cuFFT

简介

The cuFFT is a CUDA Fast Fourier Transform library consisting of two components: cuFFT and cuFFTW. The cuFFT library provides high performance on NVIDIA GPUs, and the cuFFTW library is a porting tool to use FFTW on NVIDIA GPUs.

cuFFT使用说明

思源一号上的cuFFT

  1. 先创建一个目录cuFFTtest并进入该目录:

mkdir cuFFTtest
cd cuFFTtest
  1. 在该目录下创建如下测试文件cuFFTtest.cu:

//  In this example a one-dimensional complex-to-complex transform is applied to the input data.
//Afterwards an inverse transform is performed on the computed frequency domain representation.

#include <complex>
#include <iostream>
#include <random>
#include <vector>

#include <cuda_runtime.h>
#include <cufftXt.h>

// #include "cufft_utils.h"

// CUDA API error checking
#define CUDA_RT_CALL(call)                                                     \
    {                                                                          \
        auto status = static_cast<cudaError_t>(call);                          \
        if (status != cudaSuccess)                                             \
            fprintf(stderr,                                                    \
                    "ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \
                    "with "                                                    \
                    "%s (%d).\n",                                              \
                    #call,                                                     \
                    __LINE__,                                                  \
                    __FILE__,                                                  \
                    cudaGetErrorString(status),                                \
                    status);                                                   \
    }

// cufft API error chekcing
#define CUFFT_CALL(call)                                                     \
    {                                                                        \
        auto status = static_cast<cufftResult>(call);                        \
        if (status != CUFFT_SUCCESS)                                         \
            fprintf(stderr,                                                  \
                    "ERROR: CUFFT call \"%s\" in line %d of file %s failed " \
                    "with "                                                  \
                    "code (%d).\n",                                          \
                    #call,                                                   \
                    __LINE__,                                                \
                    __FILE__,                                                \
                    status);                                                 \
    }

int main(int argc, char *argv[])
{
    cufftHandle plan;
    cudaStream_t stream = NULL;

    int n = 8;
    int batch_size = 2;
    int fft_size = batch_size * n;

    using scalar_type = float;
    using data_type = std::complex<scalar_type>;

    std::vector<data_type> data(fft_size);

    for (int i = 0; i < fft_size; i++)
    {
        data[i] = data_type(i, -i);
    }

    std::printf("Input array:\n");
    for (auto &i : data)
    {
        std::printf("%f + %fj\n", i.real(), i.imag());
    }
    std::printf("=====\n");

    cufftComplex *d_data = nullptr;

    CUFFT_CALL(cufftCreate(&plan));
    CUFFT_CALL(cufftPlan1d(&plan, data.size(), CUFFT_C2C, batch_size));

    CUDA_RT_CALL(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));
    CUFFT_CALL(cufftSetStream(plan, stream));

    // Create device data arrays
    CUDA_RT_CALL(cudaMalloc(reinterpret_cast<void **>(&d_data), sizeof(data_type) * data.size()));
    CUDA_RT_CALL(cudaMemcpyAsync(d_data, data.data(), sizeof(data_type) * data.size(), cudaMemcpyHostToDevice, stream));

    /*
     * Note:
     *  Identical pointers to data and output arrays implies in-place transformation
     */
    CUFFT_CALL(cufftExecC2C(plan, d_data, d_data, CUFFT_FORWARD));
    CUFFT_CALL(cufftExecC2C(plan, d_data, d_data, CUFFT_INVERSE));

    CUDA_RT_CALL(cudaMemcpyAsync(data.data(), d_data, sizeof(data_type) * data.size(), cudaMemcpyDeviceToHost, stream));

    CUDA_RT_CALL(cudaStreamSynchronize(stream));

    std::printf("Output array:\n");
    for (auto &i : data)
    {
        std::printf("%f + %fj\n", i.real(), i.imag());
    }
    std::printf("=====\n");

    /* free resources */
    CUDA_RT_CALL(cudaFree(d_data))

    CUFFT_CALL(cufftDestroy(plan));

