mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2026-10-25 22:28:17 +08:00
Apply consistent code formatting across the repo. Add clang-format and pre-commit hooks.
This commit is contained in:
Rob Armstrong
@@ -54,22 +54,21 @@
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*/
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// includes, system
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#include <stdlib.h>
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#include <stdio.h>
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#include <string.h>
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#include <math.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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// includes, project
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#include <helper_functions.h>
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#include <helper_cuda.h>
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#include <cuda_runtime.h>
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#include <helper_cuda.h>
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#include <helper_functions.h>
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const char *sSDKsample = "reductionMultiBlockCG";
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#include <cuda_runtime_api.h>
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#include <cooperative_groups.h>
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#include <cooperative_groups/reduce.h>
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#include <cuda_runtime_api.h>
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namespace cg = cooperative_groups;
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@@ -87,22 +86,23 @@ namespace cg = cooperative_groups;
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See the CUDA SDK "reduction" sample for more information.
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*/
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__device__ void reduceBlock(double *sdata, const cg::thread_block &cta) {
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const unsigned int tid = cta.thread_rank();
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cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
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__device__ void reduceBlock(double *sdata, const cg::thread_block &cta)
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{
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const unsigned int tid = cta.thread_rank();
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cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
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sdata[tid] = cg::reduce(tile32, sdata[tid], cg::plus<double>());
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cg::sync(cta);
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sdata[tid] = cg::reduce(tile32, sdata[tid], cg::plus<double>());
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cg::sync(cta);
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double beta = 0.0;
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if (cta.thread_rank() == 0) {
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beta = 0;
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for (int i = 0; i < blockDim.x; i += tile32.size()) {
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beta += sdata[i];
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double beta = 0.0;
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if (cta.thread_rank() == 0) {
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beta = 0;
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for (int i = 0; i < blockDim.x; i += tile32.size()) {
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beta += sdata[i];
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}
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sdata[0] = beta;
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}
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sdata[0] = beta;
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}
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cg::sync(cta);
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cg::sync(cta);
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}
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// This reduction kernel reduces an arbitrary size array in a single kernel
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@@ -110,56 +110,56 @@ __device__ void reduceBlock(double *sdata, const cg::thread_block &cta) {
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//
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// For more details on the reduction algorithm (notably the multi-pass
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// approach), see the "reduction" sample in the CUDA SDK.
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extern "C" __global__ void reduceSinglePassMultiBlockCG(const float *g_idata,
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float *g_odata,
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unsigned int n) {
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// Handle to thread block group
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cg::thread_block block = cg::this_thread_block();
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cg::grid_group grid = cg::this_grid();
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extern "C" __global__ void reduceSinglePassMultiBlockCG(const float *g_idata, float *g_odata, unsigned int n)
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{
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// Handle to thread block group
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cg::thread_block block = cg::this_thread_block();
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cg::grid_group grid = cg::this_grid();
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extern double __shared__ sdata[];
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extern double __shared__ sdata[];
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// Stride over grid and add the values to a shared memory buffer
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sdata[block.thread_rank()] = 0;
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// Stride over grid and add the values to a shared memory buffer
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sdata[block.thread_rank()] = 0;
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for (int i = grid.thread_rank(); i < n; i += grid.size()) {
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sdata[block.thread_rank()] += g_idata[i];
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}
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cg::sync(block);
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// Reduce each block (called once per block)
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reduceBlock(sdata, block);
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// Write out the result to global memory
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if (block.thread_rank() == 0) {
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g_odata[blockIdx.x] = sdata[0];
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}
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cg::sync(grid);
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if (grid.thread_rank() == 0) {
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for (int block = 1; block < gridDim.x; block++) {
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g_odata[0] += g_odata[block];
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for (int i = grid.thread_rank(); i < n; i += grid.size()) {
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sdata[block.thread_rank()] += g_idata[i];
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}
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cg::sync(block);
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// Reduce each block (called once per block)
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reduceBlock(sdata, block);
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// Write out the result to global memory
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if (block.thread_rank() == 0) {
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g_odata[blockIdx.x] = sdata[0];
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}
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cg::sync(grid);
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if (grid.thread_rank() == 0) {
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for (int block = 1; block < gridDim.x; block++) {
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g_odata[0] += g_odata[block];
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}
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}
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}
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}
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////////////////////////////////////////////////////////////////////////////////
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// Wrapper function for kernel launch
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////////////////////////////////////////////////////////////////////////////////
