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authorJérôme Glisse <jglisse@redhat.com>2017-09-08 16:11:19 -0700
committerLinus Torvalds <torvalds@linux-foundation.org>2017-09-08 18:26:45 -0700
commitbffc33ec539699f045a9254144de3d4eace05f07 (patch)
tree38090d249701b57cb656d28b271dc42d0f30f9d5 /Documentation/virtual
parent8135d8926c08e553e39b0b040c6d01f0daef0676 (diff)
downloadlinux-bffc33ec539699f045a9254144de3d4eace05f07.tar.bz2
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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