cuda-samples/Samples/concurrentKernels/concurrentKernels.cu
2020-09-15 23:45:56 +05:30

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/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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//
// This sample demonstrates the use of streams for concurrent execution. It also
// illustrates how to introduce dependencies between CUDA streams with the
// cudaStreamWaitEvent function.
//
// Devices of compute capability 2.0 or higher can overlap the kernels
//
#include <cooperative_groups.h>
#include <stdio.h>
namespace cg = cooperative_groups;
#include <helper_cuda.h>
#include <helper_functions.h>
// This is a kernel that does no real work but runs at least for a specified
// number of clocks
__global__ void clock_block(clock_t *d_o, clock_t clock_count) {
unsigned int start_clock = (unsigned int)clock();
clock_t clock_offset = 0;
while (clock_offset < clock_count) {
unsigned int end_clock = (unsigned int)clock();
// The code below should work like
// this (thanks to modular arithmetics):
//
// clock_offset = (clock_t) (end_clock > start_clock ?
// end_clock - start_clock :
// end_clock + (0xffffffffu - start_clock));
//
// Indeed, let m = 2^32 then
// end - start = end + m - start (mod m).
clock_offset = (clock_t)(end_clock - start_clock);
}
d_o[0] = clock_offset;
}
// Single warp reduction kernel
__global__ void sum(clock_t *d_clocks, int N) {
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
__shared__ clock_t s_clocks[32];
clock_t my_sum = 0;
for (int i = threadIdx.x; i < N; i += blockDim.x) {
my_sum += d_clocks[i];
}
s_clocks[threadIdx.x] = my_sum;
cg::sync(cta);
for (int i = 16; i > 0; i /= 2) {
if (threadIdx.x < i) {
s_clocks[threadIdx.x] += s_clocks[threadIdx.x + i];
}
cg::sync(cta);
}
d_clocks[0] = s_clocks[0];
}
int main(int argc, char **argv) {
int nkernels = 8; // number of concurrent kernels
int nstreams = nkernels + 1; // use one more stream than concurrent kernel
int nbytes = nkernels * sizeof(clock_t); // number of data bytes
float kernel_time = 10; // time the kernel should run in ms
float elapsed_time; // timing variables
int cuda_device = 0;
printf("[%s] - Starting...\n", argv[0]);
// get number of kernels if overridden on the command line
if (checkCmdLineFlag(argc, (const char **)argv, "nkernels")) {
nkernels = getCmdLineArgumentInt(argc, (const char **)argv, "nkernels");
nstreams = nkernels + 1;
}
// use command-line specified CUDA device, otherwise use device with highest
// Gflops/s
cuda_device = findCudaDevice(argc, (const char **)argv);
cudaDeviceProp deviceProp;
checkCudaErrors(cudaGetDevice(&cuda_device));
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, cuda_device));
if ((deviceProp.concurrentKernels == 0)) {
printf("> GPU does not support concurrent kernel execution\n");
printf(" CUDA kernel runs will be serialized\n");
}
printf("> Detected Compute SM %d.%d hardware with %d multi-processors\n",
deviceProp.major, deviceProp.minor, deviceProp.multiProcessorCount);
// allocate host memory
clock_t *a = 0; // pointer to the array data in host memory
checkCudaErrors(cudaMallocHost((void **)&a, nbytes));
// allocate device memory
clock_t *d_a = 0; // pointers to data and init value in the device memory
checkCudaErrors(cudaMalloc((void **)&d_a, nbytes));
// allocate and initialize an array of stream handles
cudaStream_t *streams =
(cudaStream_t *)malloc(nstreams * sizeof(cudaStream_t));
for (int i = 0; i < nstreams; i++) {
checkCudaErrors(cudaStreamCreate(&(streams[i])));
}
// create CUDA event handles
cudaEvent_t start_event, stop_event;
checkCudaErrors(cudaEventCreate(&start_event));
checkCudaErrors(cudaEventCreate(&stop_event));
// the events are used for synchronization only and hence do not need to
// record timings this also makes events not introduce global sync points when
// recorded which is critical to get overlap
cudaEvent_t *kernelEvent;
kernelEvent = (cudaEvent_t *)malloc(nkernels * sizeof(cudaEvent_t));
for (int i = 0; i < nkernels; i++) {
checkCudaErrors(
cudaEventCreateWithFlags(&(kernelEvent[i]), cudaEventDisableTiming));
}
//////////////////////////////////////////////////////////////////////
// time execution with nkernels streams
clock_t total_clocks = 0;
#if defined(__arm__) || defined(__aarch64__)
// the kernel takes more time than the channel reset time on arm archs, so to
// prevent hangs reduce time_clocks.
clock_t time_clocks = (clock_t)(kernel_time * (deviceProp.clockRate / 100));
#else
clock_t time_clocks = (clock_t)(kernel_time * deviceProp.clockRate);
#endif
cudaEventRecord(start_event, 0);
// queue nkernels in separate streams and record when they are done
for (int i = 0; i < nkernels; ++i) {
clock_block<<<1, 1, 0, streams[i]>>>(&d_a[i], time_clocks);
total_clocks += time_clocks;
checkCudaErrors(cudaEventRecord(kernelEvent[i], streams[i]));
// make the last stream wait for the kernel event to be recorded
checkCudaErrors(
cudaStreamWaitEvent(streams[nstreams - 1], kernelEvent[i], 0));
}
// queue a sum kernel and a copy back to host in the last stream.
// the commands in this stream get dispatched as soon as all the kernel events
// have been recorded
sum<<<1, 32, 0, streams[nstreams - 1]>>>(d_a, nkernels);
checkCudaErrors(cudaMemcpyAsync(
a, d_a, sizeof(clock_t), cudaMemcpyDeviceToHost, streams[nstreams - 1]));
// at this point the CPU has dispatched all work for the GPU and can continue
// processing other tasks in parallel
// in this sample we just wait until the GPU is done
checkCudaErrors(cudaEventRecord(stop_event, 0));
checkCudaErrors(cudaEventSynchronize(stop_event));
checkCudaErrors(cudaEventElapsedTime(&elapsed_time, start_event, stop_event));
printf("Expected time for serial execution of %d kernels = %.3fs\n", nkernels,
nkernels * kernel_time / 1000.0f);
printf("Expected time for concurrent execution of %d kernels = %.3fs\n",
nkernels, kernel_time / 1000.0f);
printf("Measured time for sample = %.3fs\n", elapsed_time / 1000.0f);
bool bTestResult = (a[0] > total_clocks);
// release resources
for (int i = 0; i < nkernels; i++) {
cudaStreamDestroy(streams[i]);
cudaEventDestroy(kernelEvent[i]);
}
free(streams);
free(kernelEvent);
cudaEventDestroy(start_event);
cudaEventDestroy(stop_event);
cudaFreeHost(a);
cudaFree(d_a);
if (!bTestResult) {
printf("Test failed!\n");
exit(EXIT_FAILURE);
}
printf("Test passed\n");
exit(EXIT_SUCCESS);
}