiding an extra scan over the pfns array doesn't seem worth the complexity. Plus pfns needs to be scanned anyhow to sort out any DEVICE_PRIVATE pages. This version replaces the uint64_t with an usigned long containing a pfn and fixed flags. On input flags is filled with the HMM_PFN_REQ_* values, on successful output it is filled with HMM_PFN_* values, describing the state of the pages. amdgpu is simple to convert, it doesn't use snapshot and doesn't use per-page flags. nouveau uses only 16 hmm_pte entries at most (ie fits in a few cache lines), and it sweeps over its pfns array a couple of times anyhow. It also has a nasty call chain before it reaches the dma map and hardware suggesting performance isn't important: nouveau_svm_fault(): args.i.m.method = NVIF_VMM_V0_PFNMAP nouveau_range_fault() nvif_object_ioctl() client->driver->ioctl() struct nvif_driver nvif_driver_nvkm: .ioctl = nvkm_client_ioctl nvkm_ioctl() nvkm_ioctl_path() nvkm_ioctl_v0[type].func(..) nvkm_ioctl_mthd() nvkm_object_mthd() struct nvkm_object_func nvkm_uvmm: .mthd = nvkm_uvmm_mthd nvkm_uvmm_mthd() nvkm_uvmm_mthd_pfnmap() nvkm_vmm_pfn_map() nvkm_vmm_ptes_get_map() func == gp100_vmm_pgt_pfn struct nvkm_vmm_desc_func gp100_vmm_desc_spt: .pfn = gp100_vmm_pgt_pfn nvkm_vmm_iter() REF_PTES == func == gp100_vmm_pgt_pfn() dma_map_page() Link: https://lore.kernel.org/r/5-v2-b4e84f444c7d+24f57-hmm_no_flags_jgg@mellanox.com Acked-by: Felix Kuehling <Felix.Kuehling@amd.com> Tested-by: Ralph Campbell <rcampbell@nvidia.com> Signed-off-by: Christoph Hellwig <hch@lst.de> Reviewed-by: Christoph Hellwig <hch@lst.de> Signed-off-by: Jason Gunthorpe <jgg@mellanox.com>
2020-05-01 18:20:48 +00:00
range->default_flags = HMM_PFN_REQ_FAULT;
range->pfn_flags_mask = 0;
and calls hmm_range_fault() as described above. This will fill fault all pages
in the range with at least read permission.
Now let's say the driver wants to do the same except for one page in the range for
which it wants to have write permission. Now driver set::
mm/hmm: remove the customizable pfn format from hmm_range_fault Presumably the intent here was that hmm_range_fault() could put the data into some HW specific format and thus avoid some work. However, nothing actually does that, and it isn't clear how anything actually could do that as hmm_range_fault() provides CPU addresses which must be DMA mapped. Perhaps there is some special HW that does not need DMA mapping, but we don't have any examples of this, and the theoretical performance win of avoiding an extra scan over the pfns array doesn't seem worth the complexity. Plus pfns needs to be scanned anyhow to sort out any DEVICE_PRIVATE pages. This version replaces the uint64_t with an usigned long containing a pfn and fixed flags. On input flags is filled with the HMM_PFN_REQ_* values, on successful output it is filled with HMM_PFN_* values, describing the state of the pages. amdgpu is simple to convert, it doesn't use snapshot and doesn't use per-page flags. nouveau uses only 16 hmm_pte entries at most (ie fits in a few cache lines), and it sweeps over its pfns array a couple of times anyhow. It also has a nasty call chain before it reaches the dma map and hardware suggesting performance isn't important: nouveau_svm_fault(): args.i.m.method = NVIF_VMM_V0_PFNMAP nouveau_range_fault() nvif_object_ioctl() client->driver->ioctl() struct nvif_driver nvif_driver_nvkm: .ioctl = nvkm_client_ioctl