mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2024-12-01 09:19:16 +08:00
413 lines
15 KiB
Plaintext
413 lines
15 KiB
Plaintext
/* Copyright (c) 2019, 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.
|
|
*/
|
|
|
|
#include <cooperative_groups.h>
|
|
#include <cuda_runtime.h>
|
|
#include <helper_cuda.h>
|
|
#include <vector>
|
|
|
|
namespace cg = cooperative_groups;
|
|
|
|
#define THREADS_PER_BLOCK 512
|
|
#define GRAPH_LAUNCH_ITERATIONS 3
|
|
|
|
typedef struct callBackData {
|
|
const char *fn_name;
|
|
double *data;
|
|
} callBackData_t;
|
|
|
|
__global__ void reduce(float *inputVec, double *outputVec, size_t inputSize,
|
|
size_t outputSize) {
|
|
__shared__ double tmp[THREADS_PER_BLOCK];
|
|
|
|
cg::thread_block cta = cg::this_thread_block();
|
|
size_t globaltid = blockIdx.x * blockDim.x + threadIdx.x;
|
|
|
|
double temp_sum = 0.0;
|
|
for (int i = globaltid; i < inputSize; i += gridDim.x * blockDim.x) {
|
|
temp_sum += (double)inputVec[i];
|
|
}
|
|
tmp[cta.thread_rank()] = temp_sum;
|
|
|
|
cg::sync(cta);
|
|
|
|
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
|
|
|
|
double beta = temp_sum;
|
|
double temp;
|
|
|
|
for (int i = tile32.size() / 2; i > 0; i >>= 1) {
|
|
if (tile32.thread_rank() < i) {
|
|
temp = tmp[cta.thread_rank() + i];
|
|
beta += temp;
|
|
tmp[cta.thread_rank()] = beta;
|
|
}
|
|
cg::sync(tile32);
|
|
}
|
|
cg::sync(cta);
|
|
|
|
if (cta.thread_rank() == 0 && blockIdx.x < outputSize) {
|
|
beta = 0.0;
|
|
for (int i = 0; i < cta.size(); i += tile32.size()) {
|
|
beta += tmp[i];
|
|
}
|
|
outputVec[blockIdx.x] = beta;
|
|
}
|
|
}
|
|
|
|
__global__ void reduceFinal(double *inputVec, double *result,
|
|
size_t inputSize) {
|
|
__shared__ double tmp[THREADS_PER_BLOCK];
|
|
|
|
cg::thread_block cta = cg::this_thread_block();
|
|
size_t globaltid = blockIdx.x * blockDim.x + threadIdx.x;
|
|
|
|
double temp_sum = 0.0;
|
|
for (int i = globaltid; i < inputSize; i += gridDim.x * blockDim.x) {
|
|
temp_sum += (double)inputVec[i];
|
|
}
|
|
tmp[cta.thread_rank()] = temp_sum;
|
|
|
|
cg::sync(cta);
|
|
|
|
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
|
|
|
|
// do reduction in shared mem
|
|
if ((blockDim.x >= 512) && (cta.thread_rank() < 256)) {
|
|
tmp[cta.thread_rank()] = temp_sum = temp_sum + tmp[cta.thread_rank() + 256];
|
|
}
|
|
|
|
cg::sync(cta);
|
|
|
|
if ((blockDim.x >= 256) && (cta.thread_rank() < 128)) {
|
|
tmp[cta.thread_rank()] = temp_sum = temp_sum + tmp[cta.thread_rank() + 128];
|
|
}
|
|
|
|
cg::sync(cta);
|
|
|
|
if ((blockDim.x >= 128) && (cta.thread_rank() < 64)) {
|
|
tmp[cta.thread_rank()] = temp_sum = temp_sum + tmp[cta.thread_rank() + 64];
|
|
}
|
|
|
|
cg::sync(cta);
|
|
|
|
if (cta.thread_rank() < 32) {
|
|
// Fetch final intermediate sum from 2nd warp
|
|
if (blockDim.x >= 64) temp_sum += tmp[cta.thread_rank() + 32];
