从 cmake 测试是否存在支持 cuda 的 GPU 的最简单方法？
https://stackoverflow.com/questions/2285185
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21-09-2019 - |
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我们有一些夜间构建机器，具有 CUDA库 已安装，但没有安装支持 cuda 的 GPU。这些机器能够构建支持 cuda 的程序，但无法运行这些程序。
在我们的自动化夜间构建过程中，我们的 cmake 脚本使用 cmake 命令
find_package(CUDA)
判断cuda软件是否安装。这设置了 cmake 变量 CUDA_FOUND 在安装了 cuda 软件的平台上。这太棒了，而且效果完美。什么时候 CUDA_FOUND 设置完毕后，就可以构建支持cuda的程序了。即使机器没有支持 cuda 的 GPU。
但是使用 cuda 的测试程序自然会在非 GPU cuda 机器上失败，导致我们的夜间仪表板看起来“脏”。所以我希望 cmake 避免在此类机器上运行这些测试。但我仍然想在那些机器上构建 cuda 软件。
得到阳性结果后 CUDA_FOUND 结果，我想测试是否存在实际的 GPU，然后设置一个变量，比如 CUDA_GPU_FOUND, ，来反映这一点。
让 cmake 测试是否存在支持 cuda 的 GPU 的最简单方法是什么？
这需要在三个平台上运行：Windows 与 MSVC、Mac 和 Linux。（这就是我们首先使用 cmake 的原因）
编辑： 关于如何编写程序来测试 GPU 是否存在，答案中有一些不错的建议。仍然缺少的是让 CMake 在配置时编译和运行该程序的方法。我怀疑 TRY_RUN CMake 中的命令在这里至关重要，但不幸的是该命令是 几乎无证, ，我不知道如何让它发挥作用。这个 CMake 问题的一部分可能是一个更困难的问题。也许我应该把这个作为两个单独的问题来问......
解决方案
这个问题的答案由两部分组成：
- 用于检测是否存在支持 cuda 的 GPU 的程序。
- CMake 代码在配置时编译、运行和解释该程序的结果。
对于第 1 部分，GPU 嗅探程序，我从 fabrizioM 提供的答案开始，因为它非常紧凑。我很快发现我需要在未知的答案中找到许多细节才能使其正常工作。我最终得到的是以下 C 源文件，我将其命名为 has_cuda_gpu.c:
#include <stdio.h>
#include <cuda_runtime.h>
int main() {
int deviceCount, device;
int gpuDeviceCount = 0;
struct cudaDeviceProp properties;
cudaError_t cudaResultCode = cudaGetDeviceCount(&deviceCount);
if (cudaResultCode != cudaSuccess)
deviceCount = 0;
/* machines with no GPUs can still report one emulation device */
for (device = 0; device < deviceCount; ++device) {
cudaGetDeviceProperties(&properties, device);
if (properties.major != 9999) /* 9999 means emulation only */
++gpuDeviceCount;
}
printf("%d GPU CUDA device(s) found\n", gpuDeviceCount);
/* don't just return the number of gpus, because other runtime cuda
errors can also yield non-zero return values */
if (gpuDeviceCount > 0)
return 0; /* success */
else
return 1; /* failure */
}
请注意，如果找到支持 cuda 的 GPU，则返回代码为零。这是因为在我的一台有 cuda 但没有 GPU 的机器上，该程序生成一个具有非零退出代码的运行时错误。因此任何非零退出代码都被解释为“cuda 在此机器上不起作用”。
你可能会问为什么我不在非 GPU 机器上使用 cuda 模拟模式。这是因为仿真模式有问题。我只想调试我的代码，并解决 cuda GPU 代码中的错误。我没有时间调试模拟器。
问题的第二部分是使用这个测试程序的cmake代码。经过一番挣扎，我想通了。以下块是更大块的一部分 CMakeLists.txt 文件：
find_package(CUDA)
if(CUDA_FOUND)
try_run(RUN_RESULT_VAR COMPILE_RESULT_VAR
${CMAKE_BINARY_DIR}
${CMAKE_CURRENT_SOURCE_DIR}/has_cuda_gpu.c
CMAKE_FLAGS
-DINCLUDE_DIRECTORIES:STRING=${CUDA_TOOLKIT_INCLUDE}
-DLINK_LIBRARIES:STRING=${CUDA_CUDART_LIBRARY}
COMPILE_OUTPUT_VARIABLE COMPILE_OUTPUT_VAR
RUN_OUTPUT_VARIABLE RUN_OUTPUT_VAR)
message("${RUN_OUTPUT_VAR}") # Display number of GPUs found
# COMPILE_RESULT_VAR is TRUE when compile succeeds
# RUN_RESULT_VAR is zero when a GPU is found
if(COMPILE_RESULT_VAR AND NOT RUN_RESULT_VAR)
set(CUDA_HAVE_GPU TRUE CACHE BOOL "Whether CUDA-capable GPU is present")
else()
set(CUDA_HAVE_GPU FALSE CACHE BOOL "Whether CUDA-capable GPU is present")
endif()
endif(CUDA_FOUND)
这设置了一个 CUDA_HAVE_GPU cmake 中的布尔变量，随后可用于触发条件操作。
我花了很长时间才弄清楚包含和链接参数需要放在 CMAKE_FLAGS 节中，以及语法应该是什么。这 try_run 文档 很轻，但是里面有更多信息 try_compile 文档, ，这是一个密切相关的命令。在让它发挥作用之前，我仍然需要在网上搜索 try_compile 和 try_run 的示例。
另一个棘手但重要的细节是第三个参数 try_run, ，“绑定目录”。您可能应该始终将其设置为 ${CMAKE_BINARY_DIR}. 。特别是，不要将其设置为 ${CMAKE_CURRENT_BINARY_DIR} 如果您位于项目的子目录中。CMake 期望找到子目录 CMakeFiles/CMakeTmp 在 bindir 内，如果该目录不存在，则会出现错误。只需使用 ${CMAKE_BINARY_DIR}, ，这是这些子目录似乎自然存在的位置之一。
其他提示
编写一个简单的程序等
#include<cuda.h>
int main (){
int deviceCount;
cudaError_t e = cudaGetDeviceCount(&deviceCount);
return e == cudaSuccess ? deviceCount : -1;
}
和检查返回值。
我只写了一个纯Python脚本，做一些你似乎需要的东西（我花了很多的这起pystream项目）。它基本上只是在CUDA运行时库（它使用ctypes的）一些功能的包装。看看在main（）函数来查看示例用法。此外，要知道，我只是写了，所以它可能包含bug。谨慎使用。
#!/bin/bash
import sys
import platform
import ctypes
"""
cudart.py: used to access pars of the CUDA runtime library.
Most of this code was lifted from the pystream project (it's BSD licensed):
http://code.google.com/p/pystream
Note that this is likely to only work with CUDA 2.3
To extend to other versions, you may need to edit the DeviceProp Class
"""
cudaSuccess = 0
errorDict = {
1: 'MissingConfigurationError',
2: 'MemoryAllocationError',
3: 'InitializationError',
4: 'LaunchFailureError',
5: 'PriorLaunchFailureError',
6: 'LaunchTimeoutError',
7: 'LaunchOutOfResourcesError',
8: 'InvalidDeviceFunctionError',
9: 'InvalidConfigurationError',
10: 'InvalidDeviceError',
11: 'InvalidValueError',
12: 'InvalidPitchValueError',
13: 'InvalidSymbolError',
14: 'MapBufferObjectFailedError',
15: 'UnmapBufferObjectFailedError',
16: 'InvalidHostPointerError',
17: 'InvalidDevicePointerError',
18: 'InvalidTextureError',
19: 'InvalidTextureBindingError',
20: 'InvalidChannelDescriptorError',
21: 'InvalidMemcpyDirectionError',
22: 'AddressOfConstantError',
23: 'TextureFetchFailedError',
24: 'TextureNotBoundError',
25: 'SynchronizationError',
26: 'InvalidFilterSettingError',
27: 'InvalidNormSettingError',
28: 'MixedDeviceExecutionError',
29: 'CudartUnloadingError',
30: 'UnknownError',
31: 'NotYetImplementedError',
32: 'MemoryValueTooLargeError',
33: 'InvalidResourceHandleError',
34: 'NotReadyError',
0x7f: 'StartupFailureError',
10000: 'ApiFailureBaseError'}
try:
if platform.system() == "Microsoft":
_libcudart = ctypes.windll.LoadLibrary('cudart.dll')
elif platform.system()=="Darwin":
_libcudart = ctypes.cdll.LoadLibrary('libcudart.dylib')
else:
_libcudart = ctypes.cdll.LoadLibrary('libcudart.so')
_libcudart_error = None
except OSError, e:
_libcudart_error = e
_libcudart = None
def _checkCudaStatus(status):
if status != cudaSuccess:
eClassString = errorDict[status]
# Get the class by name from the top level of this module
eClass = globals()[eClassString]
raise eClass()
def _checkDeviceNumber(device):
assert isinstance(device, int), "device number must be an int"
assert device >= 0, "device number must be greater than 0"
assert device < 2**8-1, "device number must be < 255"
# cudaDeviceProp
class DeviceProp(ctypes.Structure):
_fields_ = [
("name", 256*ctypes.c_char), # < ASCII string identifying device
("totalGlobalMem", ctypes.c_size_t), # < Global memory available on device in bytes
("sharedMemPerBlock", ctypes.c_size_t), # < Shared memory available per block in bytes
("regsPerBlock", ctypes.c_int), # < 32-bit registers available per block
("warpSize", ctypes.c_int), # < Warp size in threads
("memPitch", ctypes.c_size_t), # < Maximum pitch in bytes allowed by memory copies
("maxThreadsPerBlock", ctypes.c_int), # < Maximum number of threads per block
("maxThreadsDim", 3*ctypes.c_int), # < Maximum size of each dimension of a block