nvkm_ioctl() nvkm_ioctl_path() nvkm_ioctl_v0[type].func(..) nvkm_ioctl_mthd() nvkm_object_mthd() struct nvkm_object_func nvkm_uvmm: .mthd = nvkm_uvmm_mthd nvkm_uvmm_mthd() nvkm_uvmm_mthd_pfnmap() nvkm_vmm_pfn_map() nvkm_vmm_ptes_get_map() func == gp100_vmm_pgt_pfn struct nvkm_vmm_desc_func gp100_vmm_desc_spt: .pfn = gp100_vmm_pgt_pfn nvkm_vmm_iter() REF_PTES == func == gp100_vmm_pgt_pfn() dma_map_page() Link: https://lore.kernel.org/r/5-v2-b4e84f444c7d+24f57-hmm_no_flags_jgg@mellanox.com Acked-by: Felix Kuehling <Felix.Kuehling@amd.com> Tested-by: Ralph Campbell <rcampbell@nvidia.com> Signed-off-by: Christoph Hellwig <hch@lst.de> Reviewed-by: Christoph Hellwig <hch@lst.de> Signed-off-by: Jason Gunthorpe <jgg@mellanox.com>
2020-05-01 18:20:48 +00:00
range->default_flags = HMM_PFN_REQ_FAULT;
range->pfn_flags_mask = HMM_PFN_REQ_WRITE;
range->pfns[index_of_write] = HMM_PFN_REQ_WRITE;
With this, HMM will fault in all pages with at least read (i.e., valid) and for the
address == range->start + (index_of_write << PAGE_SHIFT) it will fault with
write permission i.e., if the CPU pte does not have write permission set then HMM
will call handle_mm_fault().
mm/hmm: remove the customizable pfn format from hmm_range_fault Presumably the intent here was that hmm_range_fault() could put the data into some HW specific format and thus avoid some work. However, nothing actually does that, and it isn't clear how anything actually could do that as hmm_range_fault() provides CPU addresses which must be DMA mapped. Perhaps there is some special HW that does not need DMA mapping, but we don't have any examples of this, and the theoretical performance win of avoiding an extra scan over the pfns array doesn't seem worth the complexity. Plus pfns needs to be scanned anyhow to sort out any DEVICE_PRIVATE pages. This version replaces the uint64_t with an usigned long containing a pfn and fixed flags. On input flags is filled with the HMM_PFN_REQ_* values, on successful output it is filled with HMM_PFN_* values, describing the state of the pages. amdgpu is simple to convert, it doesn't use snapshot and doesn't use per-page flags. nouveau uses only 16 hmm_pte entries at most (ie fits in a few cache lines), and it sweeps over its pfns array a couple of times anyhow. It also has a nasty call chain before it reaches the dma map and hardware suggesting performance isn't important: nouveau_svm_fault(): args.i.m.method = NVIF_VMM_V0_PFNMAP nouveau_range_fault() nvif_object_ioctl() client->driver->ioctl() struct nvif_driver nvif_driver_nvkm: .ioctl = nvkm_client_ioctl nvkm_ioctl() nvkm_ioctl_path() nvkm_ioctl_v0[type].func(..) nvkm_ioctl_mthd() nvkm_object_mthd() struct nvkm_object_func nvkm_uvmm: .mthd = nvkm_uvmm_mthd nvkm_uvmm_mthd() nvkm_uvmm_mthd_pfnmap() nvkm_vmm_pfn_map() nvkm_vmm_ptes_get_map() func == gp100_vmm_pgt_pfn struct nvkm_vmm_desc_func gp100_vmm_desc_spt: .pfn = gp100_vmm_pgt_pfn nvkm_vmm_iter() REF_PTES == func == gp100_vmm_pgt_pfn() dma_map_page() Link: https://lore.kernel.org/r/5-v2-b4e84f444c7d+24f57-hmm_no_flags_jgg@mellanox.com Acked-by: Felix Kuehling <Felix.Kuehling@amd.com> Tested-by: Ralph Campbell <rcampbell@nvidia.com> Signed-off-by: Christoph Hellwig <hch@lst.de> Reviewed-by: Christoph Hellwig <hch@lst.de> Signed-off-by: Jason Gunthorpe <jgg@mellanox.com>
2020-05-01 18:20:48 +00:00
After hmm_range_fault completes the flag bits are set to the current state of
the page tables, ie HMM_PFN_VALID | HMM_PFN_WRITE will be set if the page is
writable.
