Самый легкий способ протестировать на существование cuda-способного GPU от cmake?

Вы можете скомпилировать небольшую программу запроса GPU, если cuda была найдена. вот простой вариант, который вы можете адаптировать к потребностям:

#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);
}
}

Напишите простую программу, например

#include<cuda.h>

int main (){
    int deviceCount;
    cudaError_t e = cudaGetDeviceCount(&deviceCount);
    return e == cudaSuccess ? deviceCount : -1;
}

, и проверьте возвращаемое значение.

Я только что написал скрипт на чистом Python, который делает некоторые из тех вещей, которые вам, похоже, нужны (я взял многое из этого из проекта pystream). По сути, это просто обертка для некоторых функций из библиотеки времени выполнения CUDA (она использует ctypes). Посмотрите на функцию main(), чтобы увидеть пример использования. Также имейте в виду, что я только что написал его, поэтому в нем могут быть ошибки. Используйте с осторожностью.

#!/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()