遵循CUDA 示例中的相同步骤来启动内核并跨网格同步cooperative_groups::this_grid().sync()会导致任何 CUDA API 调用失败。虽然使用
cooperative_groups::this_thread_block().sync()工作正常并给出正确的结果。
我使用以下代码和CMakeLists.txt（cmake 版本 3.11.1）在带有 Ubuntu 16.04.5 LTS 的 TITAN V GPU（驱动程序版本 410.73）上使用 CUDA 10 对其进行测试。该代码也可以在github上找到，以便于重现错误。
该代码读取一个数组，然后将其反转（从[0 1 2 ... 9]到[9 8 7 ... 0]）。为了做到这一点，每个线程从数组中读取单个元素，同步，然后将其元素写入正确的目的地。可以轻松修改代码以确保this_thread_block().sync()正常工作。只需更改arr_size为更少的 1024 并cg::thread_block barrier = cg::this_thread_block();改用。
test_cg.cu
#include <cuda_runtime_api.h>
#include <stdio.h>
#include <stdint.h>
#include <cstdint>
#include <numeric>
#include <cuda.h>
#include <cooperative_groups.h>
namespace cg = cooperative_groups;
//********************** CUDA_ERROR
inline void HandleError(cudaError_t err, const char *file, int line) {
//Error handling micro, wrap it around function whenever possible
if (err != cudaSuccess) {
printf("\n%s in %s at line %d\n", cudaGetErrorString(err), file, line);
#ifdef _WIN32
system("pause");
#else
exit(EXIT_FAILURE);
#endif
}
}
#define CUDA_ERROR( err ) (HandleError( err, __FILE__, __LINE__ ))
//******************************************************************************
//********************** cg kernel
__global__ void testing_cg_grid_sync(const uint32_t num_elements,
uint32_t *d_arr){
uint32_t tid = threadIdx.x + blockDim.x*blockIdx.x;
if (tid < num_elements){
uint32_t my_element = d_arr[tid];
//to sync across the whole grid
cg::grid_group barrier = cg::this_grid();
//to sync within a single block
//cg::thread_block barrier = cg::this_thread_block();
//wait for all reads
barrier.sync();
uint32_t tar_id = num_elements - tid - 1;
d_arr[tar_id] = my_element;
}
}
//******************************************************************************
//********************** execute
void execute_test(const int sm_count){
//host array
const uint32_t arr_size = 1 << 20; //1M
uint32_t* h_arr = (uint32_t*)malloc(arr_size * sizeof(uint32_t));
//fill with sequential numbers
std::iota(h_arr, h_arr + arr_size, 0);
//device array
uint32_t* d_arr;
CUDA_ERROR(cudaMalloc((void**)&d_arr, arr_size*sizeof(uint32_t)));
CUDA_ERROR(cudaMemcpy(d_arr, h_arr, arr_size*sizeof(uint32_t),
cudaMemcpyHostToDevice));
//launch config
const int threads = 512;
//following the same steps done in conjugateGradientMultiBlockCG.cu
//cuda sample to launch kernel that sync across grid
//https://github.com/NVIDIA/cuda-samples/blob/master/Samples/conjugateGradientMultiBlockCG/conjugateGradientMultiBlockCG.cu#L436
int num_blocks_per_sm = 0;
CUDA_ERROR(cudaOccupancyMaxActiveBlocksPerMultiprocessor(&num_blocks_per_sm,
(void*)testing_cg_grid_sync, threads, 0));
dim3 grid_dim(sm_count * num_blocks_per_sm, 1, 1), block_dim(threads, 1, 1);
if(arr_size > grid_dim.x*block_dim.x){
printf("\n The grid size (numBlocks*numThreads) is less than array size.\n");
exit(EXIT_FAILURE);
}
printf("\n Launching %d blocks, each containing %d threads", grid_dim.x,
block_dim.x);
//argument passed to the kernel
void *kernel_args[] = {
(void *)&arr_size,
(void *)&d_arr, };
//finally launch the kernel
cudaLaunchCooperativeKernel((void*)testing_cg_grid_sync,
grid_dim, block_dim, kernel_args);
//make sure everything went okay
CUDA_ERROR(cudaGetLastError());
CUDA_ERROR(cudaDeviceSynchronize());
//get results on the host
CUDA_ERROR(cudaMemcpy(h_arr, d_arr, arr_size*sizeof(uint32_t),
cudaMemcpyDeviceToHost));
//validate
for (uint32_t i = 0; i < arr_size; i++){
if (h_arr[i] != arr_size - i - 1){
printf("\n Result mismatch in h_arr[%u] = %u\n", i, h_arr[i]);
exit(EXIT_FAILURE);
}
}
}
//******************************************************************************
int main(int argc, char**argv) {
//set to Titan V
uint32_t device_id = 0;
cudaSetDevice(device_id);
//get sm count
cudaDeviceProp devProp;
CUDA_ERROR(cudaGetDeviceProperties(&devProp, device_id));
int sm_count = devProp.multiProcessorCount;
//execute
execute_test(sm_count);
printf("\n Mission accomplished \n");
return 0;
}
CMakeLists.txt
cmake_minimum_required(VERSION 3.8 FATAL_ERROR)
set(PROJECT_NAME "test_cg")
project(${PROJECT_NAME} LANGUAGES CXX CUDA)
#default build type is Release
if (CMAKE_BUILD_TYPE STREQUAL "")
set(CMAKE_BUILD_TYPE Release)
endif ()
SET(CUDA_SEPARABLE_COMPILATION ON)
########## Libraries/flags Starts Here ######################
find_package(CUDA REQUIRED)
include_directories("${CUDA_INCLUDE_DIRS}")
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS}; -lineinfo; -std=c++11; -expt-extended-lambda; -O3; -use_fast_math; -rdc=true;)
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode=arch=compute_70,code=sm_70) #for TITAN V
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -m64 -Wall -std=c++11")
########## Libraries/flags Ends Here ######################
########## inc/libs/exe/features Starts Here ######################
set(CMAKE_INCLUDE_CURRENT_DIR ON)
CUDA_ADD_EXECUTABLE(${PROJECT_NAME} test_cg.cu)
target_compile_features(${PROJECT_NAME} PUBLIC cxx_std_11)
set_target_properties(${PROJECT_NAME} PROPERTIES POSITION_INDEPENDENT_CODE ON)
set_target_properties(${PROJECT_NAME} PROPERTIES CUDA_SEPARABLE_COMPILATION ON)
target_link_libraries(${PROJECT_NAME} ${CUDA_LIBRARIES} ${CUDA_cudadevrt_LIBRARY})
########## inc/libs/exe/features Ends Here ######################
运行此代码给出：
unknown error in /home/ahdhn/test_cg/test_cg.cu at line 67
这是使用cudaMalloc. 我通过从设备查询确保代码编译为正确的架构，结果为 700。如果您发现我在代码或文件__CUDA_ARCH__中做错了什么，请告诉我。CMakeLists.txt