mirror of
https://github.com/NVIDIA/cuda-samples.git
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216 lines
7.4 KiB
Plaintext
216 lines
7.4 KiB
Plaintext
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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#include "cuda_fp16.h"
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#include "helper_cuda.h"
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#include <cstdio>
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#include <cstdlib>
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#include <ctime>
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#define NUM_OF_BLOCKS 128
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#define NUM_OF_THREADS 128
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__forceinline__ __device__ void reduceInShared_intrinsics(half2 *const v) {
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if (threadIdx.x < 64)
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v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 64]);
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__syncthreads();
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if (threadIdx.x < 32)
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v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 32]);
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__syncthreads();
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if (threadIdx.x < 16)
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v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 16]);
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__syncthreads();
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if (threadIdx.x < 8)
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v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 8]);
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__syncthreads();
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if (threadIdx.x < 4)
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v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 4]);
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__syncthreads();
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if (threadIdx.x < 2)
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v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 2]);
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__syncthreads();
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if (threadIdx.x < 1)
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v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 1]);
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__syncthreads();
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}
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__forceinline__ __device__ void reduceInShared_native(half2 *const v) {
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if (threadIdx.x < 64) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 64];
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__syncthreads();
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if (threadIdx.x < 32) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 32];
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__syncthreads();
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if (threadIdx.x < 16) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 16];
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__syncthreads();
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if (threadIdx.x < 8) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 8];
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__syncthreads();
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if (threadIdx.x < 4) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 4];
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__syncthreads();
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if (threadIdx.x < 2) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 2];
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__syncthreads();
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if (threadIdx.x < 1) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 1];
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__syncthreads();
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}
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__global__ void scalarProductKernel_intrinsics(half2 const *const a,
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half2 const *const b,
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float *const results,
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size_t const size) {
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const int stride = gridDim.x * blockDim.x;
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__shared__ half2 shArray[NUM_OF_THREADS];
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shArray[threadIdx.x] = __float2half2_rn(0.f);
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half2 value = __float2half2_rn(0.f);
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for (int i = threadIdx.x + blockDim.x + blockIdx.x; i < size; i += stride) {
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value = __hfma2(a[i], b[i], value);
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}
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shArray[threadIdx.x] = value;
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__syncthreads();
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reduceInShared_intrinsics(shArray);
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if (threadIdx.x == 0) {
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half2 result = shArray[0];
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float f_result = __low2float(result) + __high2float(result);
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results[blockIdx.x] = f_result;
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}
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}
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__global__ void scalarProductKernel_native(half2 const *const a,
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half2 const *const b,
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float *const results,
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size_t const size) {
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const int stride = gridDim.x * blockDim.x;
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__shared__ half2 shArray[NUM_OF_THREADS];
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half2 value(0.f, 0.f);
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shArray[threadIdx.x] = value;
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for (int i = threadIdx.x + blockDim.x + blockIdx.x; i < size; i += stride) {
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value = a[i] * b[i] + value;
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}
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shArray[threadIdx.x] = value;
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__syncthreads();
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reduceInShared_native(shArray);
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if (threadIdx.x == 0) {
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half2 result = shArray[0];
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float f_result = (float)result.y + (float)result.x;
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results[blockIdx.x] = f_result;
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}
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}
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void generateInput(half2 *a, size_t size) {
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for (size_t i = 0; i < size; ++i) {
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half2 temp;
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temp.x = static_cast<float>(rand() % 4);
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temp.y = static_cast<float>(rand() % 2);
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a[i] = temp;
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}
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}
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int main(int argc, char *argv[]) {
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srand((unsigned int)time(NULL));
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size_t size = NUM_OF_BLOCKS * NUM_OF_THREADS * 16;
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half2 *vec[2];
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half2 *devVec[2];
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float *results;
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float *devResults;
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int devID = findCudaDevice(argc, (const char **)argv);
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cudaDeviceProp devProp;
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checkCudaErrors(cudaGetDeviceProperties(&devProp, devID));
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if (devProp.major < 5 || (devProp.major == 5 && devProp.minor < 3)) {
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printf(
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"ERROR: fp16ScalarProduct requires GPU devices with compute SM 5.3 or "
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"higher.\n");
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return EXIT_WAIVED;
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}
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for (int i = 0; i < 2; ++i) {
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checkCudaErrors(cudaMallocHost((void **)&vec[i], size * sizeof *vec[i]));
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checkCudaErrors(cudaMalloc((void **)&devVec[i], size * sizeof *devVec[i]));
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}
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checkCudaErrors(
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cudaMallocHost((void **)&results, NUM_OF_BLOCKS * sizeof *results));
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checkCudaErrors(
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cudaMalloc((void **)&devResults, NUM_OF_BLOCKS * sizeof *devResults));
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for (int i = 0; i < 2; ++i) {
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generateInput(vec[i], size);
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checkCudaErrors(cudaMemcpy(devVec[i], vec[i], size * sizeof *vec[i],
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cudaMemcpyHostToDevice));
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}
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scalarProductKernel_native<<<NUM_OF_BLOCKS, NUM_OF_THREADS>>>(
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devVec[0], devVec[1], devResults, size);
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checkCudaErrors(cudaMemcpy(results, devResults,
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NUM_OF_BLOCKS * sizeof *results,
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cudaMemcpyDeviceToHost));
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float result_native = 0;
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for (int i = 0; i < NUM_OF_BLOCKS; ++i) {
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result_native += results[i];
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}
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printf("Result native operators\t: %f \n", result_native);
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scalarProductKernel_intrinsics<<<NUM_OF_BLOCKS, NUM_OF_THREADS>>>(
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devVec[0], devVec[1], devResults, size);
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checkCudaErrors(cudaMemcpy(results, devResults,
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NUM_OF_BLOCKS * sizeof *results,
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cudaMemcpyDeviceToHost));
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float result_intrinsics = 0;
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for (int i = 0; i < NUM_OF_BLOCKS; ++i) {
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result_intrinsics += results[i];
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}
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printf("Result intrinsics\t: %f \n", result_intrinsics);
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printf("&&&& fp16ScalarProduct %s\n",
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(fabs(result_intrinsics - result_native) < 0.00001) ? "PASSED"
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: "FAILED");
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for (int i = 0; i < 2; ++i) {
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checkCudaErrors(cudaFree(devVec[i]));
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checkCudaErrors(cudaFreeHost(vec[i]));
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}
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checkCudaErrors(cudaFree(devResults));
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checkCudaErrors(cudaFreeHost(results));
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return EXIT_SUCCESS;
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}
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