Represent and manage device memory from core kernel point of view
=================================================================
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Several different designs were tried to support device memory. The first one
used a device specific data structure to keep information about migrated memory
and HMM hooked itself in various places of mm code to handle any access to
addresses that were backed by device memory. It turns out that this ended up
replicating most of the fields of struct page and also needed many kernel code
paths to be updated to understand this new kind of memory.
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Most kernel code paths never try to access the memory behind a page
but only care about struct page contents. Because of this, HMM switched to
directly using struct page for device memory which left most kernel code paths
unaware of the difference. We only need to make sure that no one ever tries to
map those pages from the CPU side.
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Migration to and from device memory
===================================
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
Because the CPU cannot access device memory directly, the device driver must
use hardware DMA or device specific load/store instructions to migrate data.
The migrate_vma_setup(), migrate_vma_pages(), and migrate_vma_finalize()
functions are designed to make drivers easier to write and to centralize common
code across drivers.
Before migrating pages to device private memory, special device private
``struct page`` needs to be created. These will be used as special "swap"
page table entries so that a CPU process will fault if it tries to access
a page that has been migrated to device private memory.
These can be allocated and freed with::
struct resource *res;
struct dev_pagemap pagemap;
res = request_free_mem_region(&iomem_resource, /* number of bytes */,
"name of driver resource");
pagemap.type = MEMORY_DEVICE_PRIVATE;
pagemap.range.start = res->start;
pagemap.range.end = res->end;
pagemap.nr_range = 1;
pagemap.ops = &device_devmem_ops;
memremap_pages(&pagemap, numa_node_id());
memunmap_pages(&pagemap);
release_mem_region(pagemap.range.start, range_len(&pagemap.range));
There are also devm_request_free_mem_region(), devm_memremap_pages(),
devm_memunmap_pages(), and devm_release_mem_region() when the resources can
be tied to a ``struct device``.
The overall migration steps are similar to migrating NUMA pages within system
memory (see Documentation/mm/page_migration.rst) but the steps are split
between device driver specific code and shared common code:
1. ``mmap_read_lock()``
The device driver has to pass a ``struct vm_area_struct`` to
migrate_vma_setup() so the mmap_read_lock() or mmap_write_lock() needs to
be held for the duration of the migration.
2. ``migrate_vma_setup(struct migrate_vma *args)``
The device driver initializes the ``struct migrate_vma`` fields and passes
the pointer to migrate_vma_setup(). The ``args->flags`` field is used to
filter which source pages should be migrated. For example, setting
``MIGRATE_VMA_SELECT_SYSTEM`` will only migrate system memory and
``MIGRATE_VMA_SELECT_DEVICE_PRIVATE`` will only migrate pages residing in
device private memory. If the latter flag is set, the ``args->pgmap_owner``
field is used to identify device private pages owned by the driver. This
avoids trying to migrate device private pages residing in other devices.
Currently only anonymous private VMA ranges can be migrated to or from
system memory and device private memory.
One of the first steps migrate_vma_setup() does is to invalidate other
device's MMUs with the ``mmu_notifier_invalidate_range_start(()`` and
``mmu_notifier_invalidate_range_end()`` calls around the page table
walks to fill in the ``args->src`` array with PFNs to be migrated.
The ``invalidate_range_start()`` callback is passed a
``struct mmu_notifier_range`` with the ``event`` field set to
``MMU_NOTIFY_MIGRATE`` and the ``owner`` field set to
the ``args->pgmap_owner`` field passed to migrate_vma_setup(). This
allows the device driver to skip the invalidation callback and only
invalidate device private MMU mappings that are actually migrating.
This is explained more in the next section.
While walking the page tables, a ``pte_none()`` or ``is_zero_pfn()``
entry results in a valid "zero" PFN stored in the ``args->src`` array.
This lets the driver allocate device private memory and clear it instead
of copying a page of zeros. Valid PTE entries to system memory or
device private struct pages will be locked with ``lock_page()``, isolated
from the LRU (if system memory since device private pages are not on
the LRU), unmapped from the process, and a special migration PTE is
inserted in place of the original PTE.
migrate_vma_setup() also clears the ``args->dst`` array.
3. The device driver allocates destination pages and copies source pages to
destination pages.
The driver checks each ``src`` entry to see if the ``MIGRATE_PFN_MIGRATE``
bit is set and skips entries that are not migrating. The device driver
can also choose to skip migrating a page by not filling in the ``dst``
array for that page.
The driver then allocates either a device private struct page or a
system memory page, locks the page with ``lock_page()``, and fills in the
``dst`` array entry with::
dst[i] = migrate_pfn(page_to_pfn(dpage));
Now that the driver knows that this page is being migrated, it can
invalidate device private MMU mappings and copy device private memory
to system memory or another device private page. The core Linux kernel
handles CPU page table invalidations so the device driver only has to
invalidate its own MMU mappings.
The driver can use ``migrate_pfn_to_page(src[i])`` to get the
``struct page`` of the source and either copy the source page to the
destination or clear the destination device private memory if the pointer
is ``NULL`` meaning the source page was not populated in system memory.
4. ``migrate_vma_pages()``
This step is where the migration is actually "committed".
If the source page was a ``pte_none()`` or ``is_zero_pfn()`` page, this
is where the newly allocated page is inserted into the CPU's page table.
This can fail if a CPU thread faults on the same page. However, the page
table is locked and only one of the new pages will be inserted.
The device driver will see that the ``MIGRATE_PFN_MIGRATE`` bit is cleared
if it loses the race.
If the source page was locked, isolated, etc. the source ``struct page``
information is now copied to destination ``struct page`` finalizing the
migration on the CPU side.
5. Device driver updates device MMU page tables for pages still migrating,
rolling back pages not migrating.
If the ``src`` entry still has ``MIGRATE_PFN_MIGRATE`` bit set, the device
driver can update the device MMU and set the write enable bit if the
``MIGRATE_PFN_WRITE`` bit is set.
6. ``migrate_vma_finalize()``
This step replaces the special migration page table entry with the new
page's page table entry and releases the reference to the source and
destination ``struct page``.
7. ``mmap_read_unlock()``
The lock can now be released.
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
mm: device exclusive memory access Some devices require exclusive write access to shared virtual memory (SVM) ranges to perform atomic operations on that memory. This requires CPU page tables to be updated to deny access whilst atomic operations are occurring. In order to do this introduce a new swap entry type (SWP_DEVICE_EXCLUSIVE). When a SVM range needs to be marked for exclusive access by a device all page table mappings for the particular range are replaced with device exclusive swap entries. This causes any CPU access to the page to result in a fault. Faults are resovled by replacing the faulting entry with the original mapping. This results in MMU notifiers being called which a driver uses to update access permissions such as revoking atomic access. After notifiers have been called the device will no longer have exclusive access to the region. Walking of the page tables to find the target pages is handled by get_user_pages() rather than a direct page table walk. A direct page table walk similar to what migrate_vma_collect()/unmap() does could also have been utilised. However this resulted in more code similar in functionality to what get_user_pages() provides as page faulting is required to make the PTEs present and to break COW. [dan.carpenter@oracle.com: fix signedness bug in make_device_exclusive_range()] Link: https://lkml.kernel.org/r/YNIz5NVnZ5GiZ3u1@mwanda Link: https://lkml.kernel.org/r/20210616105937.23201-8-apopple@nvidia.com Signed-off-by: Alistair Popple <apopple@nvidia.com> Signed-off-by: Dan Carpenter <dan.carpenter@oracle.com> Reviewed-by: Christoph Hellwig <hch@lst.de> Cc: Ben Skeggs <bskeggs@redhat.com> Cc: Hugh Dickins <hughd@google.com> Cc: Jason Gunthorpe <jgg@nvidia.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: "Matthew Wilcox (Oracle)" <willy@infradead.org> Cc: Peter Xu <peterx@redhat.com> Cc: Ralph Campbell <rcampbell@nvidia.com> Cc: Shakeel Butt <shakeelb@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-07-01 01:54:25 +00:00
Exclusive access memory
=======================
Some devices have features such as atomic PTE bits that can be used to implement
atomic access to system memory. To support atomic operations to a shared virtual
memory page such a device needs access to that page which is exclusive of any
userspace access from the CPU. The ``make_device_exclusive_range()`` function
can be used to make a memory range inaccessible from userspace.