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void call_reduceSinglePassMultiBlockCG(int size, int threads, int numBlocks,
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float *d_idata, float *d_odata) {
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int smemSize = threads * sizeof(double);
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void *kernelArgs[] = {
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(void *)&d_idata, (void *)&d_odata, (void *)&size,
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};
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void call_reduceSinglePassMultiBlockCG(int size, int threads, int numBlocks, float *d_idata, float *d_odata)
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{
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int smemSize = threads * sizeof(double);
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void *kernelArgs[] = {
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(void *)&d_idata,
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(void *)&d_odata,
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(void *)&size,
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};
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dim3 dimBlock(threads, 1, 1);
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dim3 dimGrid(numBlocks, 1, 1);
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dim3 dimBlock(threads, 1, 1);
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dim3 dimGrid(numBlocks, 1, 1);
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cudaLaunchCooperativeKernel((void *)reduceSinglePassMultiBlockCG, dimGrid,
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dimBlock, kernelArgs, smemSize, NULL);
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// check if kernel execution generated an error
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getLastCudaError("Kernel execution failed");
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cudaLaunchCooperativeKernel((void *)reduceSinglePassMultiBlockCG, dimGrid, dimBlock, kernelArgs, smemSize, NULL);
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// check if kernel execution generated an error
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getLastCudaError("Kernel execution failed");
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}
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////////////////////////////////////////////////////////////////////////////////
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@@ -169,26 +169,26 @@ bool runTest(int argc, char **argv, int device);
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////////////////////////////////////////////////////////////////////////////////
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// Program main
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////////////////////////////////////////////////////////////////////////////////
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int main(int argc, char **argv) {
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cudaDeviceProp deviceProp = {0};
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int dev;
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int main(int argc, char **argv)
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{
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cudaDeviceProp deviceProp = {0};
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int dev;
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printf("%s Starting...\n\n", sSDKsample);
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printf("%s Starting...\n\n", sSDKsample);
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dev = findCudaDevice(argc, (const char **)argv);
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checkCudaErrors(cudaGetDeviceProperties(&deviceProp, dev));
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if (!deviceProp.cooperativeLaunch) {
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printf(
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"\nSelected GPU (%d) does not support Cooperative Kernel Launch, "
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"Waiving the run\n",
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dev);
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exit(EXIT_WAIVED);
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}
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dev = findCudaDevice(argc, (const char **)argv);
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checkCudaErrors(cudaGetDeviceProperties(&deviceProp, dev));
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if (!deviceProp.cooperativeLaunch) {
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printf("\nSelected GPU (%d) does not support Cooperative Kernel Launch, "
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"Waiving the run\n",
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dev);
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exit(EXIT_WAIVED);
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}
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bool bTestPassed = false;
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bTestPassed = runTest(argc, argv, dev);
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bool bTestPassed = false;
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bTestPassed = runTest(argc, argv, dev);
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exit(bTestPassed ? EXIT_SUCCESS : EXIT_FAILURE);
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exit(bTestPassed ? EXIT_SUCCESS : EXIT_FAILURE);
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}
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////////////////////////////////////////////////////////////////////////////////
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@@ -199,188 +199,191 @@ int main(int argc, char **argv) {
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//! @param data pointer to input data
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//! @param size number of input data elements
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////////////////////////////////////////////////////////////////////////////////
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template <class T>
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T reduceCPU(T *data, int size) {
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T sum = data[0];
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T c = (T)0.0;
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template <class T> T reduceCPU(T *data, int size)
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{
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T sum = data[0];
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T c = (T)0.0;
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for (int i = 1; i < size; i++) {
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T y = data[i] - c;
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T t = sum + y;
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c = (t - sum) - y;
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sum = t;
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}
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for (int i = 1; i < size; i++) {
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T y = data[i] - c;
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T t = sum + y;
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c = (t - sum) - y;
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sum = t;
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}
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return sum;
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return sum;
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}
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unsigned int nextPow2(unsigned int x) {
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--x;
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x |= x >> 1;
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x |= x >> 2;
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x |= x >> 4;
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x |= x >> 8;
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x |= x >> 16;
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return ++x;
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unsigned int nextPow2(unsigned int x)
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{
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--x;
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x |= x >> 1;
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x |= x >> 2;
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x |= x >> 4;
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x |= x >> 8;
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x |= x >> 16;
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return ++x;
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}
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////////////////////////////////////////////////////////////////////////////////
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// Compute the number of threads and blocks to use for the reduction
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// We set threads / block to the minimum of maxThreads and n/2.
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////////////////////////////////////////////////////////////////////////////////
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void getNumBlocksAndThreads(int n, int maxBlocks, int maxThreads, int &blocks,
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int &threads) {
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if (n == 1) {
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threads = 1;
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blocks = 1;
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} else {
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checkCudaErrors(cudaOccupancyMaxPotentialBlockSize(
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&blocks, &threads, reduceSinglePassMultiBlockCG));
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}
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void getNumBlocksAndThreads(int n, int maxBlocks, int maxThreads, int &blocks, int &threads)
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{
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if (n == 1) {
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threads = 1;
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blocks = 1;
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}
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else {
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checkCudaErrors(cudaOccupancyMaxPotentialBlockSize(&blocks, &threads, reduceSinglePassMultiBlockCG));
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}
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blocks = min(maxBlocks, blocks);
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blocks = min(maxBlocks, blocks);
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}
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////////////////////////////////////////////////////////////////////////////////
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// This function performs a reduction of the input data multiple times and
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// measures the average reduction time.
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////////////////////////////////////////////////////////////////////////////////
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float benchmarkReduce(int n, int numThreads, int numBlocks, int maxThreads,
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int maxBlocks, int testIterations,
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StopWatchInterface *timer, float *h_odata, float *d_idata,
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float *d_odata) {
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float gpu_result = 0;
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cudaError_t error;
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float benchmarkReduce(int n,
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int numThreads,
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int numBlocks,
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int maxThreads,
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int maxBlocks,
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int testIterations,
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StopWatchInterface *timer,
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float *h_odata,
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float *d_idata,
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float *d_odata)
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{
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float gpu_result = 0;
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cudaError_t error;
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printf("\nLaunching %s kernel\n",
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"SinglePass Multi Block Cooperative Groups");
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for (int i = 0; i < testIterations; ++i) {
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gpu_result = 0;
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sdkStartTimer(&timer);
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call_reduceSinglePassMultiBlockCG(n, numThreads, numBlocks, d_idata,
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d_odata);
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cudaDeviceSynchronize();
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sdkStopTimer(&timer);
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}
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printf("\nLaunching %s kernel\n", "SinglePass Multi Block Cooperative Groups");
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for (int i = 0; i < testIterations; ++i) {
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gpu_result = 0;
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sdkStartTimer(&timer);
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call_reduceSinglePassMultiBlockCG(n, numThreads, numBlocks, d_idata, d_odata);
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cudaDeviceSynchronize();
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sdkStopTimer(&timer);
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}
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// copy final sum from device to host
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error =
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cudaMemcpy(&gpu_result, d_odata, sizeof(float), cudaMemcpyDeviceToHost);
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checkCudaErrors(error);
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// copy final sum from device to host
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error = cudaMemcpy(&gpu_result, d_odata, sizeof(float), cudaMemcpyDeviceToHost);
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checkCudaErrors(error);
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return gpu_result;
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return gpu_result;
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}
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////////////////////////////////////////////////////////////////////////////////
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// The main function which runs the reduction test.
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////////////////////////////////////////////////////////////////////////////////
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bool runTest(int argc, char **argv, int device) {
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int size = 1 << 25; // number of elements to reduce
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bool bTestPassed = false;
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bool runTest(int argc, char **argv, int device)
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{
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int size = 1 << 25; // number of elements to reduce
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bool bTestPassed = false;
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if (checkCmdLineFlag(argc, (const char **)argv, "n")) {
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size = getCmdLineArgumentInt(argc, (const char **)argv, "n");
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}
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if (checkCmdLineFlag(argc, (const char **)argv, "n")) {
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size = getCmdLineArgumentInt(argc, (const char **)argv, "n");
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}
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printf("%d elements\n", size);
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printf("%d elements\n", size);
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// Set the device to be used
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cudaDeviceProp prop = {0};
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checkCudaErrors(cudaSetDevice(device));
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checkCudaErrors(cudaGetDeviceProperties(&prop, device));
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// Set the device to be used
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cudaDeviceProp prop = {0};
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checkCudaErrors(cudaSetDevice(device));
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checkCudaErrors(cudaGetDeviceProperties(&prop, device));
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// create random input data on CPU
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unsigned int bytes = size * sizeof(float);
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// create random input data on CPU
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unsigned int bytes = size * sizeof(float);
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float *h_idata = (float *)malloc(bytes);
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float *h_idata = (float *)malloc(bytes);
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for (int i = 0; i < size; i++) {
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// Keep the numbers small so we don't get truncation error in the sum
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h_idata[i] = (rand() & 0xFF) / (float)RAND_MAX;
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}
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for (int i = 0; i < size; i++) {
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// Keep the numbers small so we don't get truncation error in the sum
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h_idata[i] = (rand() & 0xFF) / (float)RAND_MAX;
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}
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// Determine the launch configuration (threads, blocks)
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int maxThreads = 0;
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int maxBlocks = 0;
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// Determine the launch configuration (threads, blocks)
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int maxThreads = 0;
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int maxBlocks = 0;
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if (checkCmdLineFlag(argc, (const char **)argv, "threads")) {
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maxThreads = getCmdLineArgumentInt(argc, (const char **)argv, "threads");
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} else {
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maxThreads = prop.maxThreadsPerBlock;
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}
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if (checkCmdLineFlag(argc, (const char **)argv, "threads")) {
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maxThreads = getCmdLineArgumentInt(argc, (const char **)argv, "threads");
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}
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else {
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maxThreads = prop.maxThreadsPerBlock;
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}
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if (checkCmdLineFlag(argc, (const char **)argv, "maxblocks")) {
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maxBlocks = getCmdLineArgumentInt(argc, (const char **)argv, "maxblocks");
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} else {
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maxBlocks = prop.multiProcessorCount *
|
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(prop.maxThreadsPerMultiProcessor / prop.maxThreadsPerBlock);
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}
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if (checkCmdLineFlag(argc, (const char **)argv, "maxblocks")) {
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maxBlocks = getCmdLineArgumentInt(argc, (const char **)argv, "maxblocks");
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}
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else {
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maxBlocks = prop.multiProcessorCount * (prop.maxThreadsPerMultiProcessor / prop.maxThreadsPerBlock);
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}
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int numBlocks = 0;
|
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int numThreads = 0;
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getNumBlocksAndThreads(size, maxBlocks, maxThreads, numBlocks, numThreads);
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int numBlocks = 0;
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int numThreads = 0;
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getNumBlocksAndThreads(size, maxBlocks, maxThreads, numBlocks, numThreads);
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// We calculate the occupancy to know how many block can actually fit on the
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// GPU
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int numBlocksPerSm = 0;
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checkCudaErrors(cudaOccupancyMaxActiveBlocksPerMultiprocessor(
|
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&numBlocksPerSm, reduceSinglePassMultiBlockCG, numThreads,
|
||||
numThreads * sizeof(double)));
|
||||
// We calculate the occupancy to know how many block can actually fit on the
|
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// GPU
|
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int numBlocksPerSm = 0;
|
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checkCudaErrors(cudaOccupancyMaxActiveBlocksPerMultiprocessor(
|
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&numBlocksPerSm, reduceSinglePassMultiBlockCG, numThreads, numThreads * sizeof(double)));
|
||||
|
||||
int numSms = prop.multiProcessorCount;
|
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if (numBlocks > numBlocksPerSm * numSms) {
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numBlocks = numBlocksPerSm * numSms;
|
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}
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printf("numThreads: %d\n", numThreads);
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printf("numBlocks: %d\n", numBlocks);
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int numSms = prop.multiProcessorCount;
|
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if (numBlocks > numBlocksPerSm * numSms) {
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numBlocks = numBlocksPerSm * numSms;
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}
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printf("numThreads: %d\n", numThreads);
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printf("numBlocks: %d\n", numBlocks);
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// allocate mem for the result on host side
|
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float *h_odata = (float *)malloc(numBlocks * sizeof(float));
|
||||
// allocate mem for the result on host side
|
||||
float *h_odata = (float *)malloc(numBlocks * sizeof(float));
|
||||
|
||||
// allocate device memory and data
|
||||
float *d_idata = NULL;
|
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float *d_odata = NULL;
|
||||
// allocate device memory and data
|
||||
float *d_idata = NULL;
|
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float *d_odata = NULL;
|
||||
|
||||
checkCudaErrors(cudaMalloc((void **)&d_idata, bytes));
|
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checkCudaErrors(cudaMalloc((void **)&d_odata, numBlocks * sizeof(float)));
|
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checkCudaErrors(cudaMalloc((void **)&d_idata, bytes));
|
||||
checkCudaErrors(cudaMalloc((void **)&d_odata, numBlocks * sizeof(float)));
|
||||
|
||||
// copy data directly to device memory
|
||||
checkCudaErrors(cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice));
|
||||
checkCudaErrors(cudaMemcpy(d_odata, h_idata, numBlocks * sizeof(float),
|
||||
cudaMemcpyHostToDevice));
|
||||
// copy data directly to device memory
|
||||
checkCudaErrors(cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice));
|
||||
checkCudaErrors(cudaMemcpy(d_odata, h_idata, numBlocks * sizeof(float), cudaMemcpyHostToDevice));
|
||||
|
||||
int testIterations = 100;
|
||||
int testIterations = 100;
|
||||
|
||||
StopWatchInterface *timer = 0;
|
||||
sdkCreateTimer(&timer);
|
||||
StopWatchInterface *timer = 0;
|
||||
sdkCreateTimer(&timer);
|
||||
|
||||
float gpu_result = 0;
|
||||
float gpu_result = 0;
|
||||
|
||||
gpu_result =
|
||||
benchmarkReduce(size, numThreads, numBlocks, maxThreads, maxBlocks,
|
||||
testIterations, timer, h_odata, d_idata, d_odata);
|
||||
gpu_result = benchmarkReduce(
|
||||
size, numThreads, numBlocks, maxThreads, maxBlocks, testIterations, timer, h_odata, d_idata, d_odata);
|
||||
|
||||
float reduceTime = sdkGetAverageTimerValue(&timer);
|
||||
printf("Average time: %f ms\n", reduceTime);
|
||||
printf("Bandwidth: %f GB/s\n\n",
|
||||
(size * sizeof(int)) / (reduceTime * 1.0e6));
|
||||
float reduceTime = sdkGetAverageTimerValue(&timer);
|
||||
printf("Average time: %f ms\n", reduceTime);
|
||||
printf("Bandwidth: %f GB/s\n\n", (size * sizeof(int)) / (reduceTime * 1.0e6));
|
||||
|
||||
// compute reference solution
|
||||
float cpu_result = reduceCPU<float>(h_idata, size);
|
||||
printf("GPU result = %0.12f\n", gpu_result);
|
||||
printf("CPU result = %0.12f\n", cpu_result);
|
||||
// compute reference solution
|
||||
float cpu_result = reduceCPU<float>(h_idata, size);
|
||||
printf("GPU result = %0.12f\n", gpu_result);
|
||||
printf("CPU result = %0.12f\n", cpu_result);
|
||||
|
||||
double threshold = 1e-8 * size;
|
||||
double diff = abs((double)gpu_result - (double)cpu_result);
|
||||
bTestPassed = (diff < threshold);
|
||||
double threshold = 1e-8 * size;
|
||||
double diff = abs((double)gpu_result - (double)cpu_result);
|
||||
bTestPassed = (diff < threshold);
|
||||
|
||||
// cleanup
|
||||
sdkDeleteTimer(&timer);
|
||||
// cleanup
|
||||
sdkDeleteTimer(&timer);
|
||||
|
||||
free(h_idata);
|
||||
free(h_odata);
|
||||
cudaFree(d_idata);
|
||||
cudaFree(d_odata);
|
||||
free(h_idata);
|
||||
free(h_odata);
|
||||
cudaFree(d_idata);
|
||||
cudaFree(d_odata);
|
||||
|
||||
return bTestPassed;
|
||||
return bTestPassed;
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user