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我想我在这里不明白一些非常关键的东西。以下代码尝试使用 FFT 方法计算两个信号的卷积。我遇到的问题是有时我会得到错误/奇怪的输出。当我尝试在 main 中显式运行卷积函数的每个步骤（在第 104 行）时，它可以工作。现在，如果我通过卷积函数正常运行代码，它就可以工作了！在得到我期望的输出后，我无法重新创建让我得到错误答案的设置。我不知道这怎么会发生。

编辑 - 代码块包含数据。

#include <stdio.h>
#include <stdlib.h>
#include <math.h>

#include <cuda_runtime.h>
#include <cufft.h>
#include <cuda.h>

typedef enum signaltype {REAL, COMPLEX} signal;

typedef float2 Complex;

void
printData(Complex *a, int size, char *msg) {

  if (msg == "") printf("\n");
  else printf("%s\n", msg);

  for (int i = 0; i < size; i++)
    printf("%f %f\n", a[i].x, a[i].y);
}

void
normData(Complex *a, int size, float norm) {

  for (int i = 0; i < size; i++) {
    a[i].x /= norm;
    a[i].y /= norm;
  }
}

// flag = 1 for real signals.
void
randomFill(Complex *h_signal, int size, int flag) {

  // Real signal.
  if (flag == REAL) {
    for (int i = 0; i < size; i++) {
      h_signal[i].x = rand() / (float) RAND_MAX;
      h_signal[i].y = 0;
    }
  }
}

// FFT a signal that's on the _DEVICE_.
void
signalFFT(Complex *d_signal, int signal_size) {

  // Handle type used to store and execute CUFFT plans.
  // Essentially allocates the resouecwes and sort of interns
  // them.

  cufftHandle plan;
  if (cufftPlan1d(&plan, signal_size, CUFFT_C2C, 1) != CUFFT_SUCCESS) {
    printf("Failed to plan FFT\n");
    exit(0);
  }

  // Execute the plan.
  if (cufftExecC2C(plan, (cufftComplex *) d_signal, (cufftComplex *) d_signal, CUFFT_FORWARD) != CUFFT_SUCCESS) {
    printf ("Failed Executing FFT\n");
    exit(0);
  }

}


// Reverse of the signalFFT(.) function.
void
signalIFFT(Complex *d_signal, int signal_size) {

  cufftHandle plan;
  if (cufftPlan1d(&plan, signal_size, CUFFT_C2C, 1) != CUFFT_SUCCESS) {
    printf("Failed to plan IFFT\n");
    exit(0);
  }

  if (cufftExecC2C(plan, (cufftComplex *) d_signal, (cufftComplex *) d_signal, CUFFT_INVERSE) != CUFFT_SUCCESS) {
    printf ("Failed Executing FFT\n");
    exit(0);
  }
}


// Pointwise Multiplication Kernel.
__global__ void
pwProd(Complex *signal1, int size1, Complex *signal2, int size2) {

  int threadsPerBlock, blockId, globalIdx;

  threadsPerBlock = blockDim.x * blockDim.y;
  blockId = blockIdx.x + (blockIdx.y * gridDim.x);
  globalIdx = (blockId * threadsPerBlock) + threadIdx.x + (threadIdx.y * blockDim.x);

  if (globalIdx <= size1) {

      signal1[globalIdx].x = (signal1[globalIdx].x * signal2[globalIdx].x - signal1[globalIdx].y * signal2[globalIdx].y);
      signal1[globalIdx].y = (signal1[globalIdx].x * signal2[globalIdx].y + signal1[globalIdx].y * signal2[globalIdx].x);
    }

}

void
cudaConvolution(Complex *d_signal1, int size1, Complex *d_signal2,
                int size2, dim3 blockSize, dim3 gridSize) {

  signalFFT(d_signal1, size1);
  signalFFT(d_signal2, size2);

  pwProd<<<gridSize, blockSize>>>(d_signal1, size1, d_signal2, size2);

  //signalIFFT(d_signal1, size1);

}


int allocateAndPad(Complex **a, int s1, Complex **b, int s2) {

  int oldsize, newsize, i;

  newsize = s1 + s2 - 1;

  while (!((newsize != 0) && !(newsize & (newsize - 1)))) {
    newsize++;
  }

  oldsize = s1;
  *a = (Complex *) malloc(sizeof(Complex) * newsize);
  for (i = oldsize; i < newsize; i++) {
    (*a)[i].x = 0;
    (*a)[i].y = 0;
  }

  oldsize = s2;
  *b = (Complex *) malloc(sizeof(Complex) * newsize);
  for (i = oldsize; i < newsize; i++) {
    (*b)[i].x = 0;
    (*b)[i].y = 0;
  }

  return newsize;
}

int main()
{

  Complex *h1, *h2, *d1, *d2;

  int s1, s2, newsize, i, dim;

  int deviceCount;
  cudaError_t e = cudaGetDeviceCount(&deviceCount);
  if (e != cudaSuccess) {
    return -1;
  }

  dim = 1;

  s1 = 16;
  s2 = 16;

  for (i = 0; i < dim; i++)  {

      newsize = allocateAndPad(&h1, s1, &h2, s2);

      /*h1 = (Complex *) malloc(sizeof(Complex) * s1);
      h2 = (Complex *) malloc(sizeof(Complex) * s2);
      newsize = 16;*/

      randomFill(h1, s1, REAL);
      randomFill(h2, s2, REAL);

      // Kernel Block and Grid Size.
      const dim3 blockSize(16, 16, 1);
      const dim3 gridSize(newsize / 16 + 1, newsize / 16 + 1, 1);

      printData(h1, newsize, "H Signal 1");
      printData(h2, newsize, "H Signal 2");

      cudaMalloc(&d1, sizeof(Complex) * newsize);
      cudaMalloc(&d2, sizeof(Complex) * newsize);
      cudaMemcpy(d1, h1, sizeof(Complex) * newsize, cudaMemcpyHostToDevice);
      cudaMemcpy(d2, h2, sizeof(Complex) * newsize, cudaMemcpyHostToDevice);

      cudaConvolution(d1, newsize, d2, newsize, blockSize, gridSize);

      // Explicit code run below,

      /*signalFFT(d1, newsize);
      cudaMemcpy(h1, d1, sizeof(Complex) * newsize, cudaMemcpyDeviceToHost);
      printData(h1, newsize, "1 FFT");
      cudaMemcpy(d1, h1, sizeof(Complex) * newsize, cudaMemcpyHostToDevice);
      signalFFT(d2, newsize);
      cudaMemcpy(h2, d2, sizeof(Complex) * newsize, cudaMemcpyDeviceToHost);
      printData(h2, newsize, "2 FFT");
      cudaMemcpy(d2, h2, sizeof(Complex) * newsize, cudaMemcpyHostToDevice);

      pwProd<<<gridSize, blockSize>>>(d1, newsize, d2, newsize);

      signalIFFT(d1, newsize);*/

      cudaDeviceSynchronize();

      cudaMemcpy(h1, d1, sizeof(Complex) * newsize, cudaMemcpyDeviceToHost);

      //normData(h1, newsize, newsize);

      printData(h1, newsize, "PwProd");

      free(h1); free(h2);
      cudaFree(d1); cudaFree(d2);

      cudaDeviceReset();
  }

  return 0;
}


EDIT: Required Output Data
0.840188 0.000000
0.394383 0.000000
0.783099 0.000000
0.798440 0.000000
0.911647 0.000000
0.197551 0.000000
0.335223 0.000000
0.768230 0.000000
0.277775 0.000000
0.553970 0.000000
0.477397 0.000000
0.628871 0.000000
0.364784 0.000000
0.513401 0.000000
0.952230 0.000000
0.916195 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000 H Signal 2
0.635712 0.000000
0.717297 0.000000
0.141603 0.000000
0.606969 0.000000
0.016301 0.000000
0.242887 0.000000
0.137232 0.000000
0.804177 0.000000
0.156679 0.000000
0.400944 0.000000
0.129790 0.000000
0.108809 0.000000
0.998924 0.000000
0.218257 0.000000
0.512932 0.000000
0.839112 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000
0.000000 0.000000 PwProd
64.765198 0.000000
-20.097927 72.754028
1.797580 1.074046
-5.184547 7.412243
0.148326 0.121253
-3.457163 3.253345
0.834668 -0.752979
-0.414450 0.328347
-1.268492 0.297919
1.634082 -2.054814
0.542893 0.087469
0.244198 -1.392576
0.680159 -0.110084
0.938037 1.743742
1.318125 -2.269666
-1.448638 1.534995
-0.207152 -0.000000
-1.448638 -1.534995
1.318125 2.269666
0.938037 -1.743742
0.680159 0.110084
0.244198 1.392576
0.542893 -0.087469
1.634082 2.054814
-1.268492 -0.297919
-0.414450 -0.328347
0.834668 0.752980
-3.457164 -3.253347
0.148326 -0.121253
-5.184546 -7.412243
1.797580 -1.074046
-20.097923 -72.754013

错误输出将 pwprod 的另一半（最后 16 行）作为 H 信号 2 数据而没有填充。

4

1 回答 1

3

您应该对所有 cuda API 调用和内核调用进行cuda 错误检查（您已经对 cufft API 调用进行了错误检查）。

另一个有用的工具是cuda-memcheck. 当我通过 cuda-memcheck 运行你的代码时，我得到了一些错误，其中第一个是指向你的内核pwProd：

========= Invalid __global__ read of size 8
=========     at 0x00000088 in pwProd(float2*, int, float2*, int)
=========     by thread (0,2,0) in block (0,0,0)
=========     Address 0x400200300 is out of bounds
=========     Saved host backtrace up to driver entry point at kernel launch time
=========     Host Frame:/usr/lib64/libcuda.so (cuLaunchKernel + 0x3dc) [0xc9edc]
=========     Host Frame:/usr/local/cuda/lib64/libcudart.so.5.0 [0xf513]
=========     Host Frame:/usr/local/cuda/lib64/libcudart.so.5.0 (cudaLaunch + 0x183) [0x30f13]
=========     Host Frame:./t171 [0x13e1]
=========     Host Frame:./t171 (__gxx_personality_v0 + 0x2d2) [0xdea]
=========     Host Frame:./t171 (__gxx_personality_v0 + 0x2fd) [0xe15]
=========     Host Frame:./t171 [0x108b]
=========     Host Frame:./t171 [0x1322]
=========     Host Frame:/lib64/libc.so.6 (__libc_start_main + 0xf4) [0x1d994]
=========     Host Frame:./t171 (__gxx_personality_v0 + 0x51) [0xb69]

然后我注意到内核线程检查是这样的：

if (globalIdx <= size1) {

我认为应该是这样的：

if (globalIdx < size1) {

当我进行更改时，所有 cuda-memcheck 错误都消失了。

于 2013-06-01T01:07:55.837 回答