This replaces all mappings for pages in the given range with special swap
entries. Any attempt to access the swap entry results in a fault which is
resolved by replacing the entry with the original mapping. A driver gets
mm: device exclusive memory access Some devices require exclusive write access to shared virtual memory (SVM) ranges to perform atomic operations on that memory. This requires CPU page tables to be updated to deny access whilst atomic operations are occurring. In order to do this introduce a new swap entry type (SWP_DEVICE_EXCLUSIVE). When a SVM range needs to be marked for exclusive access by a device all page table mappings for the particular range are replaced with device exclusive swap entries. This causes any CPU access to the page to result in a fault. Faults are resovled by replacing the faulting entry with the original mapping. This results in MMU notifiers being called which a driver uses to update access permissions such as revoking atomic access. After notifiers have been called the device will no longer have exclusive access to the region. Walking of the page tables to find the target pages is handled by get_user_pages() rather than a direct page table walk. A direct page table walk similar to what migrate_vma_collect()/unmap() does could also have been utilised. However this resulted in more code similar in functionality to what get_user_pages() provides as page faulting is required to make the PTEs present and to break COW. [dan.carpenter@oracle.com: fix signedness bug in make_device_exclusive_range()] Link: https://lkml.kernel.org/r/YNIz5NVnZ5GiZ3u1@mwanda Link: https://lkml.kernel.org/r/20210616105937.23201-8-apopple@nvidia.com Signed-off-by: Alistair Popple <apopple@nvidia.com> Signed-off-by: Dan Carpenter <dan.carpenter@oracle.com> Reviewed-by: Christoph Hellwig <hch@lst.de> Cc: Ben Skeggs <bskeggs@redhat.com> Cc: Hugh Dickins <hughd@google.com> Cc: Jason Gunthorpe <jgg@nvidia.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: "Matthew Wilcox (Oracle)" <willy@infradead.org> Cc: Peter Xu <peterx@redhat.com> Cc: Ralph Campbell <rcampbell@nvidia.com> Cc: Shakeel Butt <shakeelb@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-07-01 01:54:25 +00:00
notified that the mapping has been changed by MMU notifiers, after which point
it will no longer have exclusive access to the page. Exclusive access is
guaranteed to last until the driver drops the page lock and page reference, at
mm: device exclusive memory access Some devices require exclusive write access to shared virtual memory (SVM) ranges to perform atomic operations on that memory. This requires CPU page tables to be updated to deny access whilst atomic operations are occurring. In order to do this introduce a new swap entry type (SWP_DEVICE_EXCLUSIVE). When a SVM range needs to be marked for exclusive access by a device all page table mappings for the particular range are replaced with device exclusive swap entries. This causes any CPU access to the page to result in a fault. Faults are resovled by replacing the faulting entry with the original mapping. This results in MMU notifiers being called which a driver uses to update access permissions such as revoking atomic access. After notifiers have been called the device will no longer have exclusive access to the region. Walking of the page tables to find the target pages is handled by get_user_pages() rather than a direct page table walk. A direct page table walk similar to what migrate_vma_collect()/unmap() does could also have been utilised. However this resulted in more code similar in functionality to what get_user_pages() provides as page faulting is required to make the PTEs present and to break COW. [dan.carpenter@oracle.com: fix signedness bug in make_device_exclusive_range()] Link: https://lkml.kernel.org/r/YNIz5NVnZ5GiZ3u1@mwanda Link: https://lkml.kernel.org/r/20210616105937.23201-8-apopple@nvidia.com Signed-off-by: Alistair Popple <apopple@nvidia.com> Signed-off-by: Dan Carpenter <dan.carpenter@oracle.com> Reviewed-by: Christoph Hellwig <hch@lst.de> Cc: Ben Skeggs <bskeggs@redhat.com> Cc: Hugh Dickins <hughd@google.com> Cc: Jason Gunthorpe <jgg@nvidia.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: "Matthew Wilcox (Oracle)" <willy@infradead.org> Cc: Peter Xu <peterx@redhat.com> Cc: Ralph Campbell <rcampbell@nvidia.com> Cc: Shakeel Butt <shakeelb@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-07-01 01:54:25 +00:00
which point any CPU faults on the page may proceed as described.
Memory cgroup (memcg) and rss accounting
========================================
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
For now, device memory is accounted as any regular page in rss counters (either
anonymous if device page is used for anonymous, file if device page is used for
file backed page, or shmem if device page is used for shared memory). This is a
deliberate choice to keep existing applications, that might start using device
memory without knowing about it, running unimpacted.
A drawback is that the OOM killer might kill an application using a lot of
device memory and not a lot of regular system memory and thus not freeing much
system memory. We want to gather more real world experience on how applications
and system react under memory pressure in the presence of device memory before
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
deciding to account device memory differently.
Same decision was made for memory cgroup. Device memory pages are accounted
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
against same memory cgroup a regular page would be accounted to. This does
simplify migration to and from device memory. This also means that migration
back from device memory to regular memory cannot fail because it would
go above memory cgroup limit. We might revisit this choice later on once we
get more experience in how device memory is used and its impact on memory
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2017-09-08 23:11:19 +00:00
resource control.
Note that device memory can never be pinned by a device driver nor through GUP
hmm: heterogeneous memory management documentation Patch series "HMM (Heterogeneous Memory Management)", v25. Heterogeneous Memory Management (HMM) (description and justification) Today device driver expose dedicated memory allocation API through their device file, often relying on a combination of IOCTL and mmap calls. The device can only access and use memory allocated through this API. This effectively split the program address space into object allocated for the device and useable by the device and other regular memory (malloc, mmap of a file, share memory, â) only accessible by CPU (or in a very limited way by a device by pinning memory). Allowing different isolated component of a program to use a device thus require duplication of the input data structure using device memory allocator. This is reasonable for simple data structure (array, grid, image, â) but this get extremely complex with advance data structure (list, tree, graph, â) that rely on a web of memory pointers. This is becoming a serious limitation on the kind of work load that can be offloaded to device like GPU. New industry standard like C++, OpenCL or CUDA are pushing to remove this barrier. This require a shared address space between GPU device and CPU so that GPU can access any memory of a process (while still obeying memory protection like read only). This kind of feature is also appearing in various other operating systems. HMM is a set of helpers to facilitate several aspects of address space sharing and device memory management. Unlike existing sharing mechanism that rely on pining pages use by a device, HMM relies on mmu_notifier to propagate CPU page table update to device page table. Duplicating CPU page table is only one aspect necessary for efficiently using device like GPU. GPU local memory have bandwidth in the TeraBytes/ second range but they are connected to main memory through a system bus like PCIE that is limited to 32GigaBytes/second (PCIE 4.0 16x). Thus it is necessary to allow migration of process memory from main system memory to device memory. Issue is that on platform that only have PCIE the device memory is not accessible by the CPU with the same properties as main memory (cache coherency, atomic operations, ...). To allow migration from main memory to device