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/* 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.
*/
/*
* This sample calculates scalar products of a
* given set of input vector pairs
*/
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <string.h>
#include <helper_functions.h>
#include <helper_cuda.h>
///////////////////////////////////////////////////////////////////////////////
// Calculate scalar products of VectorN vectors of ElementN elements on CPU
///////////////////////////////////////////////////////////////////////////////
extern "C" void scalarProdCPU(float *h_C, float *h_A, float *h_B, int vectorN,
int elementN);
///////////////////////////////////////////////////////////////////////////////
// Calculate scalar products of VectorN vectors of ElementN elements on GPU
///////////////////////////////////////////////////////////////////////////////
#include "scalarProd_kernel.cuh"
////////////////////////////////////////////////////////////////////////////////
// Helper function, returning uniformly distributed
// random float in [low, high] range
////////////////////////////////////////////////////////////////////////////////
float RandFloat(float low, float high) {
float t = (float)rand() / (float)RAND_MAX;
return (1.0f - t) * low + t * high;
}
///////////////////////////////////////////////////////////////////////////////
// Data configuration
///////////////////////////////////////////////////////////////////////////////
// Total number of input vector pairs; arbitrary
const int VECTOR_N = 256;
// Number of elements per vector; arbitrary,
// but strongly preferred to be a multiple of warp size
// to meet memory coalescing constraints
const int ELEMENT_N = 4096;
// Total number of data elements
const int DATA_N = VECTOR_N * ELEMENT_N;
const int DATA_SZ = DATA_N * sizeof(float);
const int RESULT_SZ = VECTOR_N * sizeof(float);
///////////////////////////////////////////////////////////////////////////////
// Main program
///////////////////////////////////////////////////////////////////////////////
int main(int argc, char **argv) {
float *h_A, *h_B, *h_C_CPU, *h_C_GPU;
float *d_A, *d_B, *d_C;
double delta, ref, sum_delta, sum_ref, L1norm;
StopWatchInterface *hTimer = NULL;
int i;
printf("%s Starting...\n\n", argv[0]);
// use command-line specified CUDA device, otherwise use device with highest
// Gflops/s
findCudaDevice(argc, (const char **)argv);
sdkCreateTimer(&hTimer);
printf("Initializing data...\n");
printf("...allocating CPU memory.\n");
h_A = (float *)malloc(DATA_SZ);
h_B = (float *)malloc(DATA_SZ);
h_C_CPU = (float *)malloc(RESULT_SZ);
h_C_GPU = (float *)malloc(RESULT_SZ);
printf("...allocating GPU memory.\n");
checkCudaErrors(cudaMalloc((void **)&d_A, DATA_SZ));
checkCudaErrors(cudaMalloc((void **)&d_B, DATA_SZ));
checkCudaErrors(cudaMalloc((void **)&d_C, RESULT_SZ));
printf("...generating input data in CPU mem.\n");
srand(123);
// Generating input data on CPU
for (i = 0; i < DATA_N; i++) {
h_A[i] = RandFloat(0.0f, 1.0f);
h_B[i] = RandFloat(0.0f, 1.0f);
}
printf("...copying input data to GPU mem.\n");
// Copy options data to GPU memory for further processing
checkCudaErrors(cudaMemcpy(d_A, h_A, DATA_SZ, cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_B, h_B, DATA_SZ, cudaMemcpyHostToDevice));
printf("Data init done.\n");
printf("Executing GPU kernel...\n");
checkCudaErrors(cudaDeviceSynchronize());
sdkResetTimer(&hTimer);
sdkStartTimer(&hTimer);
scalarProdGPU<<<128, 256>>>(d_C, d_A, d_B, VECTOR_N, ELEMENT_N);
getLastCudaError("scalarProdGPU() execution failed\n");
checkCudaErrors(cudaDeviceSynchronize());
sdkStopTimer(&hTimer);
printf("GPU time: %f msecs.\n", sdkGetTimerValue(&hTimer));
printf("Reading back GPU result...\n");
// Read back GPU results to compare them to CPU results
checkCudaErrors(cudaMemcpy(h_C_GPU, d_C, RESULT_SZ, cudaMemcpyDeviceToHost));
printf("Checking GPU results...\n");
printf("..running CPU scalar product calculation\n");
scalarProdCPU(h_C_CPU, h_A, h_B, VECTOR_N, ELEMENT_N);
printf("...comparing the results\n");
// Calculate max absolute difference and L1 distance
// between CPU and GPU results
sum_delta = 0;
sum_ref = 0;
for (i = 0; i < VECTOR_N; i++) {
delta = fabs(h_C_GPU[i] - h_C_CPU[i]);
ref = h_C_CPU[i];
sum_delta += delta;
sum_ref += ref;
}
L1norm = sum_delta / sum_ref;
printf("Shutting down...\n");
checkCudaErrors(cudaFree(d_C));
checkCudaErrors(cudaFree(d_B));
checkCudaErrors(cudaFree(d_A));
free(h_C_GPU);
free(h_C_CPU);
free(h_B);
free(h_A);
sdkDeleteTimer(&hTimer);
printf("L1 error: %E\n", L1norm);
printf((L1norm < 1e-6) ? "Test passed\n" : "Test failed!\n");
exit(L1norm < 1e-6 ? EXIT_SUCCESS : EXIT_FAILURE);
}
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