On Wed, Apr 22, 2015 at 11:01:26AM +1000, Benjamin Herrenschmidt wrote: > On Tue, 2015-04-21 at 19:50 -0500, Christoph Lameter wrote: > > > With a filesystem the migration can be controlled by the application. It > > can copy stuff whenever it wants to.Having the OS do that behind my back > > is not something that feels safe and secure. > > But this is not something the user wants. The filesystem model is > completely the wrong model for us. > > This is fundamentally the same model as memory migrating between NUMA > nodes except that one of these is a co-processor with its local memory. > > You want to malloc() some stuff or get a pointer provided by an app to > your library and be able to farm that job out to the co-processor. No > filesystem in the picture here. I updated the document based on feedback thus far, and a big "thank you" to everyone! Diffs below, followed by the full document. Thanx, Paul ------------------------------------------------------------------------ diff --git a/DeviceMem.txt b/DeviceMem.txt index e2d65d585f03..cdedf2ee96e9 100644 --- a/DeviceMem.txt +++ b/DeviceMem.txt @@ -48,6 +48,25 @@ The purpose of this document is to explore how this access and migration can be provided for within the Linux kernel. + +USE CASES + + o GPGPU matrix operations, from Jerome Glisse. + https://lkml.org/lkml/2015/4/21/898 + + Suppose that you have an application that uses a + scientific library to do matrix computations, and that + this application simply calls malloc() and give the + resulting pointer to the library function. If the GPGPU + has coherent access to system memory (and vice versa), + it would help performance and application compatibility + to be able to transparently migrate the malloc()ed + memory to and from the GPGPU's memory without requiring + changes to the application. + + o (More here for CAPI.) + + REQUIREMENTS 1. It should be possible to remove a given CCAD device @@ -132,6 +151,9 @@ POTENTIAL IDEAS 4. Your idea here! +The following sections cover AutoNUMA, use of memory zones, and DAX. + + AUTONUMA The Linux kernel's autonuma facility supports migrating both @@ -178,6 +200,10 @@ AUTONUMA the memory would be temporarily inaccessible -- which could be a fatal surprise to that kernel subsystem. + Jerome Glisse suggests that usermode hints are quite important, + and perhaps should replace any AutoNUMA measurements. + + MEMORY ZONE One way to avoid the problem of random kernel subsystems using @@ -206,3 +232,26 @@ MEMORY ZONE Also, because large NUMA systems will sometimes interleave the addresses of blocks of physical memory and device memory, support for discontiguous interleaved zones will be required. + + +DAX + + DAX is a mechanism for providing direct-memory access to + high-speed non-volatile (AKA "persistent") memory. Good + introductions to DAX may be found in the following LWN + articles: + + https://lwn.net/Articles/591779/ + https://lwn.net/Articles/610174/ + + DAX provides filesystem-level access to persistent memory. + One important CCAD use case is allowing a legacy application + to pass memory from malloc() to a CCAD device, and having + the allocated memory migrate as needed. DAX does not seem to + support this use case. + + +ACKNOWLEDGMENTS + + Updates to this document include feedback from Christoph Lameter + and Jerome Glisse. ------------------------------------------------------------------------ COHERENT ON-DEVICE MEMORY: ACCESS AND MIGRATION Ben Herrenschmidt (As told to Paul E. McKenney) Special-purpose hardware becoming more prevalent, and some of this hardware allows for tight interaction with CPU-based processing. For example, IBM's coherent accelerator processor interface (CAPI) will allow this sort of device to be constructed, and it is likely that GPGPUs will need similar capabilities. (See http://www-304.ibm.com/webapp/set2/sas/f/capi/home.html for a high-level description of CAPI.) Let's call these cache-coherent accelerator devices (CCAD for short, which should at least motivate someone to come up with something better). This document covers devices with the following properties: 1. The device is cache-coherent, in other words, the device's memory has all the characteristics of system memory from the viewpoint of CPUs and other devices accessing it. 2. The device provides local memory that it has high-bandwidth low-latency access to, but the device can also access normal system memory. 3. The device shares system page tables, so that it can transparently access userspace virtual memory, regardless of whether this virtual memory maps to normal system memory or to memory local to the device. Although such a device will provide CPU's with cache-coherent access to on-device memory, the resulting memory latency is expected to be slower than the normal memory that is tightly coupled to the CPUs. Nevertheless, data that is only occasionally accessed by CPUs should be stored in the device's memory. On the other hand, data that is accessed rarely by the device but frequently by the CPUs should be stored in normal system memory. Of course, some workloads will have predictable access patterns that allow data to be optimally placed up front. However, other workloads will have less-predictable access patterns, and these workloads can benefit from automatic migration of data between device memory and system memory as access patterns change. Furthermore, some devices will provide special hardware that collects access statistics that can be used to determine whether or not a given page of memory should be migrated, and if so, to where. The purpose of this document is to explore how this access and migration can be provided for within the Linux kernel. USE CASES o GPGPU matrix operations, from Jerome Glisse. https://lkml.org/lkml/2015/4/21/898 Suppose that you have an application that uses a scientific library to do matrix computations, and that this application simply calls malloc() and give the resulting pointer to the library function. If the GPGPU has coherent access to system memory (and vice versa), it would help performance and application compatibility to be able to transparently migrate the malloc()ed memory to and from the GPGPU's memory without requiring changes to the application. o (More here for CAPI.) REQUIREMENTS 1. It should be possible to remove a given CCAD device from service, for example, to reset it, to download updated firmware, or to change its functionality. This results in the following additional requirements: a. It should be possible to migrate all data away from the device's memory at any time. b. Normal memory allocation should avoid using the device's memory, as this would interfere with the needed migration. It may nevertheless be desirable to use the device's memory if system memory is exhausted, however, in some cases, even this "emergency" use is best avoided. In fact, a good solution will provide some means for avoiding this for those cases where it is necessary to evacuate memory when offlining the device. 2. Memory can be either explicitly or implicitly allocated from the CCAD device's memory. (Both usermode and kernel allocation required.) Please note that implicit allocation will need to be avoided in a number of use cases. The reason for this is that random kernel allocations might be pinned into memory, which could conflict with requirement (1) above, and might furthermore fragment the device's memory. 3. The device's memory is treated like normal system memory by the Linux kernel, for example, each page has a "struct page" associate with it. (In contrast, the traditional approach has used special-purpose OS mechanisms to manage the device's memory, and this memory was treated as MMIO space by the kernel.) 4. The system's normal tuning mechanism may be used to tune allocation locality, migration, and so on, as required to match performance and functional requirements. POTENTIAL IDEAS It is only reasonable to ask whether CCAD devices can simply use the HMM patch that has recently been proposed to allow migration between system and device memory via page faults. Although this works well for devices whose local MMU can contain mappings different from that of the system MMU, the HMM patch is still working with MMIO space that gets special treatment. The HMM patch does not (yet) provide the full transparency that would allow the device memory to be treated in the same way as system memory. Something more is therefore required, for example, one or more of the following: 1. Model the CCAD device's memory as a memory-only NUMA node with a very large distance metric. This allows use of the existing mechanisms for choosing where to satisfy explicit allocations and where to target migrations. 2. Cover the memory with a CMA to prevent non-migratable pinned data from being placed in the CCAD device's memory. It would also permit the driver to perform dedicated physically contiguous allocations as needed. 3. Add a new ZONE_EXTERNAL zone for all CCAD-like devices. Note that this would likely require support for discontinuous zones in order to support large NUMA systems, in which each node has a single block of the overall physical address space. In such systems, the physical address ranges of normal system memory would be interleaved with those of device memory. This would also require some sort of migration infrastructure to be added, as autonuma would not apply. However, this approach has the advantage of preventing allocations in these regions, at least unless those allocations have been explicitly flagged to go there. 4. Your idea here! The following sections cover AutoNUMA, use of memory zones, and DAX. AUTONUMA The Linux kernel's autonuma facility supports migrating both memory and processes to promote NUMA memory locality. It was accepted into 3.13 and is available in RHEL 7.0 and SLES 12. It is enabled by the Kconfig variable CONFIG_NUMA_BALANCING. This approach uses a kernel thread "knuma_scand" that periodically marks pages inaccessible. The page-fault handler notes any mismatches between the NUMA node that the process is running on and the NUMA node on which the page resides. http://lwn.net/Articles/488709/ https://www.kernel.org/pub/linux/kernel/people/andrea/autonuma/autonuma_bench-20120530.pdf It will be necessary to set up the CCAD device's memory as a very distant NUMA node, and the architecture-specific __numa_distance() function can be used for this purpose. There is a RECLAIM_DISTANCE macro that can be set by the architecture to prevent reclaiming from nodes that are too far away. Some experimentation would be required to determine the combination of values for the various distance macros. This approach needs some way to pull in data from the hardware on access patterns. Aneesh Kk Veetil is prototyping an approach based on Power 8 hardware counters. This data will need to be plugged into the migration algorithm, which is currently based on collecting information from page faults. Finally, the contiguous memory allocator (CMA, see http://lwn.net/Articles/486301/) is needed in order to prevent the kernel from placing non-migratable allocations in the CCAD device's memory. This would need to be of type MIGRATE_CMA to ensure that all memory taken from that range be migratable. The result would be that the kernel would allocate only migratable pages within the CCAD device's memory, and even then only if memory was otherwise exhausted. Normal CONFIG_NUMA_BALANCING migration could be brought to bear, possibly enhanced with information from hardware counters. One remaining issue is that there is no way to absolutely prevent random kernel subsystems from allocating the CCAD device's memory, which could cause failures should the device need to reset itself, in which case the memory would be temporarily inaccessible -- which could be a fatal surprise to that kernel subsystem. Jerome Glisse suggests that usermode hints are quite important, and perhaps should replace any AutoNUMA measurements. MEMORY ZONE One way to avoid the problem of random kernel subsystems using the CAPI device's memory is to create a new memory zone for this purpose. This would add something like ZONE_DEVMEM to the current set that includes ZONE_DMA, ZONE_NORMAL, and ZONE_MOVABLE. Currently, there are a maximum of four zones, so this limit must either be increased or kernels built with ZONE_DEVMEM must avoid having more than one of ZONE_DMA, ZONE_DMA32, and ZONE_HIGHMEM. This approach requires that migration be implemented on the side, as the CONFIG_NUMA_BALANCING will not help here (unless I am missing something). One advantage of this situation is that hardware locality measurements could be incorporated from the beginning. Another advantage is that random kernel subsystems and user programs would not get CAPI device memory unless they explicitly requested it. Code would be needed at boot time to place the CAPI device memory into ZONE_DEVMEM, perhaps involving changes to mem_init() and paging_init(). In addition, an appropriate GFP_DEVMEM would be needed, along with code in various paths to handle it appropriately. Also, because large NUMA systems will sometimes interleave the addresses of blocks of physical memory and device memory, support for discontiguous interleaved zones will be required. DAX DAX is a mechanism for providing direct-memory access to high-speed non-volatile (AKA "persistent") memory. Good introductions to DAX may be found in the following LWN articles: https://lwn.net/Articles/591779/ https://lwn.net/Articles/610174/ DAX provides filesystem-level access to persistent memory. One important CCAD use case is allowing a legacy application to pass memory from malloc() to a CCAD device, and having the allocated memory migrate as needed. DAX does not seem to support this use case. ACKNOWLEDGMENTS Updates to this document include feedback from Christoph Lameter and Jerome Glisse. -- To unsubscribe, send a message with 'unsubscribe linux-mm' in the body to majordomo@xxxxxxxxx. For more info on Linux MM, see: http://www.linux-mm.org/ . Don't email: <a href=mailto:"dont@xxxxxxxxx";> email@xxxxxxxxx </a>