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
@@ -37,15 +37,15 @@
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*/
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// System includes
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#include <stdio.h>
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#include <assert.h>
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#include <stdio.h>
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// CUDA runtime
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#include <cuda_runtime.h>
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// helper functions and utilities to work with CUDA
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#include <helper_functions.h>
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#include <helper_cuda.h>
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#include <helper_functions.h>
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#ifndef MAX
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#define MAX(a, b) (a > b ? a : b)
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@@ -57,180 +57,176 @@
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// Data configuration
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////////////////////////////////////////////////////////////////////////////////
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const int MAX_GPU_COUNT = 32;
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const int DATA_N = 1048576 * 32;
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const int DATA_N = 1048576 * 32;
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////////////////////////////////////////////////////////////////////////////////
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// Simple reduction kernel.
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// Refer to the 'reduction' CUDA Sample describing
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// reduction optimization strategies
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////////////////////////////////////////////////////////////////////////////////
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__global__ static void reduceKernel(float *d_Result, float *d_Input, int N) {
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const int tid = blockIdx.x * blockDim.x + threadIdx.x;
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const int threadN = gridDim.x * blockDim.x;
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float sum = 0;
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__global__ static void reduceKernel(float *d_Result, float *d_Input, int N)
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{
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const int tid = blockIdx.x * blockDim.x + threadIdx.x;
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const int threadN = gridDim.x * blockDim.x;
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float sum = 0;
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for (int pos = tid; pos < N; pos += threadN) sum += d_Input[pos];
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for (int pos = tid; pos < N; pos += threadN)
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sum += d_Input[pos];
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d_Result[tid] = sum;
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d_Result[tid] = sum;
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}
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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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// Solver config
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TGPUplan plan[MAX_GPU_COUNT];
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int main(int argc, char **argv)
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{
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// Solver config
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TGPUplan plan[MAX_GPU_COUNT];
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// GPU reduction results
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float h_SumGPU[MAX_GPU_COUNT];
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// GPU reduction results
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float h_SumGPU[MAX_GPU_COUNT];
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float sumGPU;
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double sumCPU, diff;
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float sumGPU;
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double sumCPU, diff;
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int i, j, gpuBase, GPU_N;
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int i, j, gpuBase, GPU_N;
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const int BLOCK_N = 32;
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const int THREAD_N = 256;
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const int ACCUM_N = BLOCK_N * THREAD_N;
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const int BLOCK_N = 32;
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const int THREAD_N = 256;
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const int ACCUM_N = BLOCK_N * THREAD_N;
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printf("Starting simpleMultiGPU\n");
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checkCudaErrors(cudaGetDeviceCount(&GPU_N));
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printf("Starting simpleMultiGPU\n");
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checkCudaErrors(cudaGetDeviceCount(&GPU_N));
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if (GPU_N > MAX_GPU_COUNT) {
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GPU_N = MAX_GPU_COUNT;
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}
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printf("CUDA-capable device count: %i\n", GPU_N);
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printf("Generating input data...\n\n");
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// Subdividing input data across GPUs
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// Get data sizes for each GPU
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for (i = 0; i < GPU_N; i++) {
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plan[i].dataN = DATA_N / GPU_N;
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}
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// Take into account "odd" data sizes
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for (i = 0; i < DATA_N % GPU_N; i++) {
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plan[i].dataN++;
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}
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// Assign data ranges to GPUs
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gpuBase = 0;
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for (i = 0; i < GPU_N; i++) {
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plan[i].h_Sum = h_SumGPU + i;
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gpuBase += plan[i].dataN;
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}
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// Create streams for issuing GPU command asynchronously and allocate memory
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// (GPU and System page-locked)
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for (i = 0; i < GPU_N; i++) {
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checkCudaErrors(cudaSetDevice(i));
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checkCudaErrors(cudaStreamCreate(&plan[i].stream));
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// Allocate memory
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checkCudaErrors(
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cudaMalloc((void **)&plan[i].d_Data, plan[i].dataN * sizeof(float)));
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checkCudaErrors(
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cudaMalloc((void **)&plan[i].d_Sum, ACCUM_N * sizeof(float)));
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checkCudaErrors(cudaMallocHost((void **)&plan[i].h_Sum_from_device,
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ACCUM_N * sizeof(float)));
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checkCudaErrors(cudaMallocHost((void **)&plan[i].h_Data,
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plan[i].dataN * sizeof(float)));
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for (j = 0; j < plan[i].dataN; j++) {
