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
@@ -65,9 +65,9 @@
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#include <cuda_runtime.h>
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// Utilities and system includes
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#include <algorithm>
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#include <helper_cuda.h>
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#include <helper_functions.h>
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#include <algorithm>
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// includes, project
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#include "reduction.h"
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@@ -76,8 +76,7 @@ enum ReduceType { REDUCE_INT, REDUCE_FLOAT, REDUCE_DOUBLE };
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////////////////////////////////////////////////////////////////////////////////
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// declaration, forward
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template <class T>
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bool runTest(int argc, char **argv, ReduceType datatype);
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template <class T> bool runTest(int argc, char **argv, ReduceType datatype);
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#define MAX_BLOCK_DIM_SIZE 65535
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@@ -87,71 +86,74 @@ bool runTest(int argc, char **argv, ReduceType datatype);
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extern "C" bool isPow2(unsigned int x) { return ((x & (x - 1)) == 0); }
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const char *getReduceTypeString(const ReduceType type) {
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switch (type) {
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const char *getReduceTypeString(const ReduceType type)
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{
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switch (type) {
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case REDUCE_INT:
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return "int";
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return "int";
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case REDUCE_FLOAT:
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return "float";
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return "float";
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case REDUCE_DOUBLE:
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return "double";
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return "double";
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default:
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return "unknown";
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}
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return "unknown";
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}
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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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printf("%s Starting...\n\n", argv[0]);
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int main(int argc, char **argv)
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{
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printf("%s Starting...\n\n", argv[0]);
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char *typeInput = 0;
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getCmdLineArgumentString(argc, (const char **)argv, "type", &typeInput);
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char *typeInput = 0;
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getCmdLineArgumentString(argc, (const char **)argv, "type", &typeInput);
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ReduceType datatype = REDUCE_INT;
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ReduceType datatype = REDUCE_INT;
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if (0 != typeInput) {
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if (!strcasecmp(typeInput, "float")) {
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datatype = REDUCE_FLOAT;
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} else if (!strcasecmp(typeInput, "double")) {
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datatype = REDUCE_DOUBLE;
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} else if (strcasecmp(typeInput, "int")) {
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printf("Type %s is not recognized. Using default type int.\n\n",
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typeInput);
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if (0 != typeInput) {
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if (!strcasecmp(typeInput, "float")) {
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datatype = REDUCE_FLOAT;
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}
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else if (!strcasecmp(typeInput, "double")) {
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datatype = REDUCE_DOUBLE;
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}
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else if (strcasecmp(typeInput, "int")) {
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printf("Type %s is not recognized. Using default type int.\n\n", typeInput);
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}
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}
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}
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cudaDeviceProp deviceProp;
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int dev;
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cudaDeviceProp deviceProp;
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int dev;
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dev = findCudaDevice(argc, (const char **)argv);
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dev = findCudaDevice(argc, (const char **)argv);
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checkCudaErrors(cudaGetDeviceProperties(&deviceProp, dev));
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checkCudaErrors(cudaGetDeviceProperties(&deviceProp, dev));
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printf("Using Device %d: %s\n\n", dev, deviceProp.name);
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checkCudaErrors(cudaSetDevice(dev));
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printf("Using Device %d: %s\n\n", dev, deviceProp.name);
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checkCudaErrors(cudaSetDevice(dev));
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printf("Reducing array of type %s\n\n", getReduceTypeString(datatype));
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printf("Reducing array of type %s\n\n", getReduceTypeString(datatype));
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bool bResult = false;
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bool bResult = false;
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switch (datatype) {
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switch (datatype) {
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default:
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case REDUCE_INT:
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bResult = runTest<int>(argc, argv, datatype);
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break;
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bResult = runTest<int>(argc, argv, datatype);
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break;
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case REDUCE_FLOAT:
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bResult = runTest<float>(argc, argv, datatype);
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break;
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bResult = runTest<float>(argc, argv, datatype);
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break;
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case REDUCE_DOUBLE:
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bResult = runTest<double>(argc, argv, datatype);
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break;
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}
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bResult = runTest<double>(argc, argv, datatype);
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break;
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}
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printf(bResult ? "Test passed\n" : "Test failed!\n");
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printf(bResult ? "Test passed\n" : "Test failed!\n");
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}
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////////////////////////////////////////////////////////////////////////////////
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@@ -162,29 +164,30 @@ 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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#ifndef MIN
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@@ -198,40 +201,42 @@ unsigned int nextPow2(unsigned int x) {
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// n. For kernel 6, we observe the maximum specified number of blocks, because
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// each thread in that kernel can process a variable number of elements.
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////////////////////////////////////////////////////////////////////////////////
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void getNumBlocksAndThreads(int whichKernel, int n, int maxBlocks,
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int maxThreads, int &blocks, int &threads) {
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// get device capability, to avoid block/grid size exceed the upper bound
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cudaDeviceProp prop;
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int device;
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checkCudaErrors(cudaGetDevice(&device));
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checkCudaErrors(cudaGetDeviceProperties(&prop, device));
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void getNumBlocksAndThreads(int whichKernel, int n, int maxBlocks, int maxThreads, int &blocks, int &threads)
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{
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// get device capability, to avoid block/grid size exceed the upper bound
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cudaDeviceProp prop;
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int device;
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checkCudaErrors(cudaGetDevice(&device));
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checkCudaErrors(cudaGetDeviceProperties(&prop, device));
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if (whichKernel < 3) {
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threads = (n < maxThreads) ? nextPow2(n) : maxThreads;
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blocks = (n + threads - 1) / threads;
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} else {
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threads = (n < maxThreads * 2) ? nextPow2((n + 1) / 2) : maxThreads;
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blocks = (n + (threads * 2 - 1)) / (threads * 2);
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}
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if (whichKernel < 3) {
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threads = (n < maxThreads) ? nextPow2(n) : maxThreads;
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blocks = (n + threads - 1) / threads;
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}
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else {
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threads = (n < maxThreads * 2) ? nextPow2((n + 1) / 2) : maxThreads;
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blocks = (n + (threads * 2 - 1)) / (threads * 2);
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}
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if ((float)threads * blocks >
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(float)prop.maxGridSize[0] * prop.maxThreadsPerBlock) {
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printf("n is too large, please choose a smaller number!\n");
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}
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if ((float)threads * blocks > (float)prop.maxGridSize[0] * prop.maxThreadsPerBlock) {
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printf("n is too large, please choose a smaller number!\n");
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}
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if (blocks > prop.maxGridSize[0]) {
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printf(
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"Grid size <%d> exceeds the device capability <%d>, set block size as "
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"%d (original %d)\n",
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blocks, prop.maxGridSize[0], threads * 2, threads);
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if (blocks > prop.maxGridSize[0]) {
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printf("Grid size <%d> exceeds the device capability <%d>, set block size as "
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"%d (original %d)\n",
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blocks,
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prop.maxGridSize[0],
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threads * 2,
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threads);
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blocks /= 2;
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threads *= 2;
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}
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blocks /= 2;
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threads *= 2;
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}
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if (whichKernel >= 6) {
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blocks = MIN(maxBlocks, blocks);
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}
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if (whichKernel >= 6) {
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blocks = MIN(maxBlocks, blocks);
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}
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}
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////////////////////////////////////////////////////////////////////////////////
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@@ -239,85 +244,90 @@ void getNumBlocksAndThreads(int whichKernel, int n, int maxBlocks,
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// measures the average reduction time.
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////////////////////////////////////////////////////////////////////////////////
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template <class T>
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T benchmarkReduce(int n, int numThreads, int numBlocks, int maxThreads,
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int maxBlocks, int whichKernel, int testIterations,
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bool cpuFinalReduction, int cpuFinalThreshold,
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StopWatchInterface *timer, T *h_odata, T *d_idata,
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T *d_odata) {
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T gpu_result = 0;
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bool needReadBack = true;
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T 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 whichKernel,
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int testIterations,
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bool cpuFinalReduction,
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int cpuFinalThreshold,
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StopWatchInterface *timer,
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T *h_odata,
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T *d_idata,
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T *d_odata)
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{
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T gpu_result = 0;
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bool needReadBack = true;
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T *d_intermediateSums;
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checkCudaErrors(
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cudaMalloc((void **)&d_intermediateSums, sizeof(T) * numBlocks));
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T *d_intermediateSums;
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checkCudaErrors(cudaMalloc((void **)&d_intermediateSums, sizeof(T) * numBlocks));
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for (int i = 0; i < testIterations; ++i) {
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gpu_result = 0;
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for (int i = 0; i < testIterations; ++i) {
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gpu_result = 0;
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cudaDeviceSynchronize();
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sdkStartTimer(&timer);
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cudaDeviceSynchronize();
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sdkStartTimer(&timer);
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// execute the kernel
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reduce<T>(n, numThreads, numBlocks, whichKernel, d_idata, d_odata);
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// execute the kernel
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reduce<T>(n, numThreads, numBlocks, whichKernel, d_idata, d_odata);
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// check if kernel execution generated an error
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getLastCudaError("Kernel execution failed");
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// check if kernel execution generated an error
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getLastCudaError("Kernel execution failed");
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if (cpuFinalReduction) {
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// sum partial sums from each block on CPU
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// copy result from device to host
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checkCudaErrors(cudaMemcpy(h_odata, d_odata, numBlocks * sizeof(T),
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cudaMemcpyDeviceToHost));
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if (cpuFinalReduction) {
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// sum partial sums from each block on CPU
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// copy result from device to host
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checkCudaErrors(cudaMemcpy(h_odata, d_odata, numBlocks * sizeof(T), cudaMemcpyDeviceToHost));
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for (int i = 0; i < numBlocks; i++) {
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gpu_result += h_odata[i];
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}
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for (int i = 0; i < numBlocks; i++) {
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gpu_result += h_odata[i];
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}
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needReadBack = false;
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} else {
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// sum partial block sums on GPU
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int s = numBlocks;
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int kernel = whichKernel;
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while (s > cpuFinalThreshold) {
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int threads = 0, blocks = 0;
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getNumBlocksAndThreads(kernel, s, maxBlocks, maxThreads, blocks,
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threads);
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checkCudaErrors(cudaMemcpy(d_intermediateSums, d_odata, s * sizeof(T),
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cudaMemcpyDeviceToDevice));
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reduce<T>(s, threads, blocks, kernel, d_intermediateSums, d_odata);
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if (kernel < 3) {
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s = (s + threads - 1) / threads;
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} else {
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s = (s + (threads * 2 - 1)) / (threads * 2);
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needReadBack = false;
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}
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}
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else {
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// sum partial block sums on GPU
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int s = numBlocks;
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int kernel = whichKernel;
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if (s > 1) {
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// copy result from device to host
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checkCudaErrors(cudaMemcpy(h_odata, d_odata, s * sizeof(T),
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cudaMemcpyDeviceToHost));
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while (s > cpuFinalThreshold) {
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int threads = 0, blocks = 0;
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getNumBlocksAndThreads(kernel, s, maxBlocks, maxThreads, blocks, threads);
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checkCudaErrors(cudaMemcpy(d_intermediateSums, d_odata, s * sizeof(T), cudaMemcpyDeviceToDevice));
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reduce<T>(s, threads, blocks, kernel, d_intermediateSums, d_odata);
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for (int i = 0; i < s; i++) {
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gpu_result += h_odata[i];
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if (kernel < 3) {
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s = (s + threads - 1) / threads;
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}
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else {
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s = (s + (threads * 2 - 1)) / (threads * 2);
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}
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}
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if (s > 1) {
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// copy result from device to host
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checkCudaErrors(cudaMemcpy(h_odata, d_odata, s * sizeof(T), cudaMemcpyDeviceToHost));
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for (int i = 0; i < s; i++) {
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gpu_result += h_odata[i];
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}
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needReadBack = false;
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}
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}
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needReadBack = false;
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}
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cudaDeviceSynchronize();
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sdkStopTimer(&timer);
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}
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cudaDeviceSynchronize();
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sdkStopTimer(&timer);
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}
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if (needReadBack) {
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// copy final sum from device to host
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checkCudaErrors(
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cudaMemcpy(&gpu_result, d_odata, sizeof(T), cudaMemcpyDeviceToHost));
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}
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checkCudaErrors(cudaFree(d_intermediateSums));
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return gpu_result;
|
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if (needReadBack) {
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// copy final sum from device to host
|
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checkCudaErrors(cudaMemcpy(&gpu_result, d_odata, sizeof(T), cudaMemcpyDeviceToHost));
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}
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checkCudaErrors(cudaFree(d_intermediateSums));
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return gpu_result;
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}
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////////////////////////////////////////////////////////////////////////////////
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@@ -326,222 +336,91 @@ T benchmarkReduce(int n, int numThreads, int numBlocks, int maxThreads,
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// for generating a "shmoo" plot showing the performance for each kernel
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// variation over a wide range of input sizes.
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////////////////////////////////////////////////////////////////////////////////
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template <class T>
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void shmoo(int minN, int maxN, int maxThreads, int maxBlocks,
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ReduceType datatype) {
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// create random input data on CPU
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unsigned int bytes = maxN * sizeof(T);
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T *h_idata = (T *)malloc(bytes);
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|
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for (int i = 0; i < maxN; i++) {
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// Keep the numbers small so we don't get truncation error in the sum
|
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if (datatype == REDUCE_INT) {
|
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h_idata[i] = (T)(rand() & 0xFF);
|
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} else {
|
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h_idata[i] = (rand() & 0xFF) / (T)RAND_MAX;
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}
|
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}
|
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|
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int maxNumBlocks = MIN(maxN / maxThreads, MAX_BLOCK_DIM_SIZE);
|
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|
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// allocate mem for the result on host side
|
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T *h_odata = (T *)malloc(maxNumBlocks * sizeof(T));
|
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|
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// allocate device memory and data
|
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T *d_idata = NULL;
|
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T *d_odata = NULL;
|
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|
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checkCudaErrors(cudaMalloc((void **)&d_idata, bytes));
|
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checkCudaErrors(cudaMalloc((void **)&d_odata, maxNumBlocks * sizeof(T)));
|
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|
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// copy data directly to device memory
|
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checkCudaErrors(cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice));
|
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checkCudaErrors(cudaMemcpy(d_odata, h_idata, maxNumBlocks * sizeof(T),
|
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cudaMemcpyHostToDevice));
|
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|
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// warm-up
|
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for (int kernel = 0; kernel < 8; kernel++) {
|
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reduce<T>(maxN, maxThreads, maxNumBlocks, kernel, d_idata, d_odata);
|
||||
}
|
||||
|
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int testIterations = 100;
|
||||
|
||||
StopWatchInterface *timer = 0;
|
||||
sdkCreateTimer(&timer);
|
||||
|
||||
// print headers
|
||||
printf(
|
||||
"Time in milliseconds for various numbers of elements for each "
|
||||
"kernel\n\n\n");
|
||||
printf("Kernel");
|
||||
|
||||
for (int i = minN; i <= maxN; i *= 2) {
|
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printf(", %d", i);
|
||||
}
|
||||
|
||||
for (int kernel = 0; kernel < 8; kernel++) {
|
||||
printf("\n%d", kernel);
|
||||
|
||||
for (int i = minN; i <= maxN; i *= 2) {
|
||||
sdkResetTimer(&timer);
|
||||
int numBlocks = 0;
|
||||
int numThreads = 0;
|
||||
getNumBlocksAndThreads(kernel, i, maxBlocks, maxThreads, numBlocks,
|
||||
numThreads);
|
||||
|
||||
float reduceTime;
|
||||
|
||||
if (numBlocks <= MAX_BLOCK_DIM_SIZE) {
|
||||
benchmarkReduce(i, numThreads, numBlocks, maxThreads, maxBlocks, kernel,
|
||||
testIterations, false, 1, timer, h_odata, d_idata,
|
||||
d_odata);
|
||||
reduceTime = sdkGetAverageTimerValue(&timer);
|
||||
} else {
|
||||
reduceTime = -1.0;
|
||||
}
|
||||
|
||||
printf(", %.5f", reduceTime);
|
||||
}
|
||||
}
|
||||
|
||||
// cleanup
|
||||
sdkDeleteTimer(&timer);
|
||||
free(h_idata);
|
||||
free(h_odata);
|
||||
|
||||
checkCudaErrors(cudaFree(d_idata));
|
||||
checkCudaErrors(cudaFree(d_odata));
|
||||
}
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
// The main function which runs the reduction test.
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
template <class T>
|
||||
bool runTest(int argc, char **argv, ReduceType datatype) {
|
||||
int size = 1 << 24; // number of elements to reduce
|
||||
int maxThreads = 256; // number of threads per block
|
||||
int whichKernel = 7;
|
||||
int maxBlocks = 64;
|
||||
bool cpuFinalReduction = false;
|
||||
int cpuFinalThreshold = 1;
|
||||
|
||||
if (checkCmdLineFlag(argc, (const char **)argv, "n")) {
|
||||
size = getCmdLineArgumentInt(argc, (const char **)argv, "n");
|
||||
}
|
||||
|
||||
if (checkCmdLineFlag(argc, (const char **)argv, "threads")) {
|
||||
maxThreads = getCmdLineArgumentInt(argc, (const char **)argv, "threads");
|
||||
}
|
||||
|
||||
if (checkCmdLineFlag(argc, (const char **)argv, "kernel")) {
|
||||
whichKernel = getCmdLineArgumentInt(argc, (const char **)argv, "kernel");
|
||||
}
|
||||
|
||||
if (checkCmdLineFlag(argc, (const char **)argv, "maxblocks")) {
|
||||
maxBlocks = getCmdLineArgumentInt(argc, (const char **)argv, "maxblocks");
|
||||
}
|
||||
|
||||
printf("%d elements\n", size);
|
||||
printf("%d threads (max)\n", maxThreads);
|
||||
|
||||
cpuFinalReduction = checkCmdLineFlag(argc, (const char **)argv, "cpufinal");
|
||||
|
||||
if (checkCmdLineFlag(argc, (const char **)argv, "cputhresh")) {
|
||||
cpuFinalThreshold =
|
||||
getCmdLineArgumentInt(argc, (const char **)argv, "cputhresh");
|
||||
}
|
||||
|
||||
bool runShmoo = checkCmdLineFlag(argc, (const char **)argv, "shmoo");
|
||||
|
||||
if (runShmoo) {
|
||||
shmoo<T>(1, 33554432, maxThreads, maxBlocks, datatype);
|
||||
} else {
|
||||
template <class T> void shmoo(int minN, int maxN, int maxThreads, int maxBlocks, ReduceType datatype)
|
||||
{
|
||||
// create random input data on CPU
|
||||
unsigned int bytes = size * sizeof(T);
|
||||
unsigned int bytes = maxN * sizeof(T);
|
||||
|
||||
T *h_idata = (T *)malloc(bytes);
|
||||
|
||||
for (int i = 0; i < size; i++) {
|
||||
// Keep the numbers small so we don't get truncation error in the sum
|
||||
if (datatype == REDUCE_INT) {
|
||||
h_idata[i] = (T)(rand() & 0xFF);
|
||||
} else {
|
||||
h_idata[i] = (rand() & 0xFF) / (T)RAND_MAX;
|
||||
}
|
||||
for (int i = 0; i < maxN; i++) {
|
||||
// Keep the numbers small so we don't get truncation error in the sum
|
||||
if (datatype == REDUCE_INT) {
|
||||
h_idata[i] = (T)(rand() & 0xFF);
|
||||
}
|
||||
else {
|
||||
h_idata[i] = (rand() & 0xFF) / (T)RAND_MAX;
|
||||
}
|
||||
}
|
||||
|
||||
int numBlocks = 0;
|
||||
int numThreads = 0;
|
||||
getNumBlocksAndThreads(whichKernel, size, maxBlocks, maxThreads, numBlocks,
|
||||
numThreads);
|
||||
|
||||
if (numBlocks == 1) {
|
||||
cpuFinalThreshold = 1;
|
||||
}
|
||||
int maxNumBlocks = MIN(maxN / maxThreads, MAX_BLOCK_DIM_SIZE);
|
||||
|
||||
// allocate mem for the result on host side
|
||||
T *h_odata = (T *)malloc(numBlocks * sizeof(T));
|
||||
|
||||
printf("%d blocks\n\n", numBlocks);
|
||||
T *h_odata = (T *)malloc(maxNumBlocks * sizeof(T));
|
||||
|
||||
// allocate device memory and data
|
||||
T *d_idata = NULL;
|
||||
T *d_odata = NULL;
|
||||
|
||||
checkCudaErrors(cudaMalloc((void **)&d_idata, bytes));
|
||||
checkCudaErrors(cudaMalloc((void **)&d_odata, numBlocks * sizeof(T)));
|
||||
checkCudaErrors(cudaMalloc((void **)&d_odata, maxNumBlocks * sizeof(T)));
|
||||
|
||||
// copy data directly to device memory
|
||||
checkCudaErrors(
|
||||
cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice));
|
||||
checkCudaErrors(cudaMemcpy(d_odata, h_idata, numBlocks * sizeof(T),
|
||||
cudaMemcpyHostToDevice));
|
||||
checkCudaErrors(cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice));
|
||||
checkCudaErrors(cudaMemcpy(d_odata, h_idata, maxNumBlocks * sizeof(T), cudaMemcpyHostToDevice));
|
||||
|
||||
// warm-up
|
||||
reduce<T>(size, numThreads, numBlocks, whichKernel, d_idata, d_odata);
|
||||
for (int kernel = 0; kernel < 8; kernel++) {
|
||||
reduce<T>(maxN, maxThreads, maxNumBlocks, kernel, d_idata, d_odata);
|
||||
}
|
||||
|
||||
int testIterations = 100;
|
||||
|
||||
StopWatchInterface *timer = 0;
|
||||
sdkCreateTimer(&timer);
|
||||
|
||||
T gpu_result = 0;
|
||||
// print headers
|
||||
printf("Time in milliseconds for various numbers of elements for each "
|
||||
"kernel\n\n\n");
|
||||
printf("Kernel");
|
||||
|
||||
gpu_result =
|
||||
benchmarkReduce<T>(size, numThreads, numBlocks, maxThreads, maxBlocks,
|
||||
whichKernel, testIterations, cpuFinalReduction,
|
||||
cpuFinalThreshold, timer, h_odata, d_idata, d_odata);
|
||||
for (int i = minN; i <= maxN; i *= 2) {
|
||||
printf(", %d", i);
|
||||
}
|
||||
|
||||
double reduceTime = sdkGetAverageTimerValue(&timer) * 1e-3;
|
||||
printf(
|
||||
"Reduction, Throughput = %.4f GB/s, Time = %.5f s, Size = %u Elements, "
|
||||
"NumDevsUsed = %d, Workgroup = %u\n",
|
||||
1.0e-9 * ((double)bytes) / reduceTime, reduceTime, size, 1, numThreads);
|
||||
for (int kernel = 0; kernel < 8; kernel++) {
|
||||
printf("\n%d", kernel);
|
||||
|
||||
// compute reference solution
|
||||
T cpu_result = reduceCPU<T>(h_idata, size);
|
||||
for (int i = minN; i <= maxN; i *= 2) {
|
||||
sdkResetTimer(&timer);
|
||||
int numBlocks = 0;
|
||||
int numThreads = 0;
|
||||
getNumBlocksAndThreads(kernel, i, maxBlocks, maxThreads, numBlocks, numThreads);
|
||||
|
||||
int precision = 0;
|
||||
double threshold = 0;
|
||||
double diff = 0;
|
||||
float reduceTime;
|
||||
|
||||
if (datatype == REDUCE_INT) {
|
||||
printf("\nGPU result = %d\n", (int)gpu_result);
|
||||
printf("CPU result = %d\n\n", (int)cpu_result);
|
||||
} else {
|
||||
if (datatype == REDUCE_FLOAT) {
|
||||
precision = 8;
|
||||
threshold = 1e-8 * size;
|
||||
} else {
|
||||
precision = 12;
|
||||
threshold = 1e-12 * size;
|
||||
}
|
||||
if (numBlocks <= MAX_BLOCK_DIM_SIZE) {
|
||||
benchmarkReduce(i,
|
||||
numThreads,
|
||||
numBlocks,
|
||||
maxThreads,
|
||||
maxBlocks,
|
||||
kernel,
|
||||
testIterations,
|
||||
false,
|
||||
1,
|
||||
timer,
|
||||
h_odata,
|
||||
d_idata,
|
||||
d_odata);
|
||||
reduceTime = sdkGetAverageTimerValue(&timer);
|
||||
}
|
||||
else {
|
||||
reduceTime = -1.0;
|
||||
}
|
||||
|
||||
printf("\nGPU result = %.*f\n", precision, (double)gpu_result);
|
||||
printf("CPU result = %.*f\n\n", precision, (double)cpu_result);
|
||||
|
||||
diff = fabs((double)gpu_result - (double)cpu_result);
|
||||
printf(", %.5f", reduceTime);
|
||||
}
|
||||
}
|
||||
|
||||
// cleanup
|
||||
@@ -551,13 +430,165 @@ bool runTest(int argc, char **argv, ReduceType datatype) {
|
||||
|
||||
checkCudaErrors(cudaFree(d_idata));
|
||||
checkCudaErrors(cudaFree(d_odata));
|
||||
|
||||
if (datatype == REDUCE_INT) {
|
||||
return (gpu_result == cpu_result);
|
||||
} else {
|
||||
return (diff < threshold);
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
// The main function which runs the reduction test.
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
template <class T> bool runTest(int argc, char **argv, ReduceType datatype)
|
||||
{
|
||||
int size = 1 << 24; // number of elements to reduce
|
||||
int maxThreads = 256; // number of threads per block
|
||||
int whichKernel = 7;
|
||||
int maxBlocks = 64;
|
||||
bool cpuFinalReduction = false;
|
||||
int cpuFinalThreshold = 1;
|
||||
|
||||
if (checkCmdLineFlag(argc, (const char **)argv, "n")) {
|
||||
size = getCmdLineArgumentInt(argc, (const char **)argv, "n");
|
||||
}
|
||||
|
||||
if (checkCmdLineFlag(argc, (const char **)argv, "threads")) {
|
||||
maxThreads = getCmdLineArgumentInt(argc, (const char **)argv, "threads");
|
||||
}
|
||||
|
||||
if (checkCmdLineFlag(argc, (const char **)argv, "kernel")) {
|
||||
whichKernel = getCmdLineArgumentInt(argc, (const char **)argv, "kernel");
|
||||
}
|
||||
|
||||
if (checkCmdLineFlag(argc, (const char **)argv, "maxblocks")) {
|
||||
maxBlocks = getCmdLineArgumentInt(argc, (const char **)argv, "maxblocks");
|
||||
}
|
||||
|
||||
printf("%d elements\n", size);
|
||||
printf("%d threads (max)\n", maxThreads);
|
||||
|
||||
cpuFinalReduction = checkCmdLineFlag(argc, (const char **)argv, "cpufinal");
|
||||
|
||||
if (checkCmdLineFlag(argc, (const char **)argv, "cputhresh")) {
|
||||
cpuFinalThreshold = getCmdLineArgumentInt(argc, (const char **)argv, "cputhresh");
|
||||
}
|
||||
|
||||
bool runShmoo = checkCmdLineFlag(argc, (const char **)argv, "shmoo");
|
||||
|
||||
if (runShmoo) {
|
||||
shmoo<T>(1, 33554432, maxThreads, maxBlocks, datatype);
|
||||
}
|
||||
else {
|
||||
// create random input data on CPU
|
||||
unsigned int bytes = size * sizeof(T);
|
||||
|
||||
T *h_idata = (T *)malloc(bytes);
|
||||
|
||||
for (int i = 0; i < size; i++) {
|
||||
// Keep the numbers small so we don't get truncation error in the sum
|
||||
if (datatype == REDUCE_INT) {
|
||||
h_idata[i] = (T)(rand() & 0xFF);
|
||||
}
|
||||
else {
|
||||
h_idata[i] = (rand() & 0xFF) / (T)RAND_MAX;
|
||||
}
|
||||
}
|
||||
|
||||
int numBlocks = 0;
|
||||
int numThreads = 0;
|
||||
getNumBlocksAndThreads(whichKernel, size, maxBlocks, maxThreads, numBlocks, numThreads);
|
||||
|
||||
if (numBlocks == 1) {
|
||||
cpuFinalThreshold = 1;
|
||||
}
|
||||
|
||||
// allocate mem for the result on host side
|
||||
T *h_odata = (T *)malloc(numBlocks * sizeof(T));
|
||||
|
||||
printf("%d blocks\n\n", numBlocks);
|
||||
|
||||
// allocate device memory and data
|
||||
T *d_idata = NULL;
|
||||
T *d_odata = NULL;
|
||||
|
||||
checkCudaErrors(cudaMalloc((void **)&d_idata, bytes));
|
||||
checkCudaErrors(cudaMalloc((void **)&d_odata, numBlocks * sizeof(T)));
|
||||
|
||||
// copy data directly to device memory
|
||||
checkCudaErrors(cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice));
|
||||
checkCudaErrors(cudaMemcpy(d_odata, h_idata, numBlocks * sizeof(T), cudaMemcpyHostToDevice));
|
||||
|
||||
// warm-up
|
||||
reduce<T>(size, numThreads, numBlocks, whichKernel, d_idata, d_odata);
|
||||
|
||||
int testIterations = 100;
|
||||
|
||||
StopWatchInterface *timer = 0;
|
||||
sdkCreateTimer(&timer);
|
||||
|
||||
T gpu_result = 0;
|
||||
|
||||
gpu_result = benchmarkReduce<T>(size,
|
||||
numThreads,
|
||||
numBlocks,
|
||||
maxThreads,
|
||||
maxBlocks,
|
||||
whichKernel,
|
||||
testIterations,
|
||||
cpuFinalReduction,
|
||||
cpuFinalThreshold,
|
||||
timer,
|
||||
h_odata,
|
||||
d_idata,
|
||||
d_odata);
|
||||
|
||||
double reduceTime = sdkGetAverageTimerValue(&timer) * 1e-3;
|
||||
printf("Reduction, Throughput = %.4f GB/s, Time = %.5f s, Size = %u Elements, "
|
||||
"NumDevsUsed = %d, Workgroup = %u\n",
|
||||
1.0e-9 * ((double)bytes) / reduceTime,
|
||||
reduceTime,
|
||||
size,
|
||||
1,
|
||||
numThreads);
|
||||
|
||||
// compute reference solution
|
||||
T cpu_result = reduceCPU<T>(h_idata, size);
|
||||
|
||||
int precision = 0;
|
||||
double threshold = 0;
|
||||
double diff = 0;
|
||||
|
||||
if (datatype == REDUCE_INT) {
|
||||
printf("\nGPU result = %d\n", (int)gpu_result);
|
||||
printf("CPU result = %d\n\n", (int)cpu_result);
|
||||
}
|
||||
else {
|
||||
if (datatype == REDUCE_FLOAT) {
|
||||
precision = 8;
|
||||
threshold = 1e-8 * size;
|
||||
}
|
||||
else {
|
||||
precision = 12;
|
||||
threshold = 1e-12 * size;
|
||||
}
|
||||
|
||||
printf("\nGPU result = %.*f\n", precision, (double)gpu_result);
|
||||
printf("CPU result = %.*f\n\n", precision, (double)cpu_result);
|
||||
|
||||
diff = fabs((double)gpu_result - (double)cpu_result);
|
||||
}
|
||||
|
||||
// cleanup
|
||||
sdkDeleteTimer(&timer);
|
||||
free(h_idata);
|
||||
free(h_odata);
|
||||
|
||||
checkCudaErrors(cudaFree(d_idata));
|
||||
checkCudaErrors(cudaFree(d_odata));
|
||||
|
||||
if (datatype == REDUCE_INT) {
|
||||
return (gpu_result == cpu_result);
|
||||
}
|
||||
else {
|
||||
return (diff < threshold);
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
@@ -29,8 +29,6 @@
|
||||
#ifndef __REDUCTION_H__
|
||||
#define __REDUCTION_H__
|
||||
|
||||
template <class T>
|
||||
void reduce(int size, int threads, int blocks,
|
||||
int whichKernel, T *d_idata, T *d_odata);
|
||||
template <class T> void reduce(int size, int threads, int blocks, int whichKernel, T *d_idata, T *d_odata);
|
||||
|
||||
#endif
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user