mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2024-11-28 21:29:15 +08:00
216 lines
7.4 KiB
Plaintext
216 lines
7.4 KiB
Plaintext
/* Copyright (c) 2022, 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 "cuda_fp16.h"
|
|
#include "helper_cuda.h"
|
|
|
|
#include <cstdio>
|
|
#include <cstdlib>
|
|
#include <ctime>
|
|
|
|
#define NUM_OF_BLOCKS 128
|
|
#define NUM_OF_THREADS 128
|
|
|
|
__forceinline__ __device__ void reduceInShared_intrinsics(half2 *const v) {
|
|
if (threadIdx.x < 64)
|
|
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 64]);
|
|
__syncthreads();
|
|
if (threadIdx.x < 32)
|
|
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 32]);
|
|
__syncthreads();
|
|
if (threadIdx.x < 16)
|
|
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 16]);
|
|
__syncthreads();
|
|
if (threadIdx.x < 8)
|
|
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 8]);
|
|
__syncthreads();
|
|
if (threadIdx.x < 4)
|
|
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 4]);
|
|
__syncthreads();
|
|
if (threadIdx.x < 2)
|
|
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 2]);
|
|
__syncthreads();
|
|
if (threadIdx.x < 1)
|
|
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 1]);
|
|
__syncthreads();
|
|
}
|
|
|
|
__forceinline__ __device__ void reduceInShared_native(half2 *const v) {
|
|
if (threadIdx.x < 64) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 64];
|
|
__syncthreads();
|
|
if (threadIdx.x < 32) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 32];
|
|
__syncthreads();
|
|
if (threadIdx.x < 16) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 16];
|
|
__syncthreads();
|
|
if (threadIdx.x < 8) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 8];
|
|
__syncthreads();
|
|
if (threadIdx.x < 4) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 4];
|
|
__syncthreads();
|
|
if (threadIdx.x < 2) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 2];
|
|
__syncthreads();
|
|
if (threadIdx.x < 1) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 1];
|
|
__syncthreads();
|
|
}
|
|
|
|
__global__ void scalarProductKernel_intrinsics(half2 const *const a,
|
|
half2 const *const b,
|
|
float *const results,
|
|
size_t const size) {
|
|
const int stride = gridDim.x * blockDim.x;
|
|
__shared__ half2 shArray[NUM_OF_THREADS];
|
|
|
|
shArray[threadIdx.x] = __float2half2_rn(0.f);
|
|
half2 value = __float2half2_rn(0.f);
|
|
|
|
for (int i = threadIdx.x + blockDim.x + blockIdx.x; i < size; i += stride) {
|
|
value = __hfma2(a[i], b[i], value);
|
|
}
|
|
|
|
shArray[threadIdx.x] = value;
|
|
__syncthreads();
|
|
reduceInShared_intrinsics(shArray);
|
|
|
|
if (threadIdx.x == 0) {
|
|
half2 result = shArray[0];
|
|
float f_result = __low2float(result) + __high2float(result);
|
|
results[blockIdx.x] = f_result;
|
|
}
|
|
}
|
|
|
|
__global__ void scalarProductKernel_native(half2 const *const a,
|
|
half2 const *const b,
|
|
float *const results,
|
|
size_t const size) {
|
|
const int stride = gridDim.x * blockDim.x;
|
|
__shared__ half2 shArray[NUM_OF_THREADS];
|
|
|
|
half2 value(0.f, 0.f);
|
|
shArray[threadIdx.x] = value;
|
|
|
|
for (int i = threadIdx.x + blockDim.x + blockIdx.x; i < size; i += stride) {
|
|
value = a[i] * b[i] + value;
|
|
}
|
|
|
|
shArray[threadIdx.x] = value;
|
|
__syncthreads();
|
|
reduceInShared_native(shArray);
|
|
|
|
if (threadIdx.x == 0) {
|
|
half2 result = shArray[0];
|
|
float f_result = (float)result.y + (float)result.x;
|
|
results[blockIdx.x] = f_result;
|
|
}
|
|
}
|
|
|
|
void generateInput(half2 *a, size_t size) {
|
|
for (size_t i = 0; i < size; ++i) {
|
|
half2 temp;
|
|
temp.x = static_cast<float>(rand() % 4);
|
|
temp.y = static_cast<float>(rand() % 2);
|
|
a[i] = temp;
|
|
}
|
|
}
|
|
|
|
int main(int argc, char *argv[]) {
|
|
srand((unsigned int)time(NULL));
|
|
size_t size = NUM_OF_BLOCKS * NUM_OF_THREADS * 16;
|
|
|
|
half2 *vec[2];
|
|
half2 *devVec[2];
|
|
|
|
float *results;
|
|
float *devResults;
|
|
|
|
int devID = findCudaDevice(argc, (const char **)argv);
|
|
|
|
cudaDeviceProp devProp;
|
|
checkCudaErrors(cudaGetDeviceProperties(&devProp, devID));
|
|
|
|
if (devProp.major < 5 || (devProp.major == 5 && devProp.minor < 3)) {
|
|
printf(
|
|
"ERROR: fp16ScalarProduct requires GPU devices with compute SM 5.3 or "
|
|
"higher.\n");
|
|
return EXIT_WAIVED;
|
|
}
|
|
|
|
for (int i = 0; i < 2; ++i) {
|
|
checkCudaErrors(cudaMallocHost((void **)&vec[i], size * sizeof *vec[i]));
|
|
checkCudaErrors(cudaMalloc((void **)&devVec[i], size * sizeof *devVec[i]));
|
|
}
|
|
|
|
checkCudaErrors(
|
|
cudaMallocHost((void **)&results, NUM_OF_BLOCKS * sizeof *results));
|
|
checkCudaErrors(
|
|
cudaMalloc((void **)&devResults, NUM_OF_BLOCKS * sizeof *devResults));
|
|
|
|
for (int i = 0; i < 2; ++i) {
|
|
generateInput(vec[i], size);
|
|
checkCudaErrors(cudaMemcpy(devVec[i], vec[i], size * sizeof *vec[i],
|
|
cudaMemcpyHostToDevice));
|
|
}
|
|
|
|
scalarProductKernel_native<<<NUM_OF_BLOCKS, NUM_OF_THREADS>>>(
|
|
devVec[0], devVec[1], devResults, size);
|
|
|
|
checkCudaErrors(cudaMemcpy(results, devResults,
|
|
NUM_OF_BLOCKS * sizeof *results,
|
|
cudaMemcpyDeviceToHost));
|
|
|
|
float result_native = 0;
|
|
for (int i = 0; i < NUM_OF_BLOCKS; ++i) {
|
|
result_native += results[i];
|
|
}
|
|
printf("Result native operators\t: %f \n", result_native);
|
|
|
|
scalarProductKernel_intrinsics<<<NUM_OF_BLOCKS, NUM_OF_THREADS>>>(
|
|
devVec[0], devVec[1], devResults, size);
|
|
|
|
checkCudaErrors(cudaMemcpy(results, devResults,
|
|
NUM_OF_BLOCKS * sizeof *results,
|
|
cudaMemcpyDeviceToHost));
|
|
|
|
float result_intrinsics = 0;
|
|
for (int i = 0; i < NUM_OF_BLOCKS; ++i) {
|
|
result_intrinsics += results[i];
|
|
}
|
|
printf("Result intrinsics\t: %f \n", result_intrinsics);
|
|
|
|
printf("&&&& fp16ScalarProduct %s\n",
|
|
(fabs(result_intrinsics - result_native) < 0.00001) ? "PASSED"
|
|
: "FAILED");
|
|
|
|
for (int i = 0; i < 2; ++i) {
|
|
checkCudaErrors(cudaFree(devVec[i]));
|
|
checkCudaErrors(cudaFreeHost(vec[i]));
|
|
}
|
|
|
|
checkCudaErrors(cudaFree(devResults));
|
|
checkCudaErrors(cudaFreeHost(results));
|
|
|
|
return EXIT_SUCCESS;
|
|
}
|