mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2024-12-01 10:09:18 +08:00
135 lines
4.6 KiB
C++
135 lines
4.6 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.
|
|
*/
|
|
|
|
/*
|
|
* This example shows how to use the clock function to measure the performance
|
|
* of block of threads of a kernel accurately. Blocks are executed in parallel
|
|
* and out of order. Since there's no synchronization mechanism between blocks,
|
|
* we measure the clock once for each block. The clock samples are written to
|
|
* device memory.
|
|
*/
|
|
|
|
// System includes
|
|
#include <stdio.h>
|
|
#include <stdint.h>
|
|
#include <assert.h>
|
|
|
|
#include <cuda_runtime.h>
|
|
#include <nvrtc_helper.h>
|
|
|
|
// helper functions and utilities to work with CUDA
|
|
#include <helper_functions.h>
|
|
|
|
#define NUM_BLOCKS 64
|
|
|
|
#define NUM_THREADS 256
|
|
|
|
// It's interesting to change the number of blocks and the number of threads to
|
|
// understand how to keep the hardware busy.
|
|
//
|
|
|
|
// Here are some numbers I get on my G80:
|
|
// blocks - clocks
|
|
// 1 - 3096
|
|
// 8 - 3232
|
|
// 16 - 3364
|
|
// 32 - 4615
|
|
// 64 - 9981
|
|
|
|
//
|
|
// With less than 16 blocks some of the multiprocessors of the device are idle.
|
|
// With
|
|
// more than 16 you are using all the multiprocessors, but there's only one
|
|
// block per
|
|
// multiprocessor and that doesn't allow you to hide the latency of the memory.
|
|
// With
|
|
// more than 32 the speed scales linearly.
|
|
|
|
// Start the main CUDA Sample here
|
|
|
|
int main(int argc, char **argv) {
|
|
printf("CUDA Clock sample\n");
|
|
|
|
typedef long clock_t;
|
|
|
|
clock_t timer[NUM_BLOCKS * 2];
|
|
|
|
float input[NUM_THREADS * 2];
|
|
|
|
for (int i = 0; i < NUM_THREADS * 2; i++) {
|
|
input[i] = (float)i;
|
|
}
|
|
|
|
char *cubin, *kernel_file;
|
|
size_t cubinSize;
|
|
|
|
kernel_file = sdkFindFilePath("clock_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, "timedReduction"));
|
|
|
|
dim3 cudaBlockSize(NUM_THREADS, 1, 1);
|
|
dim3 cudaGridSize(NUM_BLOCKS, 1, 1);
|
|
|
|
CUdeviceptr dinput, doutput, dtimer;
|
|
checkCudaErrors(cuMemAlloc(&dinput, sizeof(float) * NUM_THREADS * 2));
|
|
checkCudaErrors(cuMemAlloc(&doutput, sizeof(float) * NUM_BLOCKS));
|
|
checkCudaErrors(cuMemAlloc(&dtimer, sizeof(clock_t) * NUM_BLOCKS * 2));
|
|
checkCudaErrors(cuMemcpyHtoD(dinput, input, sizeof(float) * NUM_THREADS * 2));
|
|
|
|
void *arr[] = {(void *)&dinput, (void *)&doutput, (void *)&dtimer};
|
|
|
|
checkCudaErrors(cuLaunchKernel(
|
|
kernel_addr, cudaGridSize.x, cudaGridSize.y,
|
|
cudaGridSize.z, /* grid dim */
|
|
cudaBlockSize.x, cudaBlockSize.y, cudaBlockSize.z, /* block dim */
|
|
sizeof(float) * 2 * NUM_THREADS, 0, /* shared mem, stream */
|
|
&arr[0], /* arguments */
|
|
0));
|
|
|
|
checkCudaErrors(cuCtxSynchronize());
|
|
checkCudaErrors(
|
|
cuMemcpyDtoH(timer, dtimer, sizeof(clock_t) * NUM_BLOCKS * 2));
|
|
checkCudaErrors(cuMemFree(dinput));
|
|
checkCudaErrors(cuMemFree(doutput));
|
|
checkCudaErrors(cuMemFree(dtimer));
|
|
|
|
long double avgElapsedClocks = 0;
|
|
|
|
for (int i = 0; i < NUM_BLOCKS; i++) {
|
|
avgElapsedClocks += (long double)(timer[i + NUM_BLOCKS] - timer[i]);
|
|
}
|
|
|
|
avgElapsedClocks = avgElapsedClocks / NUM_BLOCKS;
|
|
printf("Average clocks/block = %Lf\n", avgElapsedClocks);
|
|
|
|
return EXIT_SUCCESS;
|
|
}
|