mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2024-11-24 21:59:18 +08:00
103 lines
3.5 KiB
C++
103 lines
3.5 KiB
C++
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
|
|
*
|
|
* Redistribution and use in source and binary forms, with or without
|
|
* modification, are permitted provided that the following conditions
|
|
* are met:
|
|
* * Redistributions of source code must retain the above copyright
|
|
* notice, this list of conditions and the following disclaimer.
|
|
* * Redistributions in binary form must reproduce the above copyright
|
|
* notice, this list of conditions and the following disclaimer in the
|
|
* documentation and/or other materials provided with the distribution.
|
|
* * Neither the name of NVIDIA CORPORATION nor the names of its
|
|
* contributors may be used to endorse or promote products derived
|
|
* from this software without specific prior written permission.
|
|
*
|
|
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
|
|
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
|
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
|
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
|
|
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
|
|
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
|
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
|
|
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
|
|
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
|
|
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
|
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
|
*/
|
|
|
|
/*
|
|
* Demonstration of inline PTX (assembly language) usage in CUDA kernels
|
|
*/
|
|
|
|
// System includes
|
|
#include <stdio.h>
|
|
#include <assert.h>
|
|
|
|
// CUDA runtime
|
|
#include <cuda_runtime.h>
|
|
#include <nvrtc_helper.h>
|
|
|
|
// helper functions and utilities to work with CUDA
|
|
#include <helper_functions.h>
|
|
|
|
void sequence_cpu(int *h_ptr, int length) {
|
|
for (int elemID = 0; elemID < length; elemID++) {
|
|
h_ptr[elemID] = elemID % 32;
|
|
}
|
|
}
|
|
|
|
int main(int argc, char **argv) {
|
|
printf("CUDA inline PTX assembler sample\n");
|
|
|
|
char *cubin, *kernel_file;
|
|
size_t cubinSize;
|
|
|
|
kernel_file = sdkFindFilePath("inlinePTX_kernel.cu", argv[0]);
|
|
compileFileToCUBIN(kernel_file, argc, argv, &cubin, &cubinSize, 0);
|
|
|
|
CUmodule module = loadCUBIN(cubin, argc, argv);
|
|
|
|
CUfunction kernel_addr;
|
|
|
|
checkCudaErrors(cuModuleGetFunction(&kernel_addr, module, "sequence_gpu"));
|
|
|
|
const int N = 1000;
|
|
int *h_ptr = (int *)malloc(N * sizeof(int));
|
|
|
|
dim3 cudaBlockSize(256, 1, 1);
|
|
dim3 cudaGridSize((N + cudaBlockSize.x - 1) / cudaBlockSize.x, 1, 1);
|
|
|
|
CUdeviceptr d_ptr;
|
|
checkCudaErrors(cuMemAlloc(&d_ptr, N * sizeof(int)));
|
|
|
|
void *arr[] = {(void *)&d_ptr, (void *)&N};
|
|
checkCudaErrors(cuLaunchKernel(kernel_addr, cudaGridSize.x, cudaGridSize.y,
|
|
cudaGridSize.z, /* grid dim */
|
|
cudaBlockSize.x, cudaBlockSize.y,
|
|
cudaBlockSize.z, /* block dim */
|
|
0, 0, /* shared mem, stream */
|
|
&arr[0], /* arguments */
|
|
0));
|
|
|
|
checkCudaErrors(cuCtxSynchronize());
|
|
|
|
sequence_cpu(h_ptr, N);
|
|
|
|
int *h_d_ptr = (int *)malloc(N * sizeof(int));
|
|
checkCudaErrors(cuMemcpyDtoH(h_d_ptr, d_ptr, N * sizeof(int)));
|
|
|
|
bool bValid = true;
|
|
|
|
for (int i = 0; i < N && bValid; i++) {
|
|
if (h_ptr[i] != h_d_ptr[i]) {
|
|
bValid = false;
|
|
}
|
|
}
|
|
|
|
printf("Test %s.\n", bValid ? "Successful" : "Failed");
|
|
|
|
checkCudaErrors(cuMemFree(d_ptr));
|
|
|
|
return bValid ? EXIT_SUCCESS : EXIT_FAILURE;
|
|
}
|