cuda-samples/Samples/convolutionSeparable/convolutionSeparable.cu

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#include <assert.h>
#include <helper_cuda.h>
#include <cooperative_groups.h>

namespace cg = cooperative_groups;
#include "convolutionSeparable_common.h"

////////////////////////////////////////////////////////////////////////////////
// Convolution kernel storage
////////////////////////////////////////////////////////////////////////////////
__constant__ float c_Kernel[KERNEL_LENGTH];

extern "C" void setConvolutionKernel(float *h_Kernel) {
  cudaMemcpyToSymbol(c_Kernel, h_Kernel, KERNEL_LENGTH * sizeof(float));
}

////////////////////////////////////////////////////////////////////////////////
// Row convolution filter
////////////////////////////////////////////////////////////////////////////////
#define ROWS_BLOCKDIM_X 16
#define ROWS_BLOCKDIM_Y 4
#define ROWS_RESULT_STEPS 8
#define ROWS_HALO_STEPS 1

__global__ void convolutionRowsKernel(float *d_Dst, float *d_Src, int imageW,
                                      int imageH, int pitch) {
  // Handle to thread block group
  cg::thread_block cta = cg::this_thread_block();
  __shared__ float
      s_Data[ROWS_BLOCKDIM_Y][(ROWS_RESULT_STEPS + 2 * ROWS_HALO_STEPS) *
                              ROWS_BLOCKDIM_X];

  // Offset to the left halo edge
  const int baseX =
      (blockIdx.x * ROWS_RESULT_STEPS - ROWS_HALO_STEPS) * ROWS_BLOCKDIM_X +
      threadIdx.x;
  const int baseY = blockIdx.y * ROWS_BLOCKDIM_Y + threadIdx.y;

  d_Src += baseY * pitch + baseX;
  d_Dst += baseY * pitch + baseX;

// Load main data
#pragma unroll

  for (int i = ROWS_HALO_STEPS; i < ROWS_HALO_STEPS + ROWS_RESULT_STEPS; i++) {
    s_Data[threadIdx.y][threadIdx.x + i * ROWS_BLOCKDIM_X] =
        d_Src[i * ROWS_BLOCKDIM_X];
  }

// Load left halo
#pragma unroll

  for (int i = 0; i < ROWS_HALO_STEPS; i++) {
    s_Data[threadIdx.y][threadIdx.x + i * ROWS_BLOCKDIM_X] =
        (baseX >= -i * ROWS_BLOCKDIM_X) ? d_Src[i * ROWS_BLOCKDIM_X] : 0;
  }

// Load right halo
#pragma unroll

  for (int i = ROWS_HALO_STEPS + ROWS_RESULT_STEPS;
       i < ROWS_HALO_STEPS + ROWS_RESULT_STEPS + ROWS_HALO_STEPS; i++) {
    s_Data[threadIdx.y][threadIdx.x + i * ROWS_BLOCKDIM_X] =
        (imageW - baseX > i * ROWS_BLOCKDIM_X) ? d_Src[i * ROWS_BLOCKDIM_X] : 0;
  }

  // Compute and store results
  cg::sync(cta);
#pragma unroll

  for (int i = ROWS_HALO_STEPS; i < ROWS_HALO_STEPS + ROWS_RESULT_STEPS; i++) {
    float sum = 0;

#pragma unroll

    for (int j = -KERNEL_RADIUS; j <= KERNEL_RADIUS; j++) {
      sum += c_Kernel[KERNEL_RADIUS - j] *
             s_Data[threadIdx.y][threadIdx.x + i * ROWS_BLOCKDIM_X + j];
    }

    d_Dst[i * ROWS_BLOCKDIM_X] = sum;
  }
}

extern "C" void convolutionRowsGPU(float *d_Dst, float *d_Src, int imageW,
                                   int imageH) {
  assert(ROWS_BLOCKDIM_X * ROWS_HALO_STEPS >= KERNEL_RADIUS);
  assert(imageW % (ROWS_RESULT_STEPS * ROWS_BLOCKDIM_X) == 0);
  assert(imageH % ROWS_BLOCKDIM_Y == 0);

  dim3 blocks(imageW / (ROWS_RESULT_STEPS * ROWS_BLOCKDIM_X),
              imageH / ROWS_BLOCKDIM_Y);
  dim3 threads(ROWS_BLOCKDIM_X, ROWS_BLOCKDIM_Y);

  convolutionRowsKernel<<<blocks, threads>>>(d_Dst, d_Src, imageW, imageH,
                                             imageW);
  getLastCudaError("convolutionRowsKernel() execution failed\n");
}

////////////////////////////////////////////////////////////////////////////////
// Column convolution filter
////////////////////////////////////////////////////////////////////////////////
#define COLUMNS_BLOCKDIM_X 16
#define COLUMNS_BLOCKDIM_Y 8
#define COLUMNS_RESULT_STEPS 8
#define COLUMNS_HALO_STEPS 1

__global__ void convolutionColumnsKernel(float *d_Dst, float *d_Src, int imageW,
                                         int imageH, int pitch) {
  // Handle to thread block group
  cg::thread_block cta = cg::this_thread_block();
  __shared__ float s_Data[COLUMNS_BLOCKDIM_X][(COLUMNS_RESULT_STEPS +
                                               2 * COLUMNS_HALO_STEPS) *
                                                  COLUMNS_BLOCKDIM_Y +
                                              1];

  // Offset to the upper halo edge
  const int baseX = blockIdx.x * COLUMNS_BLOCKDIM_X + threadIdx.x;
  const int baseY = (blockIdx.y * COLUMNS_RESULT_STEPS - COLUMNS_HALO_STEPS) *
                        COLUMNS_BLOCKDIM_Y +
                    threadIdx.y;
  d_Src += baseY * pitch + baseX;
  d_Dst += baseY * pitch + baseX;

// Main data
#pragma unroll

  for (int i = COLUMNS_HALO_STEPS;
       i < COLUMNS_HALO_STEPS + COLUMNS_RESULT_STEPS; i++) {
    s_Data[threadIdx.x][threadIdx.y + i * COLUMNS_BLOCKDIM_Y] =
        d_Src[i * COLUMNS_BLOCKDIM_Y * pitch];
  }

// Upper halo
#pragma unroll

  for (int i = 0; i < COLUMNS_HALO_STEPS; i++) {
    s_Data[threadIdx.x][threadIdx.y + i * COLUMNS_BLOCKDIM_Y] =
        (baseY >= -i * COLUMNS_BLOCKDIM_Y)
            ? d_Src[i * COLUMNS_BLOCKDIM_Y * pitch]
            : 0;
  }

// Lower halo
#pragma unroll

  for (int i = COLUMNS_HALO_STEPS + COLUMNS_RESULT_STEPS;
       i < COLUMNS_HALO_STEPS + COLUMNS_RESULT_STEPS + COLUMNS_HALO_STEPS;
       i++) {
    s_Data[threadIdx.x][threadIdx.y + i * COLUMNS_BLOCKDIM_Y] =
        (imageH - baseY > i * COLUMNS_BLOCKDIM_Y)
            ? d_Src[i * COLUMNS_BLOCKDIM_Y * pitch]
            : 0;
  }

  // Compute and store results
  cg::sync(cta);
#pragma unroll

  for (int i = COLUMNS_HALO_STEPS;
       i < COLUMNS_HALO_STEPS + COLUMNS_RESULT_STEPS; i++) {
    float sum = 0;
#pragma unroll

    for (int j = -KERNEL_RADIUS; j <= KERNEL_RADIUS; j++) {
      sum += c_Kernel[KERNEL_RADIUS - j] *
             s_Data[threadIdx.x][threadIdx.y + i * COLUMNS_BLOCKDIM_Y + j];
    }

    d_Dst[i * COLUMNS_BLOCKDIM_Y * pitch] = sum;
  }
}

extern "C" void convolutionColumnsGPU(float *d_Dst, float *d_Src, int imageW,
                                      int imageH) {
  assert(COLUMNS_BLOCKDIM_Y * COLUMNS_HALO_STEPS >= KERNEL_RADIUS);
  assert(imageW % COLUMNS_BLOCKDIM_X == 0);
  assert(imageH % (COLUMNS_RESULT_STEPS * COLUMNS_BLOCKDIM_Y) == 0);

  dim3 blocks(imageW / COLUMNS_BLOCKDIM_X,
              imageH / (COLUMNS_RESULT_STEPS * COLUMNS_BLOCKDIM_Y));
  dim3 threads(COLUMNS_BLOCKDIM_X, COLUMNS_BLOCKDIM_Y);

  convolutionColumnsKernel<<<blocks, threads>>>(d_Dst, d_Src, imageW, imageH,
                                                imageW);
  getLastCudaError("convolutionColumnsKernel() execution failed\n");
}