Multithreads of fftw on 2D matrix's transform

ghz 8months ago ⋅ 84 views

I wonder the principle of multithreading fftw on 2D matrix, if fftw uses multithreads to transform every row of the matrix.If not, i want to use multithreads to conduct 1D fft on the rows of matrix in parallel to accelerate the transform.

I have try many fft libraries, and fftw should be the most known and stable one to conduct fft on 2D matrix.However, I want to know if the fftw's multithreads in its document has accomplish the parallel transform on every row of the matrix.

Answers

FFTW (Fastest Fourier Transform in the West) is a highly optimized library for computing the Discrete Fourier Transform (DFT) and its inverse (IDFT). It provides excellent performance across a wide range of platforms and input sizes.

In FFTW, multithreading support can be enabled by configuring the library appropriately during its compilation. When multithreading is enabled, FFTW will parallelize the computation of the DFT across multiple threads, typically by distributing the computation across multiple cores or processors.

However, FFTW's multithreading support does not directly parallelize the computation of the DFT along the rows of a 2D matrix. Instead, it parallelizes the computation across different parts of the transform