[HMM 00/15] HMM (Heterogeneous Memory Management) v20
Patchset is on top of mmotm mmotm-2017-04-18 and Michal patchset ([PATCH -v3 0/13] mm: make movable onlining suck less). Branch: https://cgit.freedesktop.org/~glisse/linux/log/?h=hmm-v20 I have included all suggestion made since v19, it is all build fix and change in respect to memory hotplug with Michal rework. Changes since v19: - Included various build fix and compilation warning fix - Limit HMM to x86-64 (easy to enable other arch as separate patch) - Rebase on top of Michal memory hotplug rework 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, …) 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, …) but this get extremely complex with advance data structure (list, tree, graph, …) 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, …). 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
[HMM 00/15] HMM (Heterogeneous Memory Management) v20
Patchset is on top of mmotm mmotm-2017-04-18 and Michal patchset ([PATCH -v3 0/13] mm: make movable onlining suck less). Branch: https://cgit.freedesktop.org/~glisse/linux/log/?h=hmm-v20 I have included all suggestion made since v19, it is all build fix and change in respect to memory hotplug with Michal rework. Changes since v19: - Included various build fix and compilation warning fix - Limit HMM to x86-64 (easy to enable other arch as separate patch) - Rebase on top of Michal memory hotplug rework 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, …) 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, …) but this get extremely complex with advance data structure (list, tree, graph, …) 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, …). 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