|
|
// Reduce final warp using shuffle
|
|
for (int offset = tile32.size() / 2; offset > 0; offset /= 2) {
|
|
temp_sum += tile32.shfl_down(temp_sum, offset);
|
|
}
|
|
}
|
|
// write result for this block to global mem
|
|
if (cta.thread_rank() == 0) result[0] = temp_sum;
|
|
}
|
|
|
|
void init_input(float *a, size_t size) {
|
|
for (size_t i = 0; i < size; i++) a[i] = (rand() & 0xFF) / (float)RAND_MAX;
|
|
}
|
|
|
|
void CUDART_CB myHostNodeCallback(void *data) {
|
|
// Check status of GPU after stream operations are done
|
|
callBackData_t *tmp = (callBackData_t *)(data);
|
|
// checkCudaErrors(tmp->status);
|
|
|
|
double *result = (double *)(tmp->data);
|
|
char *function = (char *)(tmp->fn_name);
|
|
printf("[%s] Host callback final reduced sum = %lf\n", function, *result);
|
|
*result = 0.0; // reset the result
|
|
}
|
|
|
|
void cudaGraphsManual(float *inputVec_h, float *inputVec_d, double *outputVec_d,
|
|
double *result_d, size_t inputSize, size_t numOfBlocks) {
|
|
cudaStream_t streamForGraph;
|
|
cudaGraph_t graph;
|
|
std::vector<cudaGraphNode_t> nodeDependencies;
|
|
cudaGraphNode_t memcpyNode, kernelNode, memsetNode;
|
|
double result_h = 0.0;
|
|
|
|
checkCudaErrors(cudaStreamCreate(&streamForGraph));
|
|
|
|
cudaKernelNodeParams kernelNodeParams = {0};
|
|
cudaMemcpy3DParms memcpyParams = {0};
|
|
cudaMemsetParams memsetParams = {0};
|
|
|
|
memcpyParams.srcArray = NULL;
|
|
memcpyParams.srcPos = make_cudaPos(0, 0, 0);
|
|
memcpyParams.srcPtr =
|
|
make_cudaPitchedPtr(inputVec_h, sizeof(float) * inputSize, inputSize, 1);
|
|
memcpyParams.dstArray = NULL;
|
|
memcpyParams.dstPos = make_cudaPos(0, 0, 0);
|
|
memcpyParams.dstPtr =
|
|
make_cudaPitchedPtr(inputVec_d, sizeof(float) * inputSize, inputSize, 1);
|
|
memcpyParams.extent = make_cudaExtent(sizeof(float) * inputSize, 1, 1);
|
|
memcpyParams.kind = cudaMemcpyHostToDevice;
|
|
|
|
memsetParams.dst = (void *)outputVec_d;
|
|
memsetParams.value = 0;
|
|
memsetParams.pitch = 0;
|
|
memsetParams.elementSize = sizeof(float); // elementSize can be max 4 bytes
|
|
memsetParams.width = numOfBlocks * 2;
|
|
memsetParams.height = 1;
|
|
|
|
checkCudaErrors(cudaGraphCreate(&graph, 0));
|
|
checkCudaErrors(
|
|
cudaGraphAddMemcpyNode(&memcpyNode, graph, NULL, 0, &memcpyParams));
|
|
checkCudaErrors(
|
|
cudaGraphAddMemsetNode(&memsetNode, graph, NULL, 0, &memsetParams));
|
|
|
|
nodeDependencies.push_back(memsetNode);
|
|
nodeDependencies.push_back(memcpyNode);
|
|
|
|
void *kernelArgs[4] = {(void *)&inputVec_d, (void *)&outputVec_d, &inputSize,
|
|
&numOfBlocks};
|
|
|
|
kernelNodeParams.func = (void *)reduce;
|
|
kernelNodeParams.gridDim = dim3(numOfBlocks, 1, 1);
|
|
kernelNodeParams.blockDim = dim3(THREADS_PER_BLOCK, 1, 1);
|
|
kernelNodeParams.sharedMemBytes = 0;
|
|
