cuda-samples/Samples/5_Domain_Specific/fastWalshTransform/fastWalshTransform_kernel.cuh

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#ifndef FWT_KERNEL_CUH
#define FWT_KERNEL_CUH
#ifndef fwt_kernel_cuh
#define fwt_kernel_cuh

#include <cooperative_groups.h>

namespace cg = cooperative_groups;

///////////////////////////////////////////////////////////////////////////////
// Elementary(for vectors less than elementary size) in-shared memory
// combined radix-2 + radix-4 Fast Walsh Transform
///////////////////////////////////////////////////////////////////////////////
#define ELEMENTARY_LOG2SIZE 11

__global__ void fwtBatch1Kernel(float *d_Output, float *d_Input, int log2N) {
  // Handle to thread block group
  cg::thread_block cta = cg::this_thread_block();
  const int N = 1 << log2N;
  const int base = blockIdx.x << log2N;

  //(2 ** 11) * 4 bytes == 8KB -- maximum s_data[] size for G80
  extern __shared__ float s_data[];
  float *d_Src = d_Input + base;
  float *d_Dst = d_Output + base;

  for (int pos = threadIdx.x; pos < N; pos += blockDim.x) {
    s_data[pos] = d_Src[pos];
  }

  // Main radix-4 stages
  const int pos = threadIdx.x;

  for (int stride = N >> 2; stride > 0; stride >>= 2) {
    int lo = pos & (stride - 1);
    int i0 = ((pos - lo) << 2) + lo;
    int i1 = i0 + stride;
    int i2 = i1 + stride;
    int i3 = i2 + stride;

    cg::sync(cta);
    float D0 = s_data[i0];
    float D1 = s_data[i1];
    float D2 = s_data[i2];
    float D3 = s_data[i3];

    float T;
    T = D0;
    D0 = D0 + D2;
    D2 = T - D2;
    T = D1;
    D1 = D1 + D3;
    D3 = T - D3;
    T = D0;
    s_data[i0] = D0 + D1;
    s_data[i1] = T - D1;
    T = D2;
    s_data[i2] = D2 + D3;
    s_data[i3] = T - D3;
  }

  // Do single radix-2 stage for odd power of two
  if (log2N & 1) {
    cg::sync(cta);

    for (int pos = threadIdx.x; pos < N / 2; pos += blockDim.x) {
      int i0 = pos << 1;
      int i1 = i0 + 1;

      float D0 = s_data[i0];
      float D1 = s_data[i1];
      s_data[i0] = D0 + D1;
      s_data[i1] = D0 - D1;
    }
  }

  cg::sync(cta);

  for (int pos = threadIdx.x; pos < N; pos += blockDim.x) {
    d_Dst[pos] = s_data[pos];
  }
}

////////////////////////////////////////////////////////////////////////////////
// Single in-global memory radix-4 Fast Walsh Transform pass
// (for strides exceeding elementary vector size)
////////////////////////////////////////////////////////////////////////////////
__global__ void fwtBatch2Kernel(float *d_Output, float *d_Input, int stride) {
  const int pos = blockIdx.x * blockDim.x + threadIdx.x;
  const int N = blockDim.x * gridDim.x * 4;

  float *d_Src = d_Input + blockIdx.y * N;
  float *d_Dst = d_Output + blockIdx.y * N;

  int lo = pos & (stride - 1);
  int i0 = ((pos - lo) << 2) + lo;
  int i1 = i0 + stride;
  int i2 = i1 + stride;
  int i3 = i2 + stride;

  float D0 = d_Src[i0];
  float D1 = d_Src[i1];
  float D2 = d_Src[i2];
  float D3 = d_Src[i3];

  float T;
  T = D0;
  D0 = D0 + D2;
  D2 = T - D2;
  T = D1;
  D1 = D1 + D3;
  D3 = T - D3;
  T = D0;
  d_Dst[i0] = D0 + D1;
  d_Dst[i1] = T - D1;
  T = D2;
  d_Dst[i2] = D2 + D3;
  d_Dst[i3] = T - D3;
}

////////////////////////////////////////////////////////////////////////////////
// Put everything together: batched Fast Walsh Transform CPU front-end
////////////////////////////////////////////////////////////////////////////////
void fwtBatchGPU(float *d_Data, int M, int log2N) {
  const int THREAD_N = 256;

  int N = 1 << log2N;
  dim3 grid((1 << log2N) / (4 * THREAD_N), M, 1);

  for (; log2N > ELEMENTARY_LOG2SIZE; log2N -= 2, N >>= 2, M <<= 2) {
    fwtBatch2Kernel<<<grid, THREAD_N>>>(d_Data, d_Data, N / 4);
    getLastCudaError("fwtBatch2Kernel() execution failed\n");
  }

  fwtBatch1Kernel<<<M, N / 4, N * sizeof(float)>>>(d_Data, d_Data, log2N);
  getLastCudaError("fwtBatch1Kernel() execution failed\n");
}

////////////////////////////////////////////////////////////////////////////////
// Modulate two arrays
////////////////////////////////////////////////////////////////////////////////
__global__ void modulateKernel(float *d_A, float *d_B, int N) {
  int tid = blockIdx.x * blockDim.x + threadIdx.x;
  int numThreads = blockDim.x * gridDim.x;
  float rcpN = 1.0f / (float)N;

  for (int pos = tid; pos < N; pos += numThreads) {
    d_A[pos] *= d_B[pos] * rcpN;
  }
}

// Interface to modulateKernel()
void modulateGPU(float *d_A, float *d_B, int N) {
  modulateKernel<<<128, 256>>>(d_A, d_B, N);
}

#endif
#endif