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Apply consistent code formatting across the repo. Add clang-format and pre-commit hooks.

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Rob Armstrong
2025-03-27 10:30:07 -07:00
parent 2cd58fbc9a
commit ceab6e8bcc
782 changed files with 107230 additions and 106548 deletions
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424
Samples/3_CUDA_Features/warpAggregatedAtomicsCG/warpAggregatedAtomicsCG.cu
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@@ -27,301 +27,287 @@
#include <stdio.h>
// includes, project
#include <cooperative_groups.h>
#include <cooperative_groups/reduce.h>
#include <cuda_runtime.h>
#include <helper_cuda.h>
#include <helper_functions.h>
#include <cuda_runtime.h>
#include <cooperative_groups.h>
#include <cooperative_groups/reduce.h>
namespace cg = cooperative_groups;
#define NUM_ELEMS 10000000
#define NUM_ELEMS 10000000
#define NUM_THREADS_PER_BLOCK 512
// warp-aggregated atomic increment
__device__ int atomicAggInc(int *counter) {
cg::coalesced_group active = cg::coalesced_threads();
__device__ int atomicAggInc(int *counter)
{
cg::coalesced_group active = cg::coalesced_threads();
// leader does the update
int res = 0;
if (active.thread_rank() == 0) {
res = atomicAdd(counter, active.size());
}
// leader does the update
int res = 0;
if (active.thread_rank() == 0) {
res = atomicAdd(counter, active.size());
}
// broadcast result
res = active.shfl(res, 0);
// broadcast result
res = active.shfl(res, 0);
// each thread computes its own value
return res + active.thread_rank();
// each thread computes its own value
return res + active.thread_rank();
}
__global__ void filter_arr(int *dst, int *nres, const int *src, int n) {
int id = threadIdx.x + blockIdx.x * blockDim.x;
__global__ void filter_arr(int *dst, int *nres, const int *src, int n)
{
int id = threadIdx.x + blockIdx.x * blockDim.x;
for (int i = id; i < n; i += gridDim.x * blockDim.x) {
if (src[i] > 0) dst[atomicAggInc(nres)] = src[i];
}
for (int i = id; i < n; i += gridDim.x * blockDim.x) {
if (src[i] > 0)
dst[atomicAggInc(nres)] = src[i];
}
}
// warp-aggregated atomic multi bucket increment
#if __CUDA_ARCH__ >= 700
__device__ int atomicAggIncMulti(const int bucket, int *counter)
{
cg::coalesced_group active = cg::coalesced_threads();
// group all threads with same bucket value.
auto labeledGroup = cg::labeled_partition(active, bucket);
cg::coalesced_group active = cg::coalesced_threads();
// group all threads with same bucket value.
auto labeledGroup = cg::labeled_partition(active, bucket);
int res = 0;
if (labeledGroup.thread_rank() == 0)
{
res = atomicAdd(&counter[bucket], labeledGroup.size());
}
int res = 0;
if (labeledGroup.thread_rank() == 0) {
res = atomicAdd(&counter[bucket], labeledGroup.size());
}
// broadcast result
res = labeledGroup.shfl(res, 0);
// broadcast result
res = labeledGroup.shfl(res, 0);
// each thread computes its own value
return res + labeledGroup.thread_rank();
// each thread computes its own value
return res + labeledGroup.thread_rank();
}
#endif
// Places individual value indices into its corresponding buckets.
__global__ void mapToBuckets(const int *srcArr, int *indicesBuckets, int *bucketCounters, const int srcSize, const int numOfBuckets)
__global__ void
mapToBuckets(const int *srcArr, int *indicesBuckets, int *bucketCounters, const int srcSize, const int numOfBuckets)
{
#if __CUDA_ARCH__ >= 700
cg::grid_group grid = cg::this_grid();
cg::grid_group grid = cg::this_grid();
for (int i=grid.thread_rank(); i < srcSize; i += grid.size())
{
const int bucket = srcArr[i];
if (bucket < numOfBuckets)
{
indicesBuckets[atomicAggIncMulti(bucket, bucketCounters)] = i;
for (int i = grid.thread_rank(); i < srcSize; i += grid.size()) {
const int bucket = srcArr[i];
if (bucket < numOfBuckets) {
indicesBuckets[atomicAggIncMulti(bucket, bucketCounters)] = i;
}
}
}
#endif
}
int mapIndicesToBuckets(int *h_srcArr, int *d_srcArr, int numOfBuckets)
{
int *d_indicesBuckets, *d_bucketCounters;
int *cpuBucketCounters = new int[numOfBuckets];
int *h_bucketCounters = new int[numOfBuckets];
int *d_indicesBuckets, *d_bucketCounters;
int *cpuBucketCounters = new int[numOfBuckets];
int *h_bucketCounters = new int[numOfBuckets];
memset(cpuBucketCounters, 0, sizeof(int)*numOfBuckets);
// Initialize each bucket counters.