memory HMM provides a set of helper to hotplug device memory as a new type of ZONE_DEVICE memory which is un-addressable by CPU but still has struct page representing it. This allow most of the core kernel logic that deals with a process memory to stay oblivious of the peculiarity of device memory. When page backing an address of a process is migrated to device memory the CPU page table entry is set to a new specific swap entry. CPU access to such address triggers a migration back to system memory, just like if the page was swap on disk. HMM also blocks any one from pinning a ZONE_DEVICE page so that it can always be migrated back to system memory if CPU access it. Conversely HMM does not migrate to device memory any page that is pin in system memory. To allow efficient migration between device memory and main memory a new migrate_vma() helpers is added with this patchset. It allows to leverage device DMA engine to perform the copy operation. This feature will be use by upstream driver like nouveau mlx5 and probably other in the future (amdgpu is next suspect in line). We are actively working on nouveau and mlx5 support. To test this patchset we also worked with NVidia close source driver team, they have more resources than us to test this kind of infrastructure and also a bigger and better userspace eco-system with various real industry workload they can be use to test and profile HMM. The expected workload is a program builds a data set on the CPU (from disk, from network, from sensors, â). Program uses GPU API (OpenCL, CUDA, ...) to give hint on memory placement for the input data and also for the output buffer. Program call GPU API to schedule a GPU job, this happens using device driver specific ioctl. All this is hidden from programmer point of view in case of C++ compiler that transparently offload some part of a program to GPU. Program can keep doing other stuff on the CPU while the GPU is crunching numbers. It is expected that CPU will not access the same data set as the GPU while GPU is working on it, but this is not mandatory. In fact we expect some small memory object to be actively access by both GPU and CPU concurrently as synchronization channel and/or for monitoring purposes. Such object will stay in system memory and should not be bottlenecked by system bus bandwidth (rare write and read access from both CPU and GPU). As we are relying on device driver API, HMM does not introduce any new syscall nor does it modify any existing ones. It does not change any POSIX semantics or behaviors. For instance the child after a fork of a process that is using HMM will not be impacted in anyway, nor is there any data hazard between child COW or parent COW of memory that was migrated to device prior to fork. HMM assume a numbers of hardware features. Device must allow device page table to be updated at any time (ie device job must be preemptable). Device page table must provides memory protection such as read only. Device must track write access (dirty bit). Device must have a minimum granularity that match PAGE_SIZE (ie 4k). Reviewer (just hint): Patch 1 HMM documentation Patch 2 introduce core infrastructure and definition of HMM, pretty small patch and easy to review Patch 3 introduce the mirror functionality of HMM, it relies on mmu_notifier and thus someone familiar with that part would be in better position to review Patch 4 is an helper to snapshot CPU page table while synchronizing with concurrent page table update. Understanding mmu_notifier makes review easier. Patch 5 is mostly a wrapper around handle_mm_fault() Patch 6 add new add_pages() helper to avoid modifying each arch memory hot plug function Patch 7 add a new memory type for ZONE_DEVICE and also add all the logic in various core mm to support this new type. Dan Williams and any core mm contributor are best people to review each half of this patchset Patch 8 special case HMM ZONE_DEVICE pages inside put_page() Kirill and Dan Williams are best person to review this Patch 9 allow to uncharge a page from memory group without using the lru list field of struct page (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 10 Add support to uncharge ZONE_DEVICE page from