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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.
*/
/*
* Walsh transforms belong to a class of generalized Fourier transformations.
* They have applications in various fields of electrical engineering
* and numeric theory. In this sample we demonstrate efficient implementation
* of naturally-ordered Walsh transform
* (also known as Walsh-Hadamard or Hadamard transform) in CUDA and its
* particular application to dyadic convolution computation.
* Refer to excellent Jorg Arndt's "Algorithms for Programmers" textbook
* http://www.jjj.de/fxt/fxtbook.pdf (Chapter 22)
*
* Victor Podlozhnyuk (vpodlozhnyuk@nvidia.com)
*/
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <helper_functions.h>
#include <helper_cuda.h>
////////////////////////////////////////////////////////////////////////////////
// Reference CPU FWT
////////////////////////////////////////////////////////////////////////////////
extern "C" void fwtCPU(float *h_Output, float *h_Input, int log2N);
extern "C" void slowWTcpu(float *h_Output, float *h_Input, int log2N);
extern "C" void dyadicConvolutionCPU(float *h_Result, float *h_Data,
float *h_Kernel, int log2dataN,
int log2kernelN);
////////////////////////////////////////////////////////////////////////////////
// GPU FWT
////////////////////////////////////////////////////////////////////////////////
#include "fastWalshTransform_kernel.cuh"
////////////////////////////////////////////////////////////////////////////////
// Data configuration
////////////////////////////////////////////////////////////////////////////////
const int log2Kernel = 7;
const int log2Data = 23;
const int dataN = 1 << log2Data;
const int kernelN = 1 << log2Kernel;
const int DATA_SIZE = dataN * sizeof(float);
const int KERNEL_SIZE = kernelN * sizeof(float);
const double NOPS = 3.0 * (double)dataN * (double)log2Data / 2.0;
////////////////////////////////////////////////////////////////////////////////
// Main program
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char *argv[]) {
float *h_Data, *h_Kernel, *h_ResultCPU, *h_ResultGPU;
float *d_Data, *d_Kernel;
double delta, ref, sum_delta2, sum_ref2, L2norm, gpuTime;
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_Kernel = (float *)malloc(KERNEL_SIZE);
h_Data = (float *)malloc(DATA_SIZE);
h_ResultCPU = (float *)malloc(DATA_SIZE);
h_ResultGPU = (float *)malloc(DATA_SIZE);
printf("...allocating GPU memory\n");
checkCudaErrors(cudaMalloc((void **)&d_Kernel, DATA_SIZE));
checkCudaErrors(cudaMalloc((void **)&d_Data, DATA_SIZE));
printf("...generating data\n");
printf("Data length: %i; kernel length: %i\n", dataN, kernelN);
srand(2007);
for (i = 0; i < kernelN; i++) {
h_Kernel[i] = (float)rand() / (float)RAND_MAX;
}
for (i = 0; i < dataN; i++) {
h_Data[i] = (float)rand() / (float)RAND_MAX;
}
checkCudaErrors(cudaMemset(d_Kernel, 0, DATA_SIZE));
checkCudaErrors(
cudaMemcpy(d_Kernel, h_Kernel, KERNEL_SIZE, cudaMemcpyHostToDevice));
checkCudaErrors(
cudaMemcpy(d_Data, h_Data, DATA_SIZE, cudaMemcpyHostToDevice));
printf("Running GPU dyadic convolution using Fast Walsh Transform...\n");
checkCudaErrors(cudaDeviceSynchronize());
sdkResetTimer(&hTimer);
sdkStartTimer(&hTimer);
fwtBatchGPU(d_Data, 1, log2Data);
fwtBatchGPU(d_Kernel, 1, log2Data);
modulateGPU(d_Data, d_Kernel, dataN);
fwtBatchGPU(d_Data, 1, log2Data);
checkCudaErrors(cudaDeviceSynchronize());
sdkStopTimer(&hTimer);
gpuTime = sdkGetTimerValue(&hTimer);
printf("GPU time: %f ms; GOP/s: %f\n", gpuTime,
NOPS / (gpuTime * 0.001 * 1E+9));
printf("Reading back GPU results...\n");
checkCudaErrors(
cudaMemcpy(h_ResultGPU, d_Data, DATA_SIZE, cudaMemcpyDeviceToHost));
printf("Running straightforward CPU dyadic convolution...\n");
dyadicConvolutionCPU(h_ResultCPU, h_Data, h_Kernel, log2Data, log2Kernel);
printf("Comparing the results...\n");
sum_delta2 = 0;
sum_ref2 = 0;
for (i = 0; i < dataN; i++) {
delta = h_ResultCPU[i] - h_ResultGPU[i];
ref = h_ResultCPU[i];
sum_delta2 += delta * delta;
sum_ref2 += ref * ref;
}
L2norm = sqrt(sum_delta2 / sum_ref2);
printf("Shutting down...\n");
sdkDeleteTimer(&hTimer);
checkCudaErrors(cudaFree(d_Data));
checkCudaErrors(cudaFree(d_Kernel));
free(h_ResultGPU);
free(h_ResultCPU);
free(h_Data);
free(h_Kernel);
printf("L2 norm: %E\n", L2norm);
printf(L2norm < 1e-6 ? "Test passed\n" : "Test failed!\n");
}
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