for (int i = 0; i < numOfBuckets; i++)
{
h_bucketCounters[i] = i*NUM_ELEMS;
}
checkCudaErrors(cudaMalloc(&d_indicesBuckets, sizeof(int) * NUM_ELEMS * numOfBuckets));
checkCudaErrors(cudaMalloc(&d_bucketCounters, sizeof(int) * numOfBuckets));
checkCudaErrors(cudaMemcpy(d_bucketCounters, h_bucketCounters, sizeof(int)*numOfBuckets, cudaMemcpyHostToDevice));
dim3 dimBlock(NUM_THREADS_PER_BLOCK, 1, 1);
dim3 dimGrid((NUM_ELEMS / NUM_THREADS_PER_BLOCK), 1, 1);
mapToBuckets<<<dimGrid, dimBlock>>>(d_srcArr, d_indicesBuckets, d_bucketCounters, NUM_ELEMS, numOfBuckets);
checkCudaErrors(cudaMemcpy(h_bucketCounters, d_bucketCounters, sizeof(int)*numOfBuckets, cudaMemcpyDeviceToHost));
for (int i=0; i < NUM_ELEMS; i++)
{
cpuBucketCounters[h_srcArr[i]]++;
}
bool allMatch = true;
int finalElems = 0;
for (int i=0; i < numOfBuckets; i++)
{
finalElems += (h_bucketCounters[i] - i*NUM_ELEMS);
if (cpuBucketCounters[i] != (h_bucketCounters[i] - i*NUM_ELEMS))
{
allMatch = false;
break;
memset(cpuBucketCounters, 0, sizeof(int) * numOfBuckets);
// Initialize each bucket counters.
for (int i = 0; i < numOfBuckets; i++) {
h_bucketCounters[i] = i * NUM_ELEMS;
}
}
if (!allMatch && finalElems != NUM_ELEMS)
{
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
checkCudaErrors(cudaMalloc(&d_indicesBuckets, sizeof(int) * NUM_ELEMS * numOfBuckets));
checkCudaErrors(cudaMalloc(&d_bucketCounters, sizeof(int) * numOfBuckets));
checkCudaErrors(cudaMemcpy(d_bucketCounters, h_bucketCounters, sizeof(int) * numOfBuckets, cudaMemcpyHostToDevice));
dim3 dimBlock(NUM_THREADS_PER_BLOCK, 1, 1);
dim3 dimGrid((NUM_ELEMS / NUM_THREADS_PER_BLOCK), 1, 1);
mapToBuckets<<<dimGrid, dimBlock>>>(d_srcArr, d_indicesBuckets, d_bucketCounters, NUM_ELEMS, numOfBuckets);
checkCudaErrors(cudaMemcpy(h_bucketCounters, d_bucketCounters, sizeof(int) * numOfBuckets, cudaMemcpyDeviceToHost));
for (int i = 0; i < NUM_ELEMS; i++) {
cpuBucketCounters[h_srcArr[i]]++;
}
bool allMatch = true;
int finalElems = 0;
for (int i = 0; i < numOfBuckets; i++) {
finalElems += (h_bucketCounters[i] - i * NUM_ELEMS);
if (cpuBucketCounters[i] != (h_bucketCounters[i] - i * NUM_ELEMS)) {
allMatch = false;
break;
}
}
if (!allMatch && finalElems != NUM_ELEMS) {
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
// Warp-aggregated atomic Max in multi bucket
#if __CUDA_ARCH__ >= 700
__device__ void atomicAggMaxMulti(const int bucket, int *counter, const int valueForMax)
{
cg::coalesced_group active = cg::coalesced_threads();
// group all threads with same bucket value.
auto labeledGroup = cg::labeled_partition(active, bucket);
cg::coalesced_group active = cg::coalesced_threads();
// group all threads with same bucket value.
auto labeledGroup = cg::labeled_partition(active, bucket);
const int maxValueInGroup = cg::reduce(labeledGroup, valueForMax, cg::greater<int>());
const int maxValueInGroup = cg::reduce(labeledGroup, valueForMax, cg::greater<int>());
if (labeledGroup.thread_rank() == 0)
{
atomicMax(&counter[bucket], maxValueInGroup);
}
if (labeledGroup.thread_rank() == 0) {
atomicMax(&counter[bucket], maxValueInGroup);
}
}
#endif
// Performs max calculation in each buckets.
__global__ void calculateMaxInEachBuckets(const int *srcArr, const int *valueInBuckets, int *bucketsMax, const int srcSize, const int numOfBuckets)
__global__ void calculateMaxInEachBuckets(const int *srcArr,
const int *valueInBuckets,
int *bucketsMax,
const int srcSize,
const int numOfBuckets)
{
#if __CUDA_ARCH__ >= 700
cg::grid_group grid = cg::this_grid();
cg::grid_group grid = cg::this_grid();
for (int i=grid.thread_rank(); i < srcSize; i += grid.size())
{
const int bucket = srcArr[i];
if (bucket < numOfBuckets)
{
atomicAggMaxMulti(bucket, bucketsMax, valueInBuckets[i]);
for (int i = grid.thread_rank(); i < srcSize; i += grid.size()) {
const int bucket = srcArr[i];
if (bucket < numOfBuckets) {
atomicAggMaxMulti(bucket, bucketsMax, valueInBuckets[i]);
}
}
}
#endif
}
int calculateMaxInBuckets(int *h_srcArr, int *d_srcArr, int numOfBuckets)
{
int *d_valueInBuckets, *d_bucketsMax;
int *h_valueInBuckets = new int[NUM_ELEMS];
int *cpuBucketsMax = new int[numOfBuckets];
int *h_bucketsMax = new int[numOfBuckets];
int *d_valueInBuckets, *d_bucketsMax;
int *h_valueInBuckets = new int[NUM_ELEMS];
int *cpuBucketsMax = new int[numOfBuckets];
int *h_bucketsMax = new int[numOfBuckets];
memset(cpuBucketsMax, 0, sizeof(int) * numOfBuckets);
memset(cpuBucketsMax, 0, sizeof(int) * numOfBuckets);
// Here we create values which is assumed to correspond to each
// buckets of srcArr at same array index.
for (int i=0; i < NUM_ELEMS; i++)
{
h_valueInBuckets[i] = rand();
}
checkCudaErrors(cudaMalloc(&d_valueInBuckets, sizeof(int) * NUM_ELEMS));
checkCudaErrors(cudaMalloc(&d_bucketsMax, sizeof(int) * numOfBuckets));
checkCudaErrors(cudaMemset(d_bucketsMax, 0, sizeof(int) * numOfBuckets));
checkCudaErrors(cudaMemcpy(d_valueInBuckets, h_valueInBuckets, sizeof(int) * NUM_ELEMS, cudaMemcpyHostToDevice));
dim3 dimBlock(NUM_THREADS_PER_BLOCK, 1, 1);
dim3 dimGrid((NUM_ELEMS / NUM_THREADS_PER_BLOCK), 1, 1);
calculateMaxInEachBuckets<<<dimGrid, dimBlock>>>(d_srcArr, d_valueInBuckets, d_bucketsMax, NUM_ELEMS, numOfBuckets);
checkCudaErrors(cudaMemcpy(h_bucketsMax, d_bucketsMax, sizeof(int) * numOfBuckets, cudaMemcpyDeviceToHost));
for (int i = 0; i < NUM_ELEMS; i++)
{
if (cpuBucketsMax[h_srcArr[i]] < h_valueInBuckets[i])
{
cpuBucketsMax[h_srcArr[i]] = h_valueInBuckets[i];
// Here we create values which is assumed to correspond to each
// buckets of srcArr at same array index.
