Question

我使用CMake作为我的代码的构建系统，涉及CUDA。我正在考虑自动执行决定哪个compute_XX和arch_XX我需要传递给我的nvcc以便为我当前机器上的GPU编译的任务。

有没有办法做到这一点：
1. 使用NVIDIA GPU部署套件？
2. 没有NVIDIA GPU部署套件？
CMake FindCUDA是否帮助您确定这些交换机的值？

Answer 1

我的策略是编译并运行一个bash脚本来探测卡并返回cmake的gencode。灵感来自University of Chicago's SLURM。要处理错误或多个gpus或其他情况，请根据需要进行修改。

在项目文件夹中创建一个文件cudaComputeVersion.bash并确保它可以从shell执行。进入这个文件：

#!/bin/bash

# create a 'here document' that is code we compile and use to probe the card
cat << EOF > /tmp/cudaComputeVersion.cu
#include <stdio.h>
int main()
{
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop,0);
int v = prop.major * 10 + prop.minor;
printf("-gencode arch=compute_%d,code=sm_%d\n",v,v);
}
EOF

# probe the card and cleanup
/usr/local/cuda/bin/nvcc /tmp/cudaComputeVersion.cu -o /tmp/cudaComputeVersion
/tmp/cudaComputeVersion
rm /tmp/cudaComputeVersion.cu
rm /tmp/cudaComputeVersion

在你的CMakeLists.txt中：

# at cmake-build-time, probe the card and set a cmake variable
execute_process(COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/cudaComputeVersion.bash OUTPUT_VARIABLE GENCODE)
# at project-compile-time, include the gencode into the compile options
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS}; "${GENCODE}")

# this makes CMake all chatty and allows you to see that GENCODE was set correctly
set(CMAKE_VERBOSE_MAKEFILE TRUE)

欢呼声

Answer 2

比@ orthopteroid的答案略有改进，这几乎可以确保生成一个独特的临时文件，并且只需要一个而不是两个临时文件。

以下内容进入scripts/get_cuda_sm.sh：

#!/bin/bash 
#
# Prints the compute capability of the first CUDA device installed
# on the system, or alternatively the device whose index is the
# first command-line argument

device_index=${1:-0}
timestamp=$(date +%s.%N)
gcc_binary=$(which g++)
gcc_binary=${gcc_binary:-g++}
cuda_root=${CUDA_DIR:-/usr/local/cuda}
CUDA_INCLUDE_DIRS=${CUDA_INCLUDE_DIRS:-${cuda_root}/include}
CUDA_CUDART_LIBRARY=${CUDA_CUDART_LIBRARY:-${cuda_root}/lib64/libcudart.so}
generated_binary="/tmp/cuda-compute-version-helper-$$-$timestamp"
# create a 'here document' that is code we compile and use to probe the card
source_code="$(cat << EOF 
#include <stdio.h>
#include <cuda_runtime_api.h>

int main()
{
        cudaDeviceProp prop;
        cudaError_t status;
        int device_count;
        status = cudaGetDeviceCount(&device_count);
        if (status != cudaSuccess) { 
                fprintf(stderr,"cudaGetDeviceCount() failed: %s\n", cudaGetErrorString(status)); 
                return -1;
        }
        if (${device_index} >= device_count) {
                fprintf(stderr, "Specified device index %d exceeds the maximum (the device count on this system is %d)\n", ${device_index}, device_count);
                return -1;
        }
        status = cudaGetDeviceProperties(&prop, ${device_index});
        if (status != cudaSuccess) { 
                fprintf(stderr,"cudaGetDeviceProperties() for device ${device_index} failed: %s\n", cudaGetErrorString(status)); 
                return -1;
        }
        int v = prop.major * 10 + prop.minor;
        printf("%d\\n", v);
}
EOF
)"
echo "$source_code" | $gcc_binary -x c++ -I"$CUDA_INCLUDE_DIRS" -o "$generated_binary" - -x none "$CUDA_CUDART_LIBRARY"

# probe the card and cleanup

$generated_binary
rm $generated_binary

以下内容进入CMakeLists.txt或CMake模块：

if (NOT CUDA_TARGET_COMPUTE_CAPABILITY)
    if("$ENV{CUDA_SM}" STREQUAL "")
        set(ENV{CUDA_INCLUDE_DIRS} "${CUDA_INCLUDE_DIRS}")
        set(ENV{CUDA_CUDART_LIBRARY} "${CUDA_CUDART_LIBRARY}")
        set(ENV{CMAKE_CXX_COMPILER} "${CMAKE_CXX_COMPILER}")
        execute_process(COMMAND 
            bash -c "${CMAKE_CURRENT_SOURCE_DIR}/scripts/get_cuda_sm.sh" 
            OUTPUT_VARIABLE CUDA_TARGET_COMPUTE_CAPABILITY_)
    else()
        set(CUDA_TARGET_COMPUTE_CAPABILITY_ $ENV{CUDA_SM})
    endif()

    set(CUDA_TARGET_COMPUTE_CAPABILITY "${CUDA_TARGET_COMPUTE_CAPABILITY_}" 
        CACHE STRING "CUDA compute capability of the (first) CUDA device on \
        the system, in XY format (like the X.Y format but no dot); see table \
        of features and capabilities by capability X.Y value at \
        https://en.wikipedia.org/wiki/CUDA#Version_features_and_specifications")

    execute_process(COMMAND 
        bash -c "echo -n $(echo ${CUDA_TARGET_COMPUTE_CAPABILITY})" 
        OUTPUT_VARIABLE CUDA_TARGET_COMPUTE_CAPABILITY)
    execute_process(COMMAND 
        bash -c "echo ${CUDA_TARGET_COMPUTE_CAPABILITY} | sed 's/^\\([0-9]\\)\\([0-9]\\)/\\1.\\2/;' | xargs echo -n" 
        OUTPUT_VARIABLE FORMATTED_COMPUTE_CAPABILITY)

    message(STATUS 
        "CUDA device-side code will assume compute capability \
        ${FORMATTED_COMPUTE_CAPABILITY}")
endif()

set(CUDA_GENCODE
    "arch=compute_${CUDA_TARGET_COMPUTE_CAPABILITY}, code=compute_${CUDA_TARGET_COMPUTE_CAPABILITY}")
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} -gencode ${CUDA_GENCODE} )

Answer 3

使用CMake 3.7或更高版本时，可以在cuda_select_nvcc_arch_flags()模块中使用FindCUDA宏，而无需任何其他脚本。

include(FindCUDA)
set(CUDA_ARCH_LIST Auto CACHE LIST
    "List of CUDA architectures (e.g. Pascal, Volta, etc) or \
compute capability versions (6.1, 7.0, etc) to generate code for. \
Set to Auto for automatic detection (default)."
)
cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS ${CUDA_ARCH_LIST})
list(APPEND CUDA_NVCC_FLAGS ${CUDA_ARCH_FLAGS})

例如，以上方法在我的机器上将CUDA_ARCH_FLAGS设置为-gencode arch=compute_61,code=sm_61。用户可以配置CUDA_ARCH_LIST缓存变量，以生成用于特定计算功能的代码，而不是自动检测。

注意：从CMake 3.10开始不推荐使用FindCUDA模块。但是，在最新的CMake版本（v3.14）中似乎没有提供与cuda_select_nvcc_arch_flags()宏等效的替代方法。有关更多详细信息，请参见CMake问题跟踪器上的relevant issue。

在CMake中确定nvcc需要哪个gencode（compute_，arch_）值

3 个答案: