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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.
*/
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <helper_cuda.h>
#include "convolutionTexture_common.h"
////////////////////////////////////////////////////////////////////////////////
// GPU-specific defines
////////////////////////////////////////////////////////////////////////////////
// Maps to a single instruction on G8x / G9x / G10x
#define IMAD(a, b, c) (__mul24((a), (b)) + (c))
// Use unrolled innermost convolution loop
#define UNROLL_INNER 1
// Round a / b to nearest higher integer value
inline int iDivUp(int a, int b) { return (a % b != 0) ? (a / b + 1) : (a / b); }
// Align a to nearest higher multiple of b
inline int iAlignUp(int a, int b) { return (a % b != 0) ? (a - a % b + b) : a; }
////////////////////////////////////////////////////////////////////////////////
// Convolution kernel and input array storage
////////////////////////////////////////////////////////////////////////////////
__constant__ float c_Kernel[KERNEL_LENGTH];
extern "C" void setConvolutionKernel(float *h_Kernel) {
cudaMemcpyToSymbol(c_Kernel, h_Kernel, KERNEL_LENGTH * sizeof(float));
}
////////////////////////////////////////////////////////////////////////////////
// Loop unrolling templates, needed for best performance
////////////////////////////////////////////////////////////////////////////////
template <int i>
__device__ float convolutionRow(float x, float y, cudaTextureObject_t texSrc) {
return tex2D<float>(texSrc, x + (float)(KERNEL_RADIUS - i), y) * c_Kernel[i] +
convolutionRow<i - 1>(x, y, texSrc);
}
template <>
__device__ float convolutionRow<-1>(float x, float y,
cudaTextureObject_t texSrc) {
return 0;
}
template <int i>
__device__ float convolutionColumn(float x, float y,
cudaTextureObject_t texSrc) {
return tex2D<float>(texSrc, x, y + (float)(KERNEL_RADIUS - i)) * c_Kernel[i] +
convolutionColumn<i - 1>(x, y, texSrc);
}
template <>
__device__ float convolutionColumn<-1>(float x, float y,
cudaTextureObject_t texSrc) {
return 0;
}
////////////////////////////////////////////////////////////////////////////////
// Row convolution filter
////////////////////////////////////////////////////////////////////////////////
__global__ void convolutionRowsKernel(float *d_Dst, int imageW, int imageH,
cudaTextureObject_t texSrc) {
const int ix = IMAD(blockDim.x, blockIdx.x, threadIdx.x);
const int iy = IMAD(blockDim.y, blockIdx.y, threadIdx.y);
const float x = (float)ix + 0.5f;
const float y = (float)iy + 0.5f;
if (ix >= imageW || iy >= imageH) {
return;
}
float sum = 0;
#if (UNROLL_INNER)
sum = convolutionRow<2 * KERNEL_RADIUS>(x, y, texSrc);
#else
for (int k = -KERNEL_RADIUS; k <= KERNEL_RADIUS; k++) {
sum += tex2D<float>(texSrc, x + (float)k, y) * c_Kernel[KERNEL_RADIUS - k];
}
#endif
d_Dst[IMAD(iy, imageW, ix)] = sum;
}
extern "C" void convolutionRowsGPU(float *d_Dst, cudaArray *a_Src, int imageW,
int imageH, cudaTextureObject_t texSrc) {
dim3 threads(16, 12);
dim3 blocks(iDivUp(imageW, threads.x), iDivUp(imageH, threads.y));
convolutionRowsKernel<<<blocks, threads>>>(d_Dst, imageW, imageH, texSrc);
getLastCudaError("convolutionRowsKernel() execution failed\n");
}
////////////////////////////////////////////////////////////////////////////////
// Column convolution filter
////////////////////////////////////////////////////////////////////////////////
__global__ void convolutionColumnsKernel(float *d_Dst, int imageW, int imageH,
cudaTextureObject_t texSrc) {
const int ix = IMAD(blockDim.x, blockIdx.x, threadIdx.x);
const int iy = IMAD(blockDim.y, blockIdx.y, threadIdx.y);
const float x = (float)ix + 0.5f;
const float y = (float)iy + 0.5f;
if (ix >= imageW || iy >= imageH) {
return;
}
float sum = 0;
#if (UNROLL_INNER)
sum = convolutionColumn<2 * KERNEL_RADIUS>(x, y, texSrc);
#else
for (int k = -KERNEL_RADIUS; k <= KERNEL_RADIUS; k++) {
sum += tex2D<float>(texSrc, x, y + (float)k) * c_Kernel[KERNEL_RADIUS - k];
}
#endif
d_Dst[IMAD(iy, imageW, ix)] = sum;
}
extern "C" void convolutionColumnsGPU(float *d_Dst, cudaArray *a_Src,
int imageW, int imageH,
cudaTextureObject_t texSrc) {
dim3 threads(16, 12);
dim3 blocks(iDivUp(imageW, threads.x), iDivUp(imageH, threads.y));
convolutionColumnsKernel<<<blocks, threads>>>(d_Dst, imageW, imageH, texSrc);
getLastCudaError("convolutionColumnsKernel() execution failed\n");
}
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