for (int i = 0; i < NUM_ELEMS; i++) {
h_valueInBuckets[i] = rand();
}
}
bool allMatch = true;
int finalElems = 0;
for (int i=0; i < numOfBuckets; i++)
{
if (cpuBucketsMax[i] != h_bucketsMax[i])
{
allMatch = false;
printf("CPU i=%d max = %d mismatches GPU max = %d\n", i, cpuBucketsMax[i], h_bucketsMax[i]);
break;
checkCudaErrors(cudaMalloc(&d_valueInBuckets, sizeof(int) * NUM_ELEMS));
checkCudaErrors(cudaMalloc(&d_bucketsMax, sizeof(int) * numOfBuckets));
checkCudaErrors(cudaMemset(d_bucketsMax, 0, sizeof(int) * numOfBuckets));
checkCudaErrors(cudaMemcpy(d_valueInBuckets, h_valueInBuckets, sizeof(int) * NUM_ELEMS, cudaMemcpyHostToDevice));
dim3 dimBlock(NUM_THREADS_PER_BLOCK, 1, 1);
dim3 dimGrid((NUM_ELEMS / NUM_THREADS_PER_BLOCK), 1, 1);
calculateMaxInEachBuckets<<<dimGrid, dimBlock>>>(d_srcArr, d_valueInBuckets, d_bucketsMax, NUM_ELEMS, numOfBuckets);
checkCudaErrors(cudaMemcpy(h_bucketsMax, d_bucketsMax, sizeof(int) * numOfBuckets, cudaMemcpyDeviceToHost));
for (int i = 0; i < NUM_ELEMS; i++) {
if (cpuBucketsMax[h_srcArr[i]] < h_valueInBuckets[i]) {
cpuBucketsMax[h_srcArr[i]] = h_valueInBuckets[i];
}
}
}
if (allMatch)
{
printf("CPU max matches GPU max\n");
}
delete[] h_valueInBuckets;
delete[] cpuBucketsMax;
delete[] h_bucketsMax;
checkCudaErrors(cudaFree(d_valueInBuckets));
checkCudaErrors(cudaFree(d_bucketsMax));
bool allMatch = true;
int finalElems = 0;
for (int i = 0; i < numOfBuckets; i++) {
if (cpuBucketsMax[i] != h_bucketsMax[i]) {
allMatch = false;
printf("CPU i=%d max = %d mismatches GPU max = %d\n", i, cpuBucketsMax[i], h_bucketsMax[i]);
break;
}
}
if (allMatch) {
printf("CPU max matches GPU max\n");
}
if (!allMatch && finalElems != NUM_ELEMS)
{
return EXIT_FAILURE;
}
delete[] h_valueInBuckets;
delete[] cpuBucketsMax;
delete[] h_bucketsMax;
checkCudaErrors(cudaFree(d_valueInBuckets));
checkCudaErrors(cudaFree(d_bucketsMax));
return EXIT_SUCCESS;
if (!allMatch && finalElems != NUM_ELEMS) {
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
int main(int argc, char **argv) {
int *data_to_filter, *filtered_data, nres = 0;
int *d_data_to_filter, *d_filtered_data, *d_nres;
int main(int argc, char **argv)
{
int *data_to_filter, *filtered_data, nres = 0;
int *d_data_to_filter, *d_filtered_data, *d_nres;
int numOfBuckets = 5;
int numOfBuckets = 5;
data_to_filter = reinterpret_cast<int *>(malloc(sizeof(int) * NUM_ELEMS));
data_to_filter = reinterpret_cast<int *>(malloc(sizeof(int) * NUM_ELEMS));
// Generate input data.
for (int i = 0; i < NUM_ELEMS; i++) {
data_to_filter[i] = rand() % numOfBuckets;
}
int devId = findCudaDevice(argc, (const char **)argv);
checkCudaErrors(cudaMalloc(&d_data_to_filter, sizeof(int) * NUM_ELEMS));
checkCudaErrors(cudaMalloc(&d_filtered_data, sizeof(int) * NUM_ELEMS));
checkCudaErrors(cudaMalloc(&d_nres, sizeof(int)));
checkCudaErrors(cudaMemcpy(d_data_to_filter, data_to_filter,
sizeof(int) * NUM_ELEMS, cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemset(d_nres, 0, sizeof(int)));
dim3 dimBlock(NUM_THREADS_PER_BLOCK, 1, 1);
dim3 dimGrid((NUM_ELEMS / NUM_THREADS_PER_BLOCK) + 1, 1, 1);
filter_arr<<<dimGrid, dimBlock>>>(d_filtered_data, d_nres, d_data_to_filter,
NUM_ELEMS);
checkCudaErrors(
cudaMemcpy(&nres, d_nres, sizeof(int), cudaMemcpyDeviceToHost));
filtered_data = reinterpret_cast<int *>(malloc(sizeof(int) * nres));
checkCudaErrors(cudaMemcpy(filtered_data, d_filtered_data, sizeof(int) * nres,
cudaMemcpyDeviceToHost));
int *host_filtered_data =
reinterpret_cast<int *>(malloc(sizeof(int) * NUM_ELEMS));
// Generate host output with host filtering code.