a memory cgroup (best reviewer: Johannes Weiner or Vladimir Davydov or Michal Hocko) Patch 11 add helper to hotplug un-addressable device memory as new type of ZONE_DEVICE memory (new type introducted in patch 3 of this serie). This is boiler plate code around memory hotplug and it also pick a free range of physical address for the device memory. Note that the physical address do not point to anything (at least as far as the kernel knows). Patch 12 introduce a new hmm_device class as an helper for device driver that want to expose multiple device memory under a common fake device driver. This is usefull for multi-gpu configuration. Anyone familiar with device driver infrastructure can review this. Boiler plate code really. Patch 13 add a new migrate mode. Any one familiar with page migration is welcome to review. Patch 14 introduce a new migration helper (migrate_vma()) that allow to migrate a range of virtual address of a process using device DMA engine to perform the copy. It is not limited to do copy from and to device but can also do copy between any kind of source and destination memory. Again anyone familiar with migration code should be able to verify the logic. Patch 15 optimize the new migrate_vma() by unmapping pages while we are collecting them. This can be review by any mm folks. Patch 16 add unaddressable memory migration to helper introduced in patch 7, this can be review by anyone familiar with migration code Patch 17 add a feature that allow device to allocate non-present page on the GPU when migrating a range of address to device memory. This is an helper for device driver to avoid having to first allocate system memory before migration to device memory Patch 18 add a new kind of ZONE_DEVICE memory for cache coherent device memory (CDM) Patch 19 add an helper to hotplug CDM memory Previous patchset posting : v1 http://lwn.net/Articles/597289/ v2 https://lkml.org/lkml/2014/6/12/559 v3 https://lkml.org/lkml/2014/6/13/633 v4 https://lkml.org/lkml/2014/8/29/423 v5 https://lkml.org/lkml/2014/11/3/759 v6 http://lwn.net/Articles/619737/ v7 http://lwn.net/Articles/627316/ v8 https://lwn.net/Articles/645515/ v9 https://lwn.net/Articles/651553/ v10 https://lwn.net/Articles/654430/ v11 http://www.gossamer-threads.com/lists/linux/kernel/2286424 v12 http://www.kernelhub.org/?msg=972982&p=2 v13 https://lwn.net/Articles/706856/ v14 https://lkml.org/lkml/2016/12/8/344 v15 http://www.mail-archive.com/linux-kernel@xxxxxxxxxxxxxxx/msg1304107.html v16 http://www.spinics.net/lists/linux-mm/msg119814.html v17 https://lkml.org/lkml/2017/1/27/847 v18 https://lkml.org/lkml/2017/3/16/596 v19 https://lkml.org/lkml/2017/4/5/831 v20 https://lwn.net/Articles/720715/ v21 https://lkml.org/lkml/2017/4/24/747 v22 http://lkml.iu.edu/hypermail/linux/kernel/1705.2/05176.html v23 https://www.mail-archive.com/linux-kernel@vger.kernel.org/msg1404788.html v24 https://lwn.net/Articles/726691/ This patch (of 19): This adds documentation for HMM (Heterogeneous Memory Management). It presents the motivation behind it, the features necessary for it to be useful and and gives an overview of how this is implemented. Link: http://lkml.kernel.org/r/20170817000548.32038-2-jglisse@redhat.com Signed-off-by: Jérôme Glisse <jglisse@redhat.com> Cc: John Hubbard <jhubbard@nvidia.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: David Nellans <dnellans@nvidia.com> Cc: Balbir Singh <bsingharora@gmail.com> Cc: Aneesh Kumar <aneesh.kumar@linux.vnet.ibm.com> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Evgeny Baskakov <ebaskakov@nvidia.com> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Kirill A. Shutemov <kirill.shutemov@linux.intel.com> Cc: Mark Hairgrove <mhairgrove@nvidia.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Ross Zwisler <ross.zwisler@linux.intel.com> Cc: Sherry Cheung <SCheung@nvidia.com> Cc: Subhash Gutti <sgutti@nvidia.com> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Bob Liu <liubo95@huawei.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
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and thus such memory is always free upon process exit. Or when last reference
is dropped in case of shared memory or file backed memory.