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plan[i].h_Data[j] = (float)rand() / (float)RAND_MAX;
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}
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}
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// Start timing and compute on GPU(s)
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printf("Computing with %d GPUs...\n", GPU_N);
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// create and start timer
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StopWatchInterface *timer = NULL;
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sdkCreateTimer(&timer);
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// start the timer
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sdkStartTimer(&timer);
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// Copy data to GPU, launch the kernel and copy data back. All asynchronously
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for (i = 0; i < GPU_N; i++) {
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// Set device
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checkCudaErrors(cudaSetDevice(i));
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// Copy input data from CPU
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checkCudaErrors(cudaMemcpyAsync(plan[i].d_Data, plan[i].h_Data,
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plan[i].dataN * sizeof(float),
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cudaMemcpyHostToDevice, plan[i].stream));
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// Perform GPU computations
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reduceKernel<<<BLOCK_N, THREAD_N, 0, plan[i].stream>>>(
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plan[i].d_Sum, plan[i].d_Data, plan[i].dataN);
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getLastCudaError("reduceKernel() execution failed.\n");
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// Read back GPU results
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checkCudaErrors(cudaMemcpyAsync(plan[i].h_Sum_from_device, plan[i].d_Sum,
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ACCUM_N * sizeof(float),
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cudaMemcpyDeviceToHost, plan[i].stream));
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}
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// Process GPU results
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for (i = 0; i < GPU_N; i++) {
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float sum;
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// Set device
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checkCudaErrors(cudaSetDevice(i));
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// Wait for all operations to finish
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cudaStreamSynchronize(plan[i].stream);
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// Finalize GPU reduction for current subvector
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sum = 0;
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for (j = 0; j < ACCUM_N; j++) {
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sum += plan[i].h_Sum_from_device[j];
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if (GPU_N > MAX_GPU_COUNT) {
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GPU_N = MAX_GPU_COUNT;
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}
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*(plan[i].h_Sum) = (float)sum;
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printf("CUDA-capable device count: %i\n", GPU_N);
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// Shut down this GPU
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checkCudaErrors(cudaFreeHost(plan[i].h_Sum_from_device));
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checkCudaErrors(cudaFree(plan[i].d_Sum));
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checkCudaErrors(cudaFree(plan[i].d_Data));
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checkCudaErrors(cudaStreamDestroy(plan[i].stream));
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}
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printf("Generating input data...\n\n");
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sumGPU = 0;
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for (i = 0; i < GPU_N; i++) {
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sumGPU += h_SumGPU[i];
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}
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sdkStopTimer(&timer);
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printf(" GPU Processing time: %f (ms)\n\n", sdkGetTimerValue(&timer));
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sdkDeleteTimer(&timer);
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// Compute on Host CPU
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printf("Computing with Host CPU...\n\n");
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sumCPU = 0;
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for (i = 0; i < GPU_N; i++) {
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for (j = 0; j < plan[i].dataN; j++) {
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sumCPU += plan[i].h_Data[j];
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// Subdividing input data across GPUs
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// Get data sizes for each GPU
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for (i = 0; i < GPU_N; i++) {
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plan[i].dataN = DATA_N / GPU_N;
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}
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}
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// Compare GPU and CPU results
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printf("Comparing GPU and Host CPU results...\n");
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diff = fabs(sumCPU - sumGPU) / fabs(sumCPU);
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printf(" GPU sum: %f\n CPU sum: %f\n", sumGPU, sumCPU);
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printf(" Relative difference: %E \n\n", diff);
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// Take into account "odd" data sizes
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for (i = 0; i < DATA_N % GPU_N; i++) {
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plan[i].dataN++;
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}
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// Cleanup and shutdown
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for (i = 0; i < GPU_N; i++) {
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checkCudaErrors(cudaSetDevice(i));
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checkCudaErrors(cudaFreeHost(plan[i].h_Data));
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}
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// Assign data ranges to GPUs
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gpuBase = 0;
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exit((diff < 1e-5) ? EXIT_SUCCESS : EXIT_FAILURE);
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for (i = 0; i < GPU_N; i++) {
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plan[i].h_Sum = h_SumGPU + i;
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gpuBase += plan[i].dataN;
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}
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// Create streams for issuing GPU command asynchronously and allocate memory
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// (GPU and System page-locked)
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for (i = 0; i < GPU_N; i++) {
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checkCudaErrors(cudaSetDevice(i));
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checkCudaErrors(cudaStreamCreate(&plan[i].stream));
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// Allocate memory
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checkCudaErrors(cudaMalloc((void **)&plan[i].d_Data, plan[i].dataN * sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&plan[i].d_Sum, ACCUM_N * sizeof(float)));
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checkCudaErrors(cudaMallocHost((void **)&plan[i].h_Sum_from_device, ACCUM_N * sizeof(float)));
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checkCudaErrors(cudaMallocHost((void **)&plan[i].h_Data, plan[i].dataN * sizeof(float)));
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for (j = 0; j < plan[i].dataN; j++) {