int host_flt_count = 0;
for (int i = 0; i < NUM_ELEMS; i++) {
if (data_to_filter[i] > 0) {
host_filtered_data[host_flt_count++] = data_to_filter[i];
// Generate input data.
for (int i = 0; i < NUM_ELEMS; i++) {
data_to_filter[i] = rand() % numOfBuckets;
}
}
int major = 0;
checkCudaErrors(cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, devId));
int devId = findCudaDevice(argc, (const char **)argv);
int mapIndicesToBucketsStatus = EXIT_SUCCESS;
int calculateMaxInBucketsStatus = EXIT_SUCCESS;
// atomicAggIncMulti & atomicAggMaxMulti require a GPU of Volta (SM7X) architecture or higher,
// so that it can take advantage of the new MATCH capability of Volta hardware
if (major >= 7) {
mapIndicesToBucketsStatus = mapIndicesToBuckets(data_to_filter, d_data_to_filter, numOfBuckets);
calculateMaxInBucketsStatus = calculateMaxInBuckets(data_to_filter, d_data_to_filter, numOfBuckets);
}
checkCudaErrors(cudaMalloc(&d_data_to_filter, sizeof(int) * NUM_ELEMS));
checkCudaErrors(cudaMalloc(&d_filtered_data, sizeof(int) * NUM_ELEMS));
checkCudaErrors(cudaMalloc(&d_nres, sizeof(int)));
printf("\nWarp Aggregated Atomics %s \n",
(host_flt_count == nres) && (mapIndicesToBucketsStatus == EXIT_SUCCESS) &&
(calculateMaxInBucketsStatus == EXIT_SUCCESS) ? "PASSED" : "FAILED");
checkCudaErrors(cudaMemcpy(d_data_to_filter, data_to_filter, sizeof(int) * NUM_ELEMS, cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemset(d_nres, 0, sizeof(int)));
checkCudaErrors(cudaFree(d_data_to_filter));
checkCudaErrors(cudaFree(d_filtered_data));
checkCudaErrors(cudaFree(d_nres));
free(data_to_filter);
free(filtered_data);
free(host_filtered_data);
dim3 dimBlock(NUM_THREADS_PER_BLOCK, 1, 1);
dim3 dimGrid((NUM_ELEMS / NUM_THREADS_PER_BLOCK) + 1, 1, 1);
filter_arr<<<dimGrid, dimBlock>>>(d_filtered_data, d_nres, d_data_to_filter, NUM_ELEMS);
checkCudaErrors(cudaMemcpy(&nres, d_nres, sizeof(int), cudaMemcpyDeviceToHost));
filtered_data = reinterpret_cast<int *>(malloc(sizeof(int) * nres));
checkCudaErrors(cudaMemcpy(filtered_data, d_filtered_data, sizeof(int) * nres, cudaMemcpyDeviceToHost));
int *host_filtered_data = reinterpret_cast<int *>(malloc(sizeof(int) * NUM_ELEMS));
// Generate host output with host filtering code.
int host_flt_count = 0;
for (int i = 0; i < NUM_ELEMS; i++) {
if (data_to_filter[i] > 0) {
host_filtered_data[host_flt_count++] = data_to_filter[i];
}
}
int major = 0;
checkCudaErrors(cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, devId));
int mapIndicesToBucketsStatus = EXIT_SUCCESS;
int calculateMaxInBucketsStatus = EXIT_SUCCESS;
// atomicAggIncMulti & atomicAggMaxMulti require a GPU of Volta (SM7X) architecture or higher,
// so that it can take advantage of the new MATCH capability of Volta hardware
if (major >= 7) {
mapIndicesToBucketsStatus = mapIndicesToBuckets(data_to_filter, d_data_to_filter, numOfBuckets);
calculateMaxInBucketsStatus = calculateMaxInBuckets(data_to_filter, d_data_to_filter, numOfBuckets);
}
printf("\nWarp Aggregated Atomics %s \n",
(host_flt_count == nres) && (mapIndicesToBucketsStatus == EXIT_SUCCESS)
&& (calculateMaxInBucketsStatus == EXIT_SUCCESS)
? "PASSED"
: "FAILED");
checkCudaErrors(cudaFree(d_data_to_filter));
checkCudaErrors(cudaFree(d_filtered_data));
checkCudaErrors(cudaFree(d_nres));
free(data_to_filter);
free(filtered_data);
free(host_filtered_data);
}