("maxGridSize", 3*ctypes.c_int), # < Maximum size of each dimension of a grid
("clockRate", ctypes.c_int), # < Clock frequency in kilohertz
("totalConstMem", ctypes.c_size_t), # < Constant memory available on device in bytes
("major", ctypes.c_int), # < Major compute capability
("minor", ctypes.c_int), # < Minor compute capability
("textureAlignment", ctypes.c_size_t), # < Alignment requirement for textures
("deviceOverlap", ctypes.c_int), # < Device can concurrently copy memory and execute a kernel
("multiProcessorCount", ctypes.c_int), # < Number of multiprocessors on device
("kernelExecTimeoutEnabled", ctypes.c_int), # < Specified whether there is a run time limit on kernels
("integrated", ctypes.c_int), # < Device is integrated as opposed to discrete
("canMapHostMemory", ctypes.c_int), # < Device can map host memory with cudaHostAlloc/cudaHostGetDevicePointer
("computeMode", ctypes.c_int), # < Compute mode (See ::cudaComputeMode)
("__cudaReserved", 36*ctypes.c_int),
]
def __str__(self):
return """NVidia GPU Specifications:
Name: %s
Total global mem: %i
Shared mem per block: %i
Registers per block: %i
Warp size: %i
Mem pitch: %i
Max threads per block: %i
Max treads dim: (%i, %i, %i)
Max grid size: (%i, %i, %i)
Total const mem: %i
Compute capability: %i.%i
Clock Rate (GHz): %f
Texture alignment: %i
""" % (self.name, self.totalGlobalMem, self.sharedMemPerBlock,
self.regsPerBlock, self.warpSize, self.memPitch,
self.maxThreadsPerBlock,
self.maxThreadsDim[0], self.maxThreadsDim[1], self.maxThreadsDim[2],
self.maxGridSize[0], self.maxGridSize[1], self.maxGridSize[2],
self.totalConstMem, self.major, self.minor,
float(self.clockRate)/1.0e6, self.textureAlignment)
def cudaGetDeviceCount():
if _libcudart is None: return 0
deviceCount = ctypes.c_int()
status = _libcudart.cudaGetDeviceCount(ctypes.byref(deviceCount))
_checkCudaStatus(status)
return deviceCount.value
def getDeviceProperties(device):
if _libcudart is None: return None
_checkDeviceNumber(device)
props = DeviceProp()
status = _libcudart.cudaGetDeviceProperties(ctypes.byref(props), device)
_checkCudaStatus(status)
return props
def getDriverVersion():
if _libcudart is None: return None
version = ctypes.c_int()
_libcudart.cudaDriverGetVersion(ctypes.byref(version))
v = "%d.%d" % (version.value//1000,
version.value%100)
return v
def getRuntimeVersion():
if _libcudart is None: return None
version = ctypes.c_int()
_libcudart.cudaRuntimeGetVersion(ctypes.byref(version))
v = "%d.%d" % (version.value//1000,
version.value%100)
return v
def getGpuCount():
count=0
for ii in range(cudaGetDeviceCount()):
props = getDeviceProperties(ii)
if props.major!=9999: count+=1
return count
def getLoadError():
return _libcudart_error
version = getDriverVersion()
if version is not None and not version.startswith('2.3'):
sys.stdout.write("WARNING: Driver version %s may not work with %s\n" %
(version, sys.argv[0]))
version = getRuntimeVersion()
if version is not None and not version.startswith('2.3'):
sys.stdout.write("WARNING: Runtime version %s may not work with %s\n" %
(version, sys.argv[0]))
def main():
sys.stdout.write("Driver version: %s\n" % getDriverVersion())
sys.stdout.write("Runtime version: %s\n" % getRuntimeVersion())
