Question

在计算之后使用cudaDeviceReset（）从Matlab使用GPU的正常方法是什么？我无法在最新版本的Matlab中使用GPU计算，因为我的GPU不支持Compute Capability 1.3+，而且我不想为使用简单的Cuda函数（如cudaMemGetInfo（））而向Accelereyes Jacket付出大量金钱或者我简单的Cuda内核。

我从Matlab调用Cuda时发现了一些非常令人沮丧的行为。在Visual Studio 2008中，我编写了一个简单的DLL，它使用标准的MEX接口来运行一个Cuda查询：设备上有多少RAM可用（清单1）。

// cudaMemoryCheck.cpp : Defines the exported functions for the DLL application.

#include <mex.h>
#include <cuda.h>
#include <driver_types.h>
#include <cuda_runtime_api.h>

void mexFunction( int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[] )
{
    size_t free = 0, total = 0;
    cudaError_t result = cudaMemGetInfo(&free, &total);

    mexPrintf("free memory in bytes %u (%u MB), total memory in bytes %u (%u MB). ", free, free/1024/1024, total, total/1024/1024);

    if( total > 0 )
        mexPrintf("%2.2f%% free\n", (100.0*free)/total );
    else
        mexPrintf("\n");

    // this is the critical line!
    cudaDeviceReset();
}

我将项目编译为Win32 DLL（发布模式），我使用DEF文件导出mexFunction，并将DLL文件扩展名重命名为.mexw32。

当我从Matlab运行cudaMemoryCheck时，如果cudaDeviceReset（）被注释掉，我发现我的GPU会泄漏内存。这是我的简单Matlab代码（清单2）：

addpath('C:\Users\admin\Documents\Visual Studio 2008\Projects\cudaMemoryCheck\Release')

for i=1:20
    clear mex
    cudaMemoryCheck;
end

在Matlab中运行此函数，我看到：

free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free
free memory in bytes 57393152 (54 MB), total memory in bytes 244776960 (233 MB). 23.45% free

当cudaDeviceReset（）被注释掉时，Matlab的输出非常不同：

free memory in bytes 37019648 (35 MB), total memory in bytes 244776960 (233 MB). 15.12% free
free memory in bytes 25092096 (23 MB), total memory in bytes 244776960 (233 MB). 10.25% free
free memory in bytes 13549568 (12 MB), total memory in bytes 244776960 (233 MB). 5.54% free
free memory in bytes 12107776 (11 MB), total memory in bytes 244776960 (233 MB). 4.95% free
free memory in bytes 8568832 (8 MB), total memory in bytes 244776960 (233 MB). 3.50% free
free memory in bytes 9617408 (9 MB), total memory in bytes 244776960 (233 MB). 3.93% free
free memory in bytes 6078464 (5 MB), total memory in bytes 244776960 (233 MB). 2.48% free
free memory in bytes 8044544 (7 MB), total memory in bytes 244776960 (233 MB). 3.29% free
free memory in bytes 5816320 (5 MB), total memory in bytes 244776960 (233 MB). 2.38% free
free memory in bytes 7520256 (7 MB), total memory in bytes 244776960 (233 MB). 3.07% free
free memory in bytes 8830976 (8 MB), total memory in bytes 244776960 (233 MB). 3.61% free
free memory in bytes 5292032 (5 MB), total memory in bytes 244776960 (233 MB). 2.16% free
free memory in bytes 3407872 (3 MB), total memory in bytes 244776960 (233 MB). 1.39% free
free memory in bytes 0 (0 MB), total memory in bytes 0 (0 MB). 
free memory in bytes 0 (0 MB), total memory in bytes 0 (0 MB). 
free memory in bytes 0 (0 MB), total memory in bytes 0 (0 MB). 
free memory in bytes 0 (0 MB), total memory in bytes 0 (0 MB). 
free memory in bytes 0 (0 MB), total memory in bytes 0 (0 MB). 
free memory in bytes 0 (0 MB), total memory in bytes 0 (0 MB). 
free memory in bytes 0 (0 MB), total memory in bytes 0 (0 MB).