kernelNodeParams.kernelParams = (void **)kernelArgs;
|
|
kernelNodeParams.extra = NULL;
|
|
|
|
checkCudaErrors(
|
|
cudaGraphAddKernelNode(&kernelNode, graph, nodeDependencies.data(),
|
|
nodeDependencies.size(), &kernelNodeParams));
|
|
|
|
nodeDependencies.clear();
|
|
nodeDependencies.push_back(kernelNode);
|
|
|
|
memset(&memsetParams, 0, sizeof(memsetParams));
|
|
memsetParams.dst = result_d;
|
|
memsetParams.value = 0;
|
|
memsetParams.elementSize = sizeof(float);
|
|
memsetParams.width = 2;
|
|
memsetParams.height = 1;
|
|
checkCudaErrors(
|
|
cudaGraphAddMemsetNode(&memsetNode, graph, NULL, 0, &memsetParams));
|
|
|
|
nodeDependencies.push_back(memsetNode);
|
|
|
|
memset(&kernelNodeParams, 0, sizeof(kernelNodeParams));
|
|
kernelNodeParams.func = (void *)reduceFinal;
|
|
kernelNodeParams.gridDim = dim3(1, 1, 1);
|
|
kernelNodeParams.blockDim = dim3(THREADS_PER_BLOCK, 1, 1);
|
|
kernelNodeParams.sharedMemBytes = 0;
|
|
void *kernelArgs2[3] = {(void *)&outputVec_d, (void *)&result_d,
|
|
&numOfBlocks};
|
|
kernelNodeParams.kernelParams = kernelArgs2;
|
|
kernelNodeParams.extra = NULL;
|
|
|
|
checkCudaErrors(
|
|
cudaGraphAddKernelNode(&kernelNode, graph, nodeDependencies.data(),
|
|
nodeDependencies.size(), &kernelNodeParams));
|
|
nodeDependencies.clear();
|
|
nodeDependencies.push_back(kernelNode);
|
|
|
|
memset(&memcpyParams, 0, sizeof(memcpyParams));
|
|
|
|
memcpyParams.srcArray = NULL;
|
|
memcpyParams.srcPos = make_cudaPos(0, 0, 0);
|
|
memcpyParams.srcPtr = make_cudaPitchedPtr(result_d, sizeof(double), 1, 1);
|
|
memcpyParams.dstArray = NULL;
|
|
memcpyParams.dstPos = make_cudaPos(0, 0, 0);
|
|
memcpyParams.dstPtr = make_cudaPitchedPtr(&result_h, sizeof(double), 1, 1);
|
|
memcpyParams.extent = make_cudaExtent(sizeof(double), 1, 1);
|
|
memcpyParams.kind = cudaMemcpyDeviceToHost;
|
|
checkCudaErrors(
|
|
cudaGraphAddMemcpyNode(&memcpyNode, graph, nodeDependencies.data(),
|
|
nodeDependencies.size(), &memcpyParams));
|
|
nodeDependencies.clear();
|
|
nodeDependencies.push_back(memcpyNode);
|
|
|
|
cudaGraphNode_t hostNode;
|
|
cudaHostNodeParams hostParams = {0};
|
|
hostParams.fn = myHostNodeCallback;
|
|
callBackData_t hostFnData;
|
|
hostFnData.data = &result_h;
|
|
hostFnData.fn_name = "cudaGraphsManual";
|
|
hostParams.userData = &hostFnData;
|
|
|
|
checkCudaErrors(cudaGraphAddHostNode(&hostNode, graph,
|
|
nodeDependencies.data(),
|
|
nodeDependencies.size(), &hostParams));
|
|
|
|
cudaGraphNode_t *nodes = NULL;
|
|
size_t numNodes = 0;
|
|
checkCudaErrors(cudaGraphGetNodes(graph, nodes, &numNodes));
|
|
printf("\nNum of nodes in the graph created manually = %zu\n", numNodes);
|
|
|
|
cudaGraphExec_t graphExec;
|
|
checkCudaErrors(cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0));
|
|
|
|
cudaGraph_t clonedGraph;
|
|
cudaGraphExec_t clonedGraphExec;