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plan[i].h_Data[j] = (float)rand() / (float)RAND_MAX;
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}
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}
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// Start timing and compute on GPU(s)
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printf("Computing with %d GPUs...\n", GPU_N);
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// create and start timer
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StopWatchInterface *timer = NULL;
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sdkCreateTimer(&timer);
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// start the timer
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sdkStartTimer(&timer);
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// Copy data to GPU, launch the kernel and copy data back. All asynchronously
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for (i = 0; i < GPU_N; i++) {
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// Set device
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checkCudaErrors(cudaSetDevice(i));
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// Copy input data from CPU
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checkCudaErrors(cudaMemcpyAsync(
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plan[i].d_Data, plan[i].h_Data, plan[i].dataN * sizeof(float), cudaMemcpyHostToDevice, plan[i].stream));
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// Perform GPU computations
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reduceKernel<<<BLOCK_N, THREAD_N, 0, plan[i].stream>>>(plan[i].d_Sum, plan[i].d_Data, plan[i].dataN);
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getLastCudaError("reduceKernel() execution failed.\n");
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// Read back GPU results
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checkCudaErrors(cudaMemcpyAsync(
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plan[i].h_Sum_from_device, plan[i].d_Sum, ACCUM_N * sizeof(float), cudaMemcpyDeviceToHost, plan[i].stream));
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}
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// Process GPU results
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for (i = 0; i < GPU_N; i++) {
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float sum;
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// Set device
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checkCudaErrors(cudaSetDevice(i));
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// Wait for all operations to finish
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cudaStreamSynchronize(plan[i].stream);
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// Finalize GPU reduction for current subvector
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sum = 0;
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for (j = 0; j < ACCUM_N; j++) {
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sum += plan[i].h_Sum_from_device[j];
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}
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*(plan[i].h_Sum) = (float)sum;
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// Shut down this GPU
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checkCudaErrors(cudaFreeHost(plan[i].h_Sum_from_device));
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checkCudaErrors(cudaFree(plan[i].d_Sum));
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checkCudaErrors(cudaFree(plan[i].d_Data));
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checkCudaErrors(cudaStreamDestroy(plan[i].stream));
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}
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sumGPU = 0;
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for (i = 0; i < GPU_N; i++) {
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sumGPU += h_SumGPU[i];
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}
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sdkStopTimer(&timer);
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printf(" GPU Processing time: %f (ms)\n\n", sdkGetTimerValue(&timer));
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sdkDeleteTimer(&timer);
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// Compute on Host CPU
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printf("Computing with Host CPU...\n\n");
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sumCPU = 0;
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for (i = 0; i < GPU_N; i++) {
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for (j = 0; j < plan[i].dataN; j++) {
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sumCPU += plan[i].h_Data[j];
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}
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}
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// Compare GPU and CPU results
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printf("Comparing GPU and Host CPU results...\n");
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diff = fabs(sumCPU - sumGPU) / fabs(sumCPU);
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printf(" GPU sum: %f\n CPU sum: %f\n", sumGPU, sumCPU);
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printf(" Relative difference: %E \n\n", diff);
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// Cleanup and shutdown
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for (i = 0; i < GPU_N; i++) {
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checkCudaErrors(cudaSetDevice(i));
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checkCudaErrors(cudaFreeHost(plan[i].h_Data));
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}
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exit((diff < 1e-5) ? EXIT_SUCCESS : EXIT_FAILURE);
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}
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@@ -37,26 +37,26 @@
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#ifndef SIMPLEMULTIGPU_H
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#define SIMPLEMULTIGPU_H
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typedef struct {
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// Host-side input data
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int dataN;
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float *h_Data;
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typedef struct
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{
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// Host-side input data
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int dataN;
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float *h_Data;
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// Partial sum for this GPU
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float *h_Sum;
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// Partial sum for this GPU
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float *h_Sum;
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// Device buffers
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float *d_Data, *d_Sum;
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// Device buffers
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float *d_Data, *d_Sum;
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// Reduction copied back from GPU
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float *h_Sum_from_device;
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// Reduction copied back from GPU
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float *h_Sum_from_device;
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// Stream for asynchronous command execution
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cudaStream_t stream;
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// Stream for asynchronous command execution
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cudaStream_t stream;
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} TGPUplan;
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extern "C" void launch_reduceKernel(float *d_Result, float *d_Input, int N,
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int BLOCK_N, int THREAD_N, cudaStream_t &s);
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extern "C" void launch_reduceKernel(float *d_Result, float *d_Input, int N, int BLOCK_N, int THREAD_N, cudaStream_t &s);
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#endif
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