mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2024-12-01 12:29:15 +08:00
587 lines
20 KiB
C++
587 lines
20 KiB
C++
/* 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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/*
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* This sample demonstrates how 2D convolutions
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* with very large kernel sizes
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* can be efficiently implemented
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* using FFT transformations.
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*/
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#include <assert.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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// Include CUDA runtime and CUFFT
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#include <cuda_runtime.h>
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#include <cufft.h>
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// Helper functions for CUDA
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#include <helper_functions.h>
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#include <helper_cuda.h>
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#include "convolutionFFT2D_common.h"
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////////////////////////////////////////////////////////////////////////////////
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// Helper functions
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////////////////////////////////////////////////////////////////////////////////
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int snapTransformSize(int dataSize) {
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int hiBit;
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unsigned int lowPOT, hiPOT;
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dataSize = iAlignUp(dataSize, 16);
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for (hiBit = 31; hiBit >= 0; hiBit--)
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if (dataSize & (1U << hiBit)) {
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break;
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}
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lowPOT = 1U << hiBit;
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if (lowPOT == (unsigned int)dataSize) {
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return dataSize;
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}
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hiPOT = 1U << (hiBit + 1);
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if (hiPOT <= 1024) {
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return hiPOT;
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} else {
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return iAlignUp(dataSize, 512);
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}
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}
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float getRand(void) { return (float)(rand() % 16); }
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bool test0(void) {
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float *h_Data, *h_Kernel, *h_ResultCPU, *h_ResultGPU;
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float *d_Data, *d_PaddedData, *d_Kernel, *d_PaddedKernel;
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fComplex *d_DataSpectrum, *d_KernelSpectrum;
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cufftHandle fftPlanFwd, fftPlanInv;
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bool bRetVal;
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StopWatchInterface *hTimer = NULL;
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sdkCreateTimer(&hTimer);
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printf("Testing built-in R2C / C2R FFT-based convolution\n");
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const int kernelH = 7;
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const int kernelW = 6;
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const int kernelY = 3;
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const int kernelX = 4;
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const int dataH = 2000;
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const int dataW = 2000;
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const int fftH = snapTransformSize(dataH + kernelH - 1);
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const int fftW = snapTransformSize(dataW + kernelW - 1);
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printf("...allocating memory\n");
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h_Data = (float *)malloc(dataH * dataW * sizeof(float));
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h_Kernel = (float *)malloc(kernelH * kernelW * sizeof(float));
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h_ResultCPU = (float *)malloc(dataH * dataW * sizeof(float));
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h_ResultGPU = (float *)malloc(fftH * fftW * sizeof(float));
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checkCudaErrors(cudaMalloc((void **)&d_Data, dataH * dataW * sizeof(float)));
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checkCudaErrors(
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cudaMalloc((void **)&d_Kernel, kernelH * kernelW * sizeof(float)));
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checkCudaErrors(
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cudaMalloc((void **)&d_PaddedData, fftH * fftW * sizeof(float)));
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checkCudaErrors(
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cudaMalloc((void **)&d_PaddedKernel, fftH * fftW * sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_DataSpectrum,
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fftH * (fftW / 2 + 1) * sizeof(fComplex)));
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checkCudaErrors(cudaMalloc((void **)&d_KernelSpectrum,
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fftH * (fftW / 2 + 1) * sizeof(fComplex)));
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checkCudaErrors(cudaMemset(d_KernelSpectrum, 0,
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fftH * (fftW / 2 + 1) * sizeof(fComplex)));
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printf("...generating random input data\n");
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srand(2010);
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for (int i = 0; i < dataH * dataW; i++) {
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h_Data[i] = getRand();
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}
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for (int i = 0; i < kernelH * kernelW; i++) {
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h_Kernel[i] = getRand();
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}
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printf("...creating R2C & C2R FFT plans for %i x %i\n", fftH, fftW);
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checkCudaErrors(cufftPlan2d(&fftPlanFwd, fftH, fftW, CUFFT_R2C));
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checkCudaErrors(cufftPlan2d(&fftPlanInv, fftH, fftW, CUFFT_C2R));
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printf("...uploading to GPU and padding convolution kernel and input data\n");
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checkCudaErrors(cudaMemcpy(d_Kernel, h_Kernel,
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kernelH * kernelW * sizeof(float),
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cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemcpy(d_Data, h_Data, dataH * dataW * sizeof(float),
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cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemset(d_PaddedKernel, 0, fftH * fftW * sizeof(float)));
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checkCudaErrors(cudaMemset(d_PaddedData, 0, fftH * fftW * sizeof(float)));
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padKernel(d_PaddedKernel, d_Kernel, fftH, fftW, kernelH, kernelW, kernelY,
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kernelX);
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padDataClampToBorder(d_PaddedData, d_Data, fftH, fftW, dataH, dataW, kernelH,
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kernelW, kernelY, kernelX);
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// Not including kernel transformation into time measurement,
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// since convolution kernel is not changed very frequently
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printf("...transforming convolution kernel\n");
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checkCudaErrors(cufftExecR2C(fftPlanFwd, (cufftReal *)d_PaddedKernel,
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(cufftComplex *)d_KernelSpectrum));
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printf("...running GPU FFT convolution: ");
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checkCudaErrors(cudaDeviceSynchronize());
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sdkResetTimer(&hTimer);
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sdkStartTimer(&hTimer);
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checkCudaErrors(cufftExecR2C(fftPlanFwd, (cufftReal *)d_PaddedData,
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(cufftComplex *)d_DataSpectrum));
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modulateAndNormalize(d_DataSpectrum, d_KernelSpectrum, fftH, fftW, 1);
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checkCudaErrors(cufftExecC2R(fftPlanInv, (cufftComplex *)d_DataSpectrum,
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(cufftReal *)d_PaddedData));
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checkCudaErrors(cudaDeviceSynchronize());
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sdkStopTimer(&hTimer);
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double gpuTime = sdkGetTimerValue(&hTimer);
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printf("%f MPix/s (%f ms)\n",
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(double)dataH * (double)dataW * 1e-6 / (gpuTime * 0.001), gpuTime);
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printf("...reading back GPU convolution results\n");
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checkCudaErrors(cudaMemcpy(h_ResultGPU, d_PaddedData,
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fftH * fftW * sizeof(float),
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cudaMemcpyDeviceToHost));
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printf("...running reference CPU convolution\n");
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convolutionClampToBorderCPU(h_ResultCPU, h_Data, h_Kernel, dataH, dataW,
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kernelH, kernelW, kernelY, kernelX);
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printf("...comparing the results: ");
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double sum_delta2 = 0;
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double sum_ref2 = 0;
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double max_delta_ref = 0;
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for (int y = 0; y < dataH; y++)
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for (int x = 0; x < dataW; x++) {
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double rCPU = (double)h_ResultCPU[y * dataW + x];
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double rGPU = (double)h_ResultGPU[y * fftW + x];
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double delta = (rCPU - rGPU) * (rCPU - rGPU);
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double ref = rCPU * rCPU + rCPU * rCPU;
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if ((delta / ref) > max_delta_ref) {
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max_delta_ref = delta / ref;
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}
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sum_delta2 += delta;
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sum_ref2 += ref;
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}
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double L2norm = sqrt(sum_delta2 / sum_ref2);
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printf("rel L2 = %E (max delta = %E)\n", L2norm, sqrt(max_delta_ref));
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bRetVal = (L2norm < 1e-6) ? true : false;
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printf(bRetVal ? "L2norm Error OK\n" : "L2norm Error too high!\n");
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printf("...shutting down\n");
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sdkDeleteTimer(&hTimer);
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checkCudaErrors(cufftDestroy(fftPlanInv));
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checkCudaErrors(cufftDestroy(fftPlanFwd));
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checkCudaErrors(cudaFree(d_DataSpectrum));
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checkCudaErrors(cudaFree(d_KernelSpectrum));
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checkCudaErrors(cudaFree(d_PaddedData));
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checkCudaErrors(cudaFree(d_PaddedKernel));
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checkCudaErrors(cudaFree(d_Data));
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checkCudaErrors(cudaFree(d_Kernel));
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free(h_ResultGPU);
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free(h_ResultCPU);
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free(h_Data);
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free(h_Kernel);
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return bRetVal;
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}
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bool test1(void) {
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float *h_Data, *h_Kernel, *h_ResultCPU, *h_ResultGPU;
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float *d_Data, *d_Kernel, *d_PaddedData, *d_PaddedKernel;
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fComplex *d_DataSpectrum0, *d_KernelSpectrum0, *d_DataSpectrum,
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*d_KernelSpectrum;
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cufftHandle fftPlan;
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bool bRetVal;
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StopWatchInterface *hTimer = NULL;
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sdkCreateTimer(&hTimer);
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printf("Testing custom R2C / C2R FFT-based convolution\n");
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const uint fftPadding = 16;
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const int kernelH = 7;
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const int kernelW = 6;
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const int kernelY = 3;
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const int kernelX = 4;
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const int dataH = 2000;
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const int dataW = 2000;
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const int fftH = snapTransformSize(dataH + kernelH - 1);
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const int fftW = snapTransformSize(dataW + kernelW - 1);
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printf("...allocating memory\n");
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h_Data = (float *)malloc(dataH * dataW * sizeof(float));
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h_Kernel = (float *)malloc(kernelH * kernelW * sizeof(float));
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h_ResultCPU = (float *)malloc(dataH * dataW * sizeof(float));
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h_ResultGPU = (float *)malloc(fftH * fftW * sizeof(float));
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checkCudaErrors(cudaMalloc((void **)&d_Data, dataH * dataW * sizeof(float)));
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checkCudaErrors(
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cudaMalloc((void **)&d_Kernel, kernelH * kernelW * sizeof(float)));
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checkCudaErrors(
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cudaMalloc((void **)&d_PaddedData, fftH * fftW * sizeof(float)));
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checkCudaErrors(
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cudaMalloc((void **)&d_PaddedKernel, fftH * fftW * sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_DataSpectrum0,
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fftH * (fftW / 2) * sizeof(fComplex)));
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checkCudaErrors(cudaMalloc((void **)&d_KernelSpectrum0,
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fftH * (fftW / 2) * sizeof(fComplex)));
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checkCudaErrors(
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cudaMalloc((void **)&d_DataSpectrum,
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fftH * (fftW / 2 + fftPadding) * sizeof(fComplex)));
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checkCudaErrors(
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cudaMalloc((void **)&d_KernelSpectrum,
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fftH * (fftW / 2 + fftPadding) * sizeof(fComplex)));
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printf("...generating random input data\n");
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srand(2010);
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for (int i = 0; i < dataH * dataW; i++) {
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h_Data[i] = getRand();
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}
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for (int i = 0; i < kernelH * kernelW; i++) {
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h_Kernel[i] = getRand();
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}
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printf("...creating C2C FFT plan for %i x %i\n", fftH, fftW / 2);
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checkCudaErrors(cufftPlan2d(&fftPlan, fftH, fftW / 2, CUFFT_C2C));
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printf("...uploading to GPU and padding convolution kernel and input data\n");
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checkCudaErrors(cudaMemcpy(d_Data, h_Data, dataH * dataW * sizeof(float),
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cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemcpy(d_Kernel, h_Kernel,
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kernelH * kernelW * sizeof(float),
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cudaMemcpyHostToDevice));
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checkCudaErrors(cudaMemset(d_PaddedData, 0, fftH * fftW * sizeof(float)));
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checkCudaErrors(cudaMemset(d_PaddedKernel, 0, fftH * fftW * sizeof(float)));
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padDataClampToBorder(d_PaddedData, d_Data, fftH, fftW, dataH, dataW, kernelH,
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kernelW, kernelY, kernelX);
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padKernel(d_PaddedKernel, d_Kernel, fftH, fftW, kernelH, kernelW, kernelY,
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kernelX);
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// CUFFT_INVERSE works just as well...
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const int FFT_DIR = CUFFT_FORWARD;
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// Not including kernel transformation into time measurement,
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// since convolution kernel is not changed very frequently
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printf("...transforming convolution kernel\n");
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checkCudaErrors(cufftExecC2C(fftPlan, (cufftComplex *)d_PaddedKernel,
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(cufftComplex *)d_KernelSpectrum0, FFT_DIR));
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spPostprocess2D(d_KernelSpectrum, d_KernelSpectrum0, fftH, fftW / 2,
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fftPadding, FFT_DIR);
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printf("...running GPU FFT convolution: ");
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checkCudaErrors(cudaDeviceSynchronize());
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sdkResetTimer(&hTimer);
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sdkStartTimer(&hTimer);
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checkCudaErrors(cufftExecC2C(fftPlan, (cufftComplex *)d_PaddedData,
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(cufftComplex *)d_DataSpectrum0, FFT_DIR));
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spPostprocess2D(d_DataSpectrum, d_DataSpectrum0, fftH, fftW / 2, fftPadding,
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FFT_DIR);
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modulateAndNormalize(d_DataSpectrum, d_KernelSpectrum, fftH, fftW,
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fftPadding);
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spPreprocess2D(d_DataSpectrum0, d_DataSpectrum, fftH, fftW / 2, fftPadding,
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-FFT_DIR);
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checkCudaErrors(cufftExecC2C(fftPlan, (cufftComplex *)d_DataSpectrum0,
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(cufftComplex *)d_PaddedData, -FFT_DIR));
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checkCudaErrors(cudaDeviceSynchronize());
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sdkStopTimer(&hTimer);
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double gpuTime = sdkGetTimerValue(&hTimer);
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printf("%f MPix/s (%f ms)\n",
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(double)dataH * (double)dataW * 1e-6 / (gpuTime * 0.001), gpuTime);
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printf("...reading back GPU FFT results\n");
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checkCudaErrors(cudaMemcpy(h_ResultGPU, d_PaddedData,
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fftH * fftW * sizeof(float),
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cudaMemcpyDeviceToHost));
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printf("...running reference CPU convolution\n");
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convolutionClampToBorderCPU(h_ResultCPU, h_Data, h_Kernel, dataH, dataW,
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kernelH, kernelW, kernelY, kernelX);
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printf("...comparing the results: ");
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double sum_delta2 = 0;
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double sum_ref2 = 0;
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double max_delta_ref = 0;
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for (int y = 0; y < dataH; y++)
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for (int x = 0; x < dataW; x++) {
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double rCPU = (double)h_ResultCPU[y * dataW + x];
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double rGPU = (double)h_ResultGPU[y * fftW + x];
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double delta = (rCPU - rGPU) * (rCPU - rGPU);
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double ref = rCPU * rCPU + rCPU * rCPU;
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if ((delta / ref) > max_delta_ref) {
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max_delta_ref = delta / ref;
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}
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sum_delta2 += delta;
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sum_ref2 += ref;
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}
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double L2norm = sqrt(sum_delta2 / sum_ref2);
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printf("rel L2 = %E (max delta = %E)\n", L2norm, sqrt(max_delta_ref));
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bRetVal = (L2norm < 1e-6) ? true : false;
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printf(bRetVal ? "L2norm Error OK\n" : "L2norm Error too high!\n");
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printf("...shutting down\n");
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sdkDeleteTimer(&hTimer);
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checkCudaErrors(cufftDestroy(fftPlan));
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checkCudaErrors(cudaFree(d_KernelSpectrum));
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checkCudaErrors(cudaFree(d_DataSpectrum));
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checkCudaErrors(cudaFree(d_KernelSpectrum0));
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checkCudaErrors(cudaFree(d_DataSpectrum0));
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checkCudaErrors(cudaFree(d_PaddedKernel));
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checkCudaErrors(cudaFree(d_PaddedData));
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checkCudaErrors(cudaFree(d_Kernel));
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checkCudaErrors(cudaFree(d_Data));
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free(h_ResultGPU);
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free(h_ResultCPU);
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free(h_Kernel);
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free(h_Data);
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return bRetVal;
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}
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bool test2(void) {
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float *h_Data, *h_Kernel, *h_ResultCPU, *h_ResultGPU;
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float *d_Data, *d_Kernel, *d_PaddedData, *d_PaddedKernel;
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fComplex *d_DataSpectrum0, *d_KernelSpectrum0;
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cufftHandle fftPlan;
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bool bRetVal;
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StopWatchInterface *hTimer = NULL;
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sdkCreateTimer(&hTimer);
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printf("Testing updated custom R2C / C2R FFT-based convolution\n");
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const int kernelH = 7;
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const int kernelW = 6;
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const int kernelY = 3;
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const int kernelX = 4;
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const int dataH = 2000;
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const int dataW = 2000;
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const int fftH = snapTransformSize(dataH + kernelH - 1);
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const int fftW = snapTransformSize(dataW + kernelW - 1);
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printf("...allocating memory\n");
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h_Data = (float *)malloc(dataH * dataW * sizeof(float));
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h_Kernel = (float *)malloc(kernelH * kernelW * sizeof(float));
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h_ResultCPU = (float *)malloc(dataH * dataW * sizeof(float));
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h_ResultGPU = (float *)malloc(fftH * fftW * sizeof(float));
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checkCudaErrors(cudaMalloc((void **)&d_Data, dataH * dataW * sizeof(float)));
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checkCudaErrors(
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cudaMalloc((void **)&d_Kernel, kernelH * kernelW * sizeof(float)));
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checkCudaErrors(
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cudaMalloc((void **)&d_PaddedData, fftH * fftW * sizeof(float)));
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checkCudaErrors(
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cudaMalloc((void **)&d_PaddedKernel, fftH * fftW * sizeof(float)));
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checkCudaErrors(cudaMalloc((void **)&d_DataSpectrum0,
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fftH * (fftW / 2) * sizeof(fComplex)));
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checkCudaErrors(cudaMalloc((void **)&d_KernelSpectrum0,
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fftH * (fftW / 2) * sizeof(fComplex)));
|
|
|
|
printf("...generating random input data\n");
|
|
srand(2010);
|
|
|
|
for (int i = 0; i < dataH * dataW; i++) {
|
|
h_Data[i] = getRand();
|
|
}
|
|
|
|
for (int i = 0; i < kernelH * kernelW; i++) {
|
|
h_Kernel[i] = getRand();
|
|
}
|
|
|
|
printf("...creating C2C FFT plan for %i x %i\n", fftH, fftW / 2);
|
|
checkCudaErrors(cufftPlan2d(&fftPlan, fftH, fftW / 2, CUFFT_C2C));
|
|
|
|
printf("...uploading to GPU and padding convolution kernel and input data\n");
|
|
checkCudaErrors(cudaMemcpy(d_Data, h_Data, dataH * dataW * sizeof(float),
|
|
cudaMemcpyHostToDevice));
|
|
checkCudaErrors(cudaMemcpy(d_Kernel, h_Kernel,
|
|
kernelH * kernelW * sizeof(float),
|
|
cudaMemcpyHostToDevice));
|
|
checkCudaErrors(cudaMemset(d_PaddedData, 0, fftH * fftW * sizeof(float)));
|
|
checkCudaErrors(cudaMemset(d_PaddedKernel, 0, fftH * fftW * sizeof(float)));
|
|
|
|
padDataClampToBorder(d_PaddedData, d_Data, fftH, fftW, dataH, dataW, kernelH,
|
|
kernelW, kernelY, kernelX);
|
|
|
|
padKernel(d_PaddedKernel, d_Kernel, fftH, fftW, kernelH, kernelW, kernelY,
|
|
kernelX);
|
|
|
|
// CUFFT_INVERSE works just as well...
|
|
const int FFT_DIR = CUFFT_FORWARD;
|
|
|
|
// Not including kernel transformation into time measurement,
|
|
// since convolution kernel is not changed very frequently
|
|
printf("...transforming convolution kernel\n");
|
|
checkCudaErrors(cufftExecC2C(fftPlan, (cufftComplex *)d_PaddedKernel,
|
|
(cufftComplex *)d_KernelSpectrum0, FFT_DIR));
|
|
|
|
printf("...running GPU FFT convolution: ");
|
|
checkCudaErrors(cudaDeviceSynchronize());
|
|
sdkResetTimer(&hTimer);
|
|
sdkStartTimer(&hTimer);
|
|
|
|
checkCudaErrors(cufftExecC2C(fftPlan, (cufftComplex *)d_PaddedData,
|
|
(cufftComplex *)d_DataSpectrum0, FFT_DIR));
|
|
spProcess2D(d_DataSpectrum0, d_DataSpectrum0, d_KernelSpectrum0, fftH,
|
|
fftW / 2, FFT_DIR);
|
|
checkCudaErrors(cufftExecC2C(fftPlan, (cufftComplex *)d_DataSpectrum0,
|
|
(cufftComplex *)d_PaddedData, -FFT_DIR));
|
|
|
|
checkCudaErrors(cudaDeviceSynchronize());
|
|
sdkStopTimer(&hTimer);
|
|
double gpuTime = sdkGetTimerValue(&hTimer);
|
|
printf("%f MPix/s (%f ms)\n",
|
|
(double)dataH * (double)dataW * 1e-6 / (gpuTime * 0.001), gpuTime);
|
|
|
|
printf("...reading back GPU FFT results\n");
|
|
checkCudaErrors(cudaMemcpy(h_ResultGPU, d_PaddedData,
|
|
fftH * fftW * sizeof(float),
|
|
cudaMemcpyDeviceToHost));
|
|
|
|
printf("...running reference CPU convolution\n");
|
|
convolutionClampToBorderCPU(h_ResultCPU, h_Data, h_Kernel, dataH, dataW,
|
|
kernelH, kernelW, kernelY, kernelX);
|
|
|
|
printf("...comparing the results: ");
|
|
double sum_delta2 = 0;
|
|
double sum_ref2 = 0;
|
|
double max_delta_ref = 0;
|
|
|
|
for (int y = 0; y < dataH; y++) {
|
|
for (int x = 0; x < dataW; x++) {
|
|
double rCPU = (double)h_ResultCPU[y * dataW + x];
|
|
double rGPU = (double)h_ResultGPU[y * fftW + x];
|
|
double delta = (rCPU - rGPU) * (rCPU - rGPU);
|
|
double ref = rCPU * rCPU + rCPU * rCPU;
|
|
|
|
if ((delta / ref) > max_delta_ref) {
|
|
max_delta_ref = delta / ref;
|
|
}
|
|
|
|
sum_delta2 += delta;
|
|
sum_ref2 += ref;
|
|
}
|
|
}
|
|
|
|
double L2norm = sqrt(sum_delta2 / sum_ref2);
|
|
printf("rel L2 = %E (max delta = %E)\n", L2norm, sqrt(max_delta_ref));
|
|
bRetVal = (L2norm < 1e-6) ? true : false;
|
|
printf(bRetVal ? "L2norm Error OK\n" : "L2norm Error too high!\n");
|
|
|
|
printf("...shutting down\n");
|
|
sdkDeleteTimer(&hTimer);
|
|
checkCudaErrors(cufftDestroy(fftPlan));
|
|
|
|
checkCudaErrors(cudaFree(d_KernelSpectrum0));
|
|
checkCudaErrors(cudaFree(d_DataSpectrum0));
|
|
checkCudaErrors(cudaFree(d_PaddedKernel));
|
|
checkCudaErrors(cudaFree(d_PaddedData));
|
|
checkCudaErrors(cudaFree(d_Kernel));
|
|
checkCudaErrors(cudaFree(d_Data));
|
|
|
|
free(h_ResultGPU);
|
|
free(h_ResultCPU);
|
|
free(h_Kernel);
|
|
free(h_Data);
|
|
|
|
return bRetVal;
|
|
}
|
|
|
|
int main(int argc, char **argv) {
|
|
printf("[%s] - Starting...\n", argv[0]);
|
|
|
|
// Use command-line specified CUDA device, otherwise use device with highest
|
|
// Gflops/s
|
|
findCudaDevice(argc, (const char **)argv);
|
|
|
|
int nFailures = 0;
|
|
|
|
if (!test0()) {
|
|
nFailures++;
|
|
}
|
|
|
|
if (!test1()) {
|
|
nFailures++;
|
|
}
|
|
|
|
if (!test2()) {
|
|
nFailures++;
|
|
}
|
|
|
|
printf("Test Summary: %d errors\n", nFailures);
|
|
|
|
if (nFailures > 0) {
|
|
printf("Test failed!\n");
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
|
|
printf("Test passed\n");
|
|
exit(EXIT_SUCCESS);
|
|
}
|