    CUDA_RT_CALL(cudaStreamDestroy(stream));

    CUDA_RT_CALL(cudaDeviceReset());

    return EXIT_SUCCESS;
}
  1. 在该目录下创建如下作业提交脚本cuFFTtest.slurm:

#!/bin/bash

#SBATCH --job-name=cuFFTtest        # 作业名
#SBATCH --partition=a100             # a100 队列
#SBATCH -N 1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=1            # 1:1 的 GPU:CPU 配比
#SBATCH --gres=gpu:1                 # 1 块 GPU
#SBATCH --output=%j.out
#SBATCH --error=%j.err

module load cuda/11.5.0
module load gcc/11.2.0

nvcc cuFFTtest.cu -o cuFFTtest -lcufft
./cuFFTtest
  1. 使用如下命令提交作业:

sbatch cuFFTtest.slurm
  1. 作业完成后在.out文件中可看到如下结果:

Input array:
0.000000 + 0.000000j
1.000000 + -1.000000j
2.000000 + -2.000000j
3.000000 + -3.000000j
4.000000 + -4.000000j
5.000000 + -5.000000j
6.000000 + -6.000000j
7.000000 + -7.000000j
8.000000 + -8.000000j
9.000000 + -9.000000j
10.000000 + -10.000000j
11.000000 + -11.000000j
12.000000 + -12.000000j
13.000000 + -13.000000j
14.000000 + -14.000000j
15.000000 + -15.000000j
=====
Output array:
0.000004 + 0.000000j
16.000015 + -16.000004j
32.000004 + -32.000004j
48.000004 + -48.000004j
64.000000 + -64.000000j
80.000000 + -80.000008j
96.000000 + -96.000015j
112.000015 + -112.000000j
128.000000 + -128.000000j
144.000000 + -144.000000j
160.000000 + -160.000000j
176.000000 + -176.000000j
192.000000 + -192.000000j
208.000000 + -208.000000j
224.000000 + -223.999985j
239.999985 + -240.000000j
=====

pi2.0上的cuFFT

  1. 此步骤和上文完全相同;

  2. 此步骤和上文完全相同;

  3. 在该目录下创建如下作业提交脚本cuFFTtest.slurm:

#!/bin/bash

#SBATCH --job-name=cuFFTtest        # 作业名
#SBATCH --partition=dgx2             # dgx2 队列
#SBATCH -N 1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=1            # 1:1 的 GPU:CPU 配比
#SBATCH --gres=gpu:1                 # 1 块 GPU
#SBATCH --output=%j.out
#SBATCH --error=%j.err

module load cuda/11.6.2-gcc-8.3.0
module load gcc/8.3.0

nvcc cuFFTtest.cu -o cuFFTtest -lcufft
./cuFFTtest
  1. 使用如下命令提交作业:

sbatch cuFFTtest.slurm
  1. 作业完成后在.out文件中可看到如下结果:

Input array:
0.000000 + 0.000000j
1.000000 + -1.000000j
2.000000 + -2.000000j
3.000000 + -3.000000j
4.000000 + -4.000000j
5.000000 + -5.000000j
6.000000 + -6.000000j
7.000000 + -7.000000j
8.000000 + -8.000000j
9.000000 + -9.000000j
10.000000 + -10.000000j
11.000000 + -11.000000j
12.000000 + -12.000000j
13.000000 + -13.000000j
14.000000 + -14.000000j
15.000000 + -15.000000j
=====
Output array:
0.000004 + 0.000000j
16.000015 + -16.000004j
32.000004 + -32.000004j
48.000004 + -48.000004j
64.000000 + -64.000000j
80.000000 + -80.000008j
96.000000 + -96.000015j
112.000015 + -112.000000j
128.000000 + -128.000000j
144.000000 + -144.000000j
160.000000 + -160.000000j
176.000000 + -176.000000j
192.000000 + -192.000000j
208.000000 + -208.000000j
224.000000 + -223.999985j
239.999985 + -240.000000j
=====

参考资料


最后更新: 2024 年 04 月 08 日