|
|
checkCudaErrors(cudaGraphClone(&clonedGraph, graph));
|
|
checkCudaErrors(
|
|
cudaGraphInstantiate(&clonedGraphExec, clonedGraph, NULL, NULL, 0));
|
|
|
|
for (int i = 0; i < GRAPH_LAUNCH_ITERATIONS; i++) {
|
|
checkCudaErrors(cudaGraphLaunch(graphExec, streamForGraph));
|
|
}
|
|
|
|
checkCudaErrors(cudaStreamSynchronize(streamForGraph));
|
|
|
|
printf("Cloned Graph Output.. \n");
|
|
for (int i = 0; i < GRAPH_LAUNCH_ITERATIONS; i++) {
|
|
checkCudaErrors(cudaGraphLaunch(clonedGraphExec, streamForGraph));
|
|
}
|
|
checkCudaErrors(cudaStreamSynchronize(streamForGraph));
|
|
|
|
checkCudaErrors(cudaGraphExecDestroy(graphExec));
|
|
checkCudaErrors(cudaGraphExecDestroy(clonedGraphExec));
|
|
checkCudaErrors(cudaGraphDestroy(graph));
|
|
checkCudaErrors(cudaGraphDestroy(clonedGraph));
|
|
checkCudaErrors(cudaStreamDestroy(streamForGraph));
|
|
}
|
|
|
|
void cudaGraphsUsingStreamCapture(float *inputVec_h, float *inputVec_d,
|
|
double *outputVec_d, double *result_d,
|
|
size_t inputSize, size_t numOfBlocks) {
|
|
cudaStream_t stream1, stream2, stream3, streamForGraph;
|
|
cudaEvent_t forkStreamEvent, memsetEvent1, memsetEvent2;
|
|
cudaGraph_t graph;
|
|
double result_h = 0.0;
|
|
|
|
checkCudaErrors(cudaStreamCreate(&stream1));
|
|
checkCudaErrors(cudaStreamCreate(&stream2));
|
|
checkCudaErrors(cudaStreamCreate(&stream3));
|
|
checkCudaErrors(cudaStreamCreate(&streamForGraph));
|
|
|
|
checkCudaErrors(cudaEventCreate(&forkStreamEvent));
|
|
checkCudaErrors(cudaEventCreate(&memsetEvent1));
|
|
checkCudaErrors(cudaEventCreate(&memsetEvent2));
|
|
|
|
checkCudaErrors(cudaStreamBeginCapture(stream1, cudaStreamCaptureModeGlobal));
|
|
|
|
checkCudaErrors(cudaEventRecord(forkStreamEvent, stream1));
|
|
checkCudaErrors(cudaStreamWaitEvent(stream2, forkStreamEvent, 0));
|
|
checkCudaErrors(cudaStreamWaitEvent(stream3, forkStreamEvent, 0));
|
|
|
|
checkCudaErrors(cudaMemcpyAsync(inputVec_d, inputVec_h,
|
|
sizeof(float) * inputSize, cudaMemcpyDefault,
|
|
stream1));
|
|
|
|
checkCudaErrors(
|
|
cudaMemsetAsync(outputVec_d, 0, sizeof(double) * numOfBlocks, stream2));
|
|
|
|
checkCudaErrors(cudaEventRecord(memsetEvent1, stream2));
|
|
|
|
checkCudaErrors(cudaMemsetAsync(result_d, 0, sizeof(double), stream3));
|
|
checkCudaErrors(cudaEventRecord(memsetEvent2, stream3));
|
|
|
|
checkCudaErrors(cudaStreamWaitEvent(stream1, memsetEvent1, 0));
|
|
|
|
reduce<<<numOfBlocks, THREADS_PER_BLOCK, 0, stream1>>>(
|
|
inputVec_d, outputVec_d, inputSize, numOfBlocks);
|
|
|
|
checkCudaErrors(cudaStreamWaitEvent(stream1, memsetEvent2, 0));
|
|
|
|
reduceFinal<<<1, THREADS_PER_BLOCK, 0, stream1>>>(outputVec_d, result_d,
|
|
numOfBlocks);
|
|
checkCudaErrors(cudaMemcpyAsync(&result_h, result_d, sizeof(double),
|
|
cudaMemcpyDefault, stream1));
|
|
|
|
callBackData_t hostFnData = {0};
|
|
hostFnData.data = &result_h;
|
|
hostFnData.fn_name = "cudaGraphsUsingStreamCapture";
|
|
cudaHostFn_t fn = myHostNodeCallback;
|
|
checkCudaErrors(cudaLaunchHostFunc(stream1, fn, &hostFnData));
|
|
checkCudaErrors(cudaStreamEndCapture(stream1, &graph));
|
|
|
|
cudaGraphNode_t *nodes = NULL;
|
|
size_t numNodes = 0;
|
|
checkCudaErrors(cudaGraphGetNodes(graph, nodes, &numNodes));
|
|
printf("\nNum of nodes in the graph created using stream capture API = %zu\n",
|
|
numNodes);
|
|
|
|
cudaGraphExec_t graphExec;
|
|
checkCudaErrors(cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0));
|
|
|
|
cudaGraph_t clonedGraph;
|
|
cudaGraphExec_t clonedGraphExec;
|
|
checkCudaErrors(cudaGraphClone(&clonedGraph, graph));
|
|
checkCudaErrors(
|
|
cudaGraphInstantiate(&clonedGraphExec, clonedGraph, NULL, NULL, 0));
|
|
|
|
for (int i = 0; i < GRAPH_LAUNCH_ITERATIONS; i++) {
|
|
checkCudaErrors(cudaGraphLaunch(graphExec, streamForGraph));
|
|
}
|
|
|
|
checkCudaErrors(cudaStreamSynchronize(streamForGraph));
|
|
|
|
printf("Cloned Graph Output.. \n");
|
|
for (int i = 0; i < GRAPH_LAUNCH_ITERATIONS; i++) {
|
|
checkCudaErrors(cudaGraphLaunch(clonedGraphExec, streamForGraph));
|
|
}
|
|
|
|
checkCudaErrors(cudaStreamSynchronize(streamForGraph));
|
|
|
|
checkCudaErrors(cudaGraphExecDestroy(graphExec));
|
|
checkCudaErrors(cudaGraphExecDestroy(clonedGraphExec));
|
|
checkCudaErrors(cudaGraphDestroy(graph));
|
|
checkCudaErrors(cudaGraphDestroy(clonedGraph));
|
|
checkCudaErrors(cudaStreamDestroy(stream1));
|
|
checkCudaErrors(cudaStreamDestroy(stream2));
|
|
checkCudaErrors(cudaStreamDestroy(streamForGraph));
|
|
}
|
|
|
|
int main(int argc, char **argv) {
|
|
size_t size = 1 << 24; // number of elements to reduce
|
|
size_t maxBlocks = 512;
|
|
|
|
// This will pick the best possible CUDA capable device
|
|
int devID = findCudaDevice(argc, (const char **)argv);
|
|
|
|
printf("%zu elements\n", size);
|
|
printf("threads per block = %d\n", THREADS_PER_BLOCK);
|
|
printf("Graph Launch iterations = %d\n", GRAPH_LAUNCH_ITERATIONS);
|
|
|
|
float *inputVec_d = NULL, *inputVec_h = NULL;
|
|
double *outputVec_d = NULL, *result_d;
|
|
|
|
inputVec_h = (float *)malloc(sizeof(float) * size);
|
|
checkCudaErrors(cudaMalloc(&inputVec_d, sizeof(float) * size));
|
|
checkCudaErrors(cudaMalloc(&outputVec_d, sizeof(double) * maxBlocks));
|
|
checkCudaErrors(cudaMalloc(&result_d, sizeof(double)));
|
|
|
|
init_input(inputVec_h, size);
|
|
|
|
cudaGraphsManual(inputVec_h, inputVec_d, outputVec_d, result_d, size,
|
|
maxBlocks);
|
|
cudaGraphsUsingStreamCapture(inputVec_h, inputVec_d, outputVec_d, result_d,
|
|
size, maxBlocks);
|
|
|
|
checkCudaErrors(cudaFree(inputVec_d));
|
|
checkCudaErrors(cudaFree(outputVec_d));
|
|
checkCudaErrors(cudaFree(result_d));
|
|
return EXIT_SUCCESS;
|
|
}
|