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https://github.com/NVIDIA/cuda-samples.git
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215 lines
7.4 KiB
Plaintext
215 lines
7.4 KiB
Plaintext
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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/*
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* This sample implements multi-threaded heterogeneous computing workloads with
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* the new CPU callbacks for CUDA streams and events introduced with CUDA 5.0.
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* Together with the thread safety of the CUDA API implementing heterogeneous
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* workloads that float between CPU threads and GPUs has become simple and
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* efficient.
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*
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* The workloads in the sample follow the form CPU preprocess -> GPU process ->
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* CPU postprocess.
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* Each CPU processing step is handled by its own dedicated thread. GPU
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* workloads are sent to all available GPUs in the system.
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*
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*/
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// System includes
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#include <stdio.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 "multithreading.h"
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const int N_workloads = 8;
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const int N_elements_per_workload = 100000;
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CUTBarrier thread_barrier;
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void CUDART_CB myStreamCallback(cudaStream_t event, cudaError_t status,
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void *data);
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struct heterogeneous_workload {
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int id;
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int cudaDeviceID;
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int *h_data;
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int *d_data;
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cudaStream_t stream;
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bool success;
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};
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__global__ void incKernel(int *data, int N) {
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int i = blockIdx.x * blockDim.x + threadIdx.x;
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if (i < N) data[i]++;
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}
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CUT_THREADPROC launch(void *void_arg) {
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heterogeneous_workload *workload = (heterogeneous_workload *)void_arg;
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// Select GPU for this CPU thread
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checkCudaErrors(cudaSetDevice(workload->cudaDeviceID));
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// Allocate Resources
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checkCudaErrors(cudaStreamCreate(&workload->stream));
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checkCudaErrors(
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cudaMalloc(&workload->d_data, N_elements_per_workload * sizeof(int)));
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checkCudaErrors(cudaHostAlloc(&workload->h_data,
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N_elements_per_workload * sizeof(int),
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cudaHostAllocPortable));
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// CPU thread generates data
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for (int i = 0; i < N_elements_per_workload; ++i) {
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workload->h_data[i] = workload->id + i;
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}
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// Schedule work for GPU in CUDA stream without blocking the CPU thread
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// Note: Dedicated streams enable concurrent execution of workloads on the GPU
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dim3 block(512);
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dim3 grid((N_elements_per_workload + block.x - 1) / block.x);
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checkCudaErrors(cudaMemcpyAsync(workload->d_data, workload->h_data,
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N_elements_per_workload * sizeof(int),
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cudaMemcpyHostToDevice, workload->stream));
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incKernel<<<grid, block, 0, workload->stream>>>(workload->d_data,
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N_elements_per_workload);
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checkCudaErrors(cudaMemcpyAsync(workload->h_data, workload->d_data,
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N_elements_per_workload * sizeof(int),
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cudaMemcpyDeviceToHost, workload->stream));
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// New in CUDA 5.0: Add a CPU callback which is called once all currently
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// pending operations in the CUDA stream have finished
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checkCudaErrors(
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cudaStreamAddCallback(workload->stream, myStreamCallback, workload, 0));
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CUT_THREADEND;
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// CPU thread end of life, GPU continues to process data...
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}
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CUT_THREADPROC postprocess(void *void_arg) {
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heterogeneous_workload *workload = (heterogeneous_workload *)void_arg;
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// ... GPU is done with processing, continue on new CPU thread...
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// Select GPU for this CPU thread
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checkCudaErrors(cudaSetDevice(workload->cudaDeviceID));
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// CPU thread consumes results from GPU
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workload->success = true;
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for (int i = 0; i < N_workloads; ++i) {
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workload->success &= workload->h_data[i] == i + workload->id + 1;
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}
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// Free Resources
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checkCudaErrors(cudaFree(workload->d_data));
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checkCudaErrors(cudaFreeHost(workload->h_data));
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checkCudaErrors(cudaStreamDestroy(workload->stream));
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// Signal the end of the heterogeneous workload to main thread
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cutIncrementBarrier(&thread_barrier);
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CUT_THREADEND;
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}
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void CUDART_CB myStreamCallback(cudaStream_t stream, cudaError_t status,
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void *data) {
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// Check status of GPU after stream operations are done
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checkCudaErrors(status);
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// Spawn new CPU worker thread and continue processing on the CPU
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cutStartThread(postprocess, data);
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}
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int main(int argc, char **argv) {
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int N_gpus, max_gpus = 0;
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int gpuInfo[32]; // assume a maximum of 32 GPUs in a system configuration
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printf("Starting simpleCallback\n");
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checkCudaErrors(cudaGetDeviceCount(&N_gpus));
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printf("Found %d CUDA capable GPUs\n", N_gpus);
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if (N_gpus > 32) {
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printf("simpleCallback only supports 32 GPU(s)\n");
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}
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for (int devid = 0; devid < N_gpus; devid++) {
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int SMversion;
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cudaDeviceProp deviceProp;
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cudaSetDevice(devid);
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cudaGetDeviceProperties(&deviceProp, devid);
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SMversion = deviceProp.major << 4 + deviceProp.minor;
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printf("GPU[%d] %s supports SM %d.%d", devid, deviceProp.name,
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deviceProp.major, deviceProp.minor);
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printf(", %s GPU Callback Functions\n",
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(SMversion >= 0x11) ? "capable" : "NOT capable");
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if (SMversion >= 0x11) {
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gpuInfo[max_gpus++] = devid;
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}
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}
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printf("%d GPUs available to run Callback Functions\n", max_gpus);
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heterogeneous_workload *workloads;
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workloads = (heterogeneous_workload *)malloc(N_workloads *
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sizeof(heterogeneous_workload));
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;
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thread_barrier = cutCreateBarrier(N_workloads);
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// Main thread spawns a CPU worker thread for each heterogeneous workload
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printf("Starting %d heterogeneous computing workloads\n", N_workloads);
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for (int i = 0; i < N_workloads; ++i) {
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workloads[i].id = i;
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workloads[i].cudaDeviceID = gpuInfo[i % max_gpus]; // i % N_gpus;
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cutStartThread(launch, &workloads[i]);
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}
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// Sleep until all workloads have finished
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cutWaitForBarrier(&thread_barrier);
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printf("Total of %d workloads finished:\n", N_workloads);
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bool success = true;
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for (int i = 0; i < N_workloads; ++i) {
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success &= workloads[i].success;
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}
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printf("%s\n", success ? "Success" : "Failure");
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free(workloads);
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exit(success ? EXIT_SUCCESS : EXIT_FAILURE);
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}
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