nn = cudaGetDeviceCount()
sys.stdout.write("Device count: %s\n" % nn)
for ii in range(nn):
props = getDeviceProperties(ii)
sys.stdout.write("\nDevice %d:\n" % ii)
#sys.stdout.write("%s" % props)
for f_name, f_type in props._fields_:
attr = props.__getattribute__(f_name)
sys.stdout.write( " %s: %s\n" % (f_name, attr))
gpuCount = getGpuCount()
if gpuCount > 0:
sys.stdout.write("\n")
sys.stdout.write("GPU count: %d\n" % getGpuCount())
e = getLoadError()
if e is not None:
sys.stdout.write("There was an error loading a library:\n%s\n\n" % e)
if __name__=="__main__":
main()
如果CUDA发现可以编译小GPU查询程序。这里是一个简单的，你可以采取的需求：
#include <stdlib.h>
#include <stdio.h>
#include <cuda.h>
#include <cuda_runtime.h>
int main(int argc, char** argv) {
int ct,dev;
cudaError_t code;
struct cudaDeviceProp prop;
cudaGetDeviceCount(&ct);
code = cudaGetLastError();
if(code) printf("%s\n", cudaGetErrorString(code));
if(ct == 0) {
printf("Cuda device not found.\n");
exit(0);
}
printf("Found %i Cuda device(s).\n",ct);
for (dev = 0; dev < ct; ++dev) {
printf("Cuda device %i\n", dev);
cudaGetDeviceProperties(&prop,dev);
printf("\tname : %s\n", prop.name);
printf("\ttotalGlobablMem: %lu\n", (unsigned long)prop.totalGlobalMem);
printf("\tsharedMemPerBlock: %i\n", prop.sharedMemPerBlock);
printf("\tregsPerBlock: %i\n", prop.regsPerBlock);
printf("\twarpSize: %i\n", prop.warpSize);
printf("\tmemPitch: %i\n", prop.memPitch);
printf("\tmaxThreadsPerBlock: %i\n", prop.maxThreadsPerBlock);
printf("\tmaxThreadsDim: %i, %i, %i\n", prop.maxThreadsDim[0], prop.maxThreadsDim[1], prop.maxThreadsDim[2]);
printf("\tmaxGridSize: %i, %i, %i\n", prop.maxGridSize[0], prop.maxGridSize[1], prop.maxGridSize[2]);
printf("\tclockRate: %i\n", prop.clockRate);
printf("\ttotalConstMem: %i\n", prop.totalConstMem);
printf("\tmajor: %i\n", prop.major);
printf("\tminor: %i\n", prop.minor);
printf("\ttextureAlignment: %i\n", prop.textureAlignment);
printf("\tdeviceOverlap: %i\n", prop.deviceOverlap);
printf("\tmultiProcessorCount: %i\n", prop.multiProcessorCount);
}
}
一个有用的方法是运行CUDA已经安装的程序，如NVIDIA-SMI，看看他们返回的内容。
find_program(_nvidia_smi "nvidia-smi") if (_nvidia_smi) set(DETECT_GPU_COUNT_NVIDIA_SMI 0) # execute nvidia-smi -L to get a short list of GPUs available exec_program(${_nvidia_smi_path} ARGS -L OUTPUT_VARIABLE _nvidia_smi_out RETURN_VALUE _nvidia_smi_ret) # process the stdout of nvidia-smi if (_nvidia_smi_ret EQUAL 0) # convert string with newlines to list of strings string(REGEX REPLACE "\n" ";" _nvidia_smi_out "${_nvidia_smi_out}") foreach(_line ${_nvidia_smi_out}) if (_line MATCHES "^GPU [0-9]+:") math(EXPR DETECT_GPU_COUNT_NVIDIA_SMI "${DETECT_GPU_COUNT_NVIDIA_SMI}+1") # the UUID is not very useful for the user, remove it string(REGEX REPLACE " \\(UUID:.*\\)" "" _gpu_info "${_line}") if (NOT _gpu_info STREQUAL "") list(APPEND DETECT_GPU_INFO "${_gpu_info}") endif() endif() endforeach() check_num_gpu_info(${DETECT_GPU_COUNT_NVIDIA_SMI} DETECT_GPU_INFO) set(DETECT_GPU_COUNT ${DETECT_GPU_COUNT_NVIDIA_SMI}) endif() endif()
人们还可能查询的linux / PROC或lspci的。看到 https://github.com/gromacs/完全工作CMake的例子GROMACS /斑点/主/ cmake的/ gmxDetectGpu.cmake
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