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gdrcopy

A low-latency GPU memory copy library based on NVIDIA GPUDirect RDMA technology.

Introduction

While GPUDirect RDMA is meant for direct access to GPU memory from third-party devices, it is possible to use these same APIs to create perfectly valid CPU mappings of the GPU memory.

The advantage of a CPU driven copy is the essencially zero latency involved in the copy process. This might be useful when low latencies are required.

Disclaimer

This is just for technology demonstration purposes. In particular this is not an NVIDIA-supported product.

For bugs or feature requests, feel free to [file an issue] (https://github.com/NVIDIA/gdrcopy/issues). No guarantee is given that the team will follow up on any of those. Alternatively, see Bug filing section below for the official bug filing procedure.

The library relies on a small kernel-mode driver (gdrdrv) which has bug(s) and can even crash your machine. In particular, there is a latent bug related to the concurrent invalidation of mappings and memory deallocation.

What is inside

Basically, gdrcopy offers the infrastructure to create user-space mappings of GPU memory, which can then be manipulated as if it was plain host memory (caveats apply here).

A simple by-product of it is a copy library with the following characteristics:

  • very low overhead, as it is driven by the CPU. As a reference, currently a cudaMemcpy can incur in a 6-7us overhead.

  • An initial memory pinning phase is required, which is potentially expensive, 10us-1ms depending on the buffer size.

  • Fast H-D, because of write-combining. H-D bandwidth is 6-8GB/s on Ivy Bridge Xeon but it is subject to NUMA effects.

  • Slow D-H, because the GPU BAR, which backs the mappings, can't be prefetched and so burst reads transactions are not generated through PCIE

The library comes with two tests:

  • validate, which is a simple application testing the APIs.
  • copybw, a minimal application which calculates the R/W bandwidth.

Requirements

GPUDirect RDMA requires an NVIDIA Tesla and Quadro class GPUs based on Kepler/Maxwell, see GPUDirect RDMA.

For more technical informations, please refer to the official GPUDirect RDMA design document.

The device driver requires CUDA >= 5.0. The library and tests require CUDA >= 6.0 and/or display driver >= 331.14.

Developed and tested on RH6.x. The only supported architecture is Linux x86_64 so far.

root priviledges are necessary to load/install the kernel-mode device driver.

Build & execution

Build:

$ cd gdrcopy
$ make PREFIX=<install path dir> CUDA=<cuda install path> all install

Install kernel-mode driver (root/sudo caps required):

$ ./insmod.sh

Prepare environment:

$ export LD_LIBRARY_PATH=$PWD:$LD_LIBRARY_PATH

Execute provided tests:

$ ./validate
buffer size: 327680
check 1: direct access + read back via cuMemcpy D->H
check 2: gdr_copy_to_bar() + read back via cuMemcpy D->H
check 3: gdr_copy_to_bar() + read back via gdr_copy_from_bar()
check 4: gdr_copy_to_bar() + read back via gdr_copy_from_bar() + extra_dwords=5

$ ./copybw
testing size: 4096
rounded size: 65536
device ptr: 5046c0000
bar_ptr: 0x7f8cff410000
info.va: 5046c0000
info.mapped_size: 65536
info.page_size: 65536
page offset: 0
user-space pointer:0x7f8cff410000
BAR writing test...
BAR1 write BW: 9549.25MB/s
BAR reading test...
BAR1 read BW: 1.50172MB/s
unmapping buffer
unpinning buffer
closing gdrdrv

NUMA effects

Depending on the platform architecture, like where the GPU are placed in the PCIe topology, performance may suffer if the processor which is driving the copy is not the one which is hosting the GPU, for example in a multi-socket server.

In the example below, the K40m and K80 GPU are respectively hosted by socket0 and socket1. By explicitly playing with the OS process and memory affinity, it is possible to run the test onto the optimal processor:

$ GDRCOPY_ENABLE_LOGGING=1 GDRCOPY_LOG_LEVEL=0 LD_LIBRARY_PATH=$PWD:$LD_LIBRARY_PATH numactl -N 0 -l copybw -d 0 -s $((64 * 1024)) -o $((0 * 1024)) -c $((64 * 1024))
GPU id:0 name:Tesla K40m PCI domain: 0 bus: 2 device: 0
GPU id:1 name:Tesla K80 PCI domain: 0 bus: 132 device: 0
GPU id:2 name:Tesla K80 PCI domain: 0 bus: 133 device: 0
selecting device 0
testing size: 65536
rounded size: 65536
device ptr: 2305ba0000
bar_ptr: 0x7fe60956c000
info.va: 2305ba0000
info.mapped_size: 65536
info.page_size: 65536
page offset: 0
user-space pointer:0x7fe60956c000
BAR writing test, size=65536 offset=0 num_iters=10000
DBG:  sse4_1=1 avx=1 sse=1 sse2=1
DBG:  using AVX implementation of gdr_copy_to_bar
BAR1 write BW: 9793.23MB/s
BAR reading test, size=65536 offset=0 num_iters=100
DBG:  using SSE4_1 implementation of gdr_copy_from_bar
BAR1 read BW: 787.957MB/s
unmapping buffer
unpinning buffer
closing gdrdrv

or on the other one:

drossetti@drossetti-hsw0 16:52 (1181) gdrcopy>GDRCOPY_ENABLE_LOGGING=1 GDRCOPY_LOG_LEVEL=0 LD_LIBRARY_PATH=$PWD:$LD_LIBRARY_PATH numactl -N 1 -l copybw -d 0 -s $((64 * 1024)) -o $((0 * 1024)) -c $((64 * 1024))
GPU id:0 name:Tesla K40m PCI domain: 0 bus: 2 device: 0
GPU id:1 name:Tesla K80 PCI domain: 0 bus: 132 device: 0
GPU id:2 name:Tesla K80 PCI domain: 0 bus: 133 device: 0
selecting device 0
testing size: 65536
rounded size: 65536
device ptr: 2305ba0000
bar_ptr: 0x7f2299166000
info.va: 2305ba0000
info.mapped_size: 65536
info.page_size: 65536
page offset: 0
user-space pointer:0x7f2299166000
BAR writing test, size=65536 offset=0 num_iters=10000
DBG:  sse4_1=1 avx=1 sse=1 sse2=1
DBG:  using AVX implementation of gdr_copy_to_bar
BAR1 write BW: 6812.08MB/s
BAR reading test, size=65536 offset=0 num_iters=100
DBG:  using SSE4_1 implementation of gdr_copy_from_bar
BAR1 read BW: 669.825MB/s
unmapping buffer
unpinning buffer
closing gdrdrv

Bug filing

For reporting issues you may be having using any of NVIDIA software or reporting suspected bugs we would recommend you use the bug filing system which is available to NVIDIA registered developers on the developer site.

If you are not a member you can sign up.

Once a member you can submit issues using this form. Be sure to select GPUDirect in the "Relevant Area" field.

You can later track their progress using the My Bugs link on the left of this view.

TODO

  • add RPM specs for both library and kernel-mode driver
  • explore use of DKMS for kernel-mode driver
  • Conditionally use P2P tokens, to be compatible with CUDA 5.x.
  • Implement an event-queue mechanism in gdrdrv to deliver mapping invalidation events to user-space applications.