所以我得出结论，即使我的MEX函数在GPU上没有分配内存，Cuda Runtime API每次运行MEX函数时都会创建新的CUcontexts，并且在我关闭Matlab或使用cudaDeviceReset之前它永远不会清除它们。）。最终GPU耗尽了内存，尽管我没有在其上分配任何内容！

我不喜欢使用cudaDeviceReset（）。 API说，“函数cudaDeviceReset（）将立即取消初始化调用线程的当前设备的主要上下文”和“当调用此函数时，调用者有责任确保该进程中的任何其他主机线程都不会访问该设备叫做。”换句话说，使用cudaDeviceReset（）可以立即终止其他GPU计算而不会发出警告。我没有找到任何使用cudaDeviceReset（）的文档经常是正常的，所以我不想这样做。我将接受这里的任何答案，证明使用cudaDeviceReset（）是正常的并且是必需的。

版本信息：NVIDIA GPU计算工具包4.0，Matlab 7.8.0（R2009a，32位），Windows 7企业版SP1（64位），Nvidia Quadro NVS 420（最新的Nvidia驱动程序，270.81）。

我还可以在Windows XP（32位，SP3）上使用GeForce 8400 GS，Matlab，Visual Studio和GPU计算工具包重现此问题。

deviceQuery.exe的输出：

deviceQuery.exe Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Found 2 CUDA Capable device(s)

Device 0: "Quadro NVS 420"
  CUDA Driver Version / Runtime Version          4.0 / 4.0
  CUDA Capability Major/Minor version number:    1.1
  Total amount of global memory:                 233 MBytes (244776960 bytes)
  ( 1) Multiprocessors x ( 8) CUDA Cores/MP:     8 CUDA Cores
  GPU Clock Speed:                               1.40 GHz
  Memory Clock rate:                             700.00 Mhz
  Memory Bus Width:                              64-bit
  Max Texture Dimension Size (x,y,z)             1D=(8192), 2D=(65536,32768), 3D=(2048,2048,2048)
  Max Layered Texture Size (dim) x layers        1D=(8192) x 512, 2D=(8192,8192) x 512
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       16384 bytes
  Total number of registers available per block: 8192
  Warp size:                                     32
  Maximum number of threads per block:           512
  Maximum sizes of each dimension of a block:    512 x 512 x 64
  Maximum sizes of each dimension of a grid:     65535 x 65535 x 1
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             256 bytes
  Concurrent copy and execution:                 No with 0 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Concurrent kernel execution:                   No
  Alignment requirement for Surfaces:            Yes
  Device has ECC support enabled:                No
  Device is using TCC driver mode:               No
  Device supports Unified Addressing (UVA):      No
  Device PCI Bus ID / PCI location ID:           3 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

Device 1: "Quadro NVS 420"
  CUDA Driver Version / Runtime Version          4.0 / 4.0
  CUDA Capability Major/Minor version number:    1.1
  Total amount of global memory:                 234 MBytes (244908032 bytes)
  ( 1) Multiprocessors x ( 8) CUDA Cores/MP:     8 CUDA Cores
  GPU Clock Speed:                               1.40 GHz
  Memory Clock rate:                             700.00 Mhz
  Memory Bus Width:                              64-bit
  Max Texture Dimension Size (x,y,z)             1D=(8192), 2D=(65536,32768), 3D=(2048,2048,2048)
  Max Layered Texture Size (dim) x layers        1D=(8192) x 512, 2D=(8192,8192) x 512
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       16384 bytes
  Total number of registers available per block: 8192
  Warp size:                                     32
  Maximum number of threads per block:           512
  Maximum sizes of each dimension of a block:    512 x 512 x 64
  Maximum sizes of each dimension of a grid:     65535 x 65535 x 1
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             256 bytes
  Concurrent copy and execution:                 No with 0 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Concurrent kernel execution:                   No
  Alignment requirement for Surfaces:            Yes
  Device has ECC support enabled:                No
  Device is using TCC driver mode:               No
  Device supports Unified Addressing (UVA):      No
  Device PCI Bus ID / PCI location ID:           4 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 4.0, CUDA Runtime Version = 4.0, NumDevs = 2, Device = Quadro NVS 420, Device = Quadro NVS 420

Answer 1

我认为您不应该使用cudaDeviceReset，如果省略对clear mex的调用会怎样？你为什么一开始就这样做？这将导致MATLAB卸载您的MEX文件，我怀疑这是内存泄漏的根源。

由于CUcontext缓存，Matlab会导致Cuda泄漏内存吗？

1 个答案: