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cuda-samples/Samples/2_Concepts_and_Techniques/reduction/reduction.cpp

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/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
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* modification, are permitted provided that the following conditions
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* notice, this list of conditions and the following disclaimer.
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* notice, this list of conditions and the following disclaimer in the
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* * 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
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/*
Parallel reduction
This sample shows how to perform a reduction operation on an array of values
to produce a single value.
Reductions are a very common computation in parallel algorithms. Any time
an array of values needs to be reduced to a single value using a binary
associative operator, a reduction can be used. Example applications include
statistics computations such as mean and standard deviation, and image
processing applications such as finding the total luminance of an
image.
This code performs sum reductions, but any associative operator such as
min() or max() could also be used.
It assumes the input size is a power of 2.
COMMAND LINE ARGUMENTS
"--shmoo": Test performance for 1 to 32M elements with each of the 7
different kernels
"--n=<N>": Specify the number of elements to reduce (default
1048576)
"--threads=<N>": Specify the number of threads per block (default 128)
"--kernel=<N>": Specify which kernel to run (0-6, default 6)
"--maxblocks=<N>": Specify the maximum number of thread blocks to launch
(kernel 6 only, default 64)
"--cpufinal": Read back the per-block results and do final sum of block
sums on CPU (default false)
"--cputhresh=<N>": The threshold of number of blocks sums below which to
perform a CPU final reduction (default 1)
"-type=<T>": The datatype for the reduction, where T is "int",
"float", or "double" (default int)
*/
// CUDA Runtime
#include <cuda_runtime.h>
// Utilities and system includes
#include <algorithm>
#include <helper_cuda.h>
#include <helper_functions.h>
// includes, project
#include "reduction.h"
enum ReduceType { REDUCE_INT, REDUCE_FLOAT, REDUCE_DOUBLE };
////////////////////////////////////////////////////////////////////////////////
// declaration, forward
template <class T> bool runTest(int argc, char **argv, ReduceType datatype);
#define MAX_BLOCK_DIM_SIZE 65535
#ifdef WIN32
#define strcasecmp strcmpi
#endif
extern "C" bool isPow2(unsigned int x) { return ((x & (x - 1)) == 0); }
const char *getReduceTypeString(const ReduceType type)
{
switch (type) {
case REDUCE_INT:
return "int";
case REDUCE_FLOAT:
return "float";
case REDUCE_DOUBLE:
return "double";
default:
return "unknown";
}
}
////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char **argv)
{
printf("%s Starting...\n\n", argv[0]);
char *typeInput = 0;
getCmdLineArgumentString(argc, (const char **)argv, "type", &typeInput);
ReduceType datatype = REDUCE_INT;
if (0 != typeInput) {
if (!strcasecmp(typeInput, "float")) {
datatype = REDUCE_FLOAT;
}
else if (!strcasecmp(typeInput, "double")) {
datatype = REDUCE_DOUBLE;
}
else if (strcasecmp(typeInput, "int")) {
printf("Type %s is not recognized. Using default type int.\n\n", typeInput);
}
}
cudaDeviceProp deviceProp;
int dev;
dev = findCudaDevice(argc, (const char **)argv);
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, dev));
printf("Using Device %d: %s\n\n", dev, deviceProp.name);
checkCudaErrors(cudaSetDevice(dev));
printf("Reducing array of type %s\n\n", getReduceTypeString(datatype));
bool bResult = false;
switch (datatype) {
default:
case REDUCE_INT:
bResult = runTest<int>(argc, argv, datatype);
break;
case REDUCE_FLOAT:
bResult = runTest<float>(argc, argv, datatype);
break;
case REDUCE_DOUBLE:
bResult = runTest<double>(argc, argv, datatype);
break;
}
printf(bResult ? "Test passed\n" : "Test failed!\n");
}
////////////////////////////////////////////////////////////////////////////////
//! Compute sum reduction on CPU
//! We use Kahan summation for an accurate sum of large arrays.
//! http://en.wikipedia.org/wiki/Kahan_summation_algorithm
//!
//! @param data pointer to input data
//! @param size number of input data elements
////////////////////////////////////////////////////////////////////////////////
template <class T> T reduceCPU(T *data, int size)
{
T sum = data[0];
T c = (T)0.0;
for (int i = 1; i < size; i++) {
T y = data[i] - c;
T t = sum + y;
c = (t - sum) - y;
sum = t;
}
return sum;
}
unsigned int nextPow2(unsigned int x)
{
--x;
x |= x >> 1;
x |= x >> 2;
x |= x >> 4;
x |= x >> 8;
x |= x >> 16;
return ++x;
}
#ifndef MIN
#define MIN(x, y) ((x < y) ? x : y)
#endif
////////////////////////////////////////////////////////////////////////////////
// Compute the number of threads and blocks to use for the given reduction
// kernel For the kernels >= 3, we set threads / block to the minimum of
// maxThreads and n/2. For kernels < 3, we set to the minimum of maxThreads and
// n. For kernel 6, we observe the maximum specified number of blocks, because
// each thread in that kernel can process a variable number of elements.
////////////////////////////////////////////////////////////////////////////////
void getNumBlocksAndThreads(int whichKernel, int n, int maxBlocks, int maxThreads, int &blocks, int &threads)
{
// get device capability, to avoid block/grid size exceed the upper bound
cudaDeviceProp prop;
int device;
checkCudaErrors(cudaGetDevice(&device));
checkCudaErrors(cudaGetDeviceProperties(&prop, device));
if (whichKernel < 3) {
threads = (n < maxThreads) ? nextPow2(n) : maxThreads;
blocks = (n + threads - 1) / threads;
}
else {
threads = (n < maxThreads * 2) ? nextPow2((n + 1) / 2) : maxThreads;
blocks = (n + (threads * 2 - 1)) / (threads * 2);
}
if ((float)threads * blocks > (float)prop.maxGridSize[0] * prop.maxThreadsPerBlock) {
printf("n is too large, please choose a smaller number!\n");
}
if (blocks > prop.maxGridSize[0]) {
printf("Grid size <%d> exceeds the device capability <%d>, set block size as "
"%d (original %d)\n",
blocks,
prop.maxGridSize[0],
threads * 2,
threads);
blocks /= 2;
threads *= 2;
}
if (whichKernel >= 6) {
blocks = MIN(maxBlocks, blocks);
}
}
////////////////////////////////////////////////////////////////////////////////
// This function performs a reduction of the input data multiple times and
// measures the average reduction time.
////////////////////////////////////////////////////////////////////////////////
template <class T>
T benchmarkReduce(int n,
int numThreads,
int numBlocks,
int maxThreads,
int maxBlocks,
int whichKernel,
int testIterations,
bool cpuFinalReduction,
int cpuFinalThreshold,
StopWatchInterface *timer,
T *h_odata,
T *d_idata,
T *d_odata)
{
T gpu_result = 0;
bool needReadBack = true;
T *d_intermediateSums;
checkCudaErrors(cudaMalloc((void **)&d_intermediateSums, sizeof(T) * numBlocks));
for (int i = 0; i < testIterations; ++i) {
gpu_result = 0;
cudaDeviceSynchronize();
sdkStartTimer(&timer);
// execute the kernel
reduce<T>(n, numThreads, numBlocks, whichKernel, d_idata, d_odata);
// check if kernel execution generated an error
getLastCudaError("Kernel execution failed");
if (cpuFinalReduction) {
// sum partial sums from each block on CPU
// copy result from device to host
checkCudaErrors(cudaMemcpy(h_odata, d_odata, numBlocks * sizeof(T), cudaMemcpyDeviceToHost));
for (int i = 0; i < numBlocks; i++) {
gpu_result += h_odata[i];
}
needReadBack = false;
}
else {
// sum partial block sums on GPU
int s = numBlocks;
int kernel = whichKernel;
while (s > cpuFinalThreshold) {
int threads = 0, blocks = 0;
getNumBlocksAndThreads(kernel, s, maxBlocks, maxThreads, blocks, threads);
checkCudaErrors(cudaMemcpy(d_intermediateSums, d_odata, s * sizeof(T), cudaMemcpyDeviceToDevice));
reduce<T>(s, threads, blocks, kernel, d_intermediateSums, d_odata);
if (kernel < 3) {
s = (s + threads - 1) / threads;
}
else {
s = (s + (threads * 2 - 1)) / (threads * 2);
}
}
if (s > 1) {
// copy result from device to host
checkCudaErrors(cudaMemcpy(h_odata, d_odata, s * sizeof(T), cudaMemcpyDeviceToHost));
for (int i = 0; i < s; i++) {
gpu_result += h_odata[i];
}
needReadBack = false;
}
}
cudaDeviceSynchronize();
sdkStopTimer(&timer);
}
if (needReadBack) {
// copy final sum from device to host
checkCudaErrors(cudaMemcpy(&gpu_result, d_odata, sizeof(T), cudaMemcpyDeviceToHost));
}
checkCudaErrors(cudaFree(d_intermediateSums));
return gpu_result;
}
////////////////////////////////////////////////////////////////////////////////
// This function calls benchmarkReduce multiple times for a range of array sizes
// and prints a report in CSV (comma-separated value) format that can be used
// for generating a "shmoo" plot showing the performance for each kernel
// variation over a wide range of input sizes.
////////////////////////////////////////////////////////////////////////////////
template <class T> void shmoo(int minN, int maxN, int maxThreads, int maxBlocks, ReduceType datatype)
{
// create random input data on CPU
unsigned int bytes = maxN * sizeof(T);
T *h_idata = (T *)malloc(bytes);
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 maxNumBlocks = MIN(maxN / maxThreads, MAX_BLOCK_DIM_SIZE);
// allocate mem for the result on host side
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, maxNumBlocks * sizeof(T)));
// copy data directly to device memory
checkCudaErrors(cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_odata, h_idata, maxNumBlocks * sizeof(T), cudaMemcpyHostToDevice));
// warm-up
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);
// 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) {
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 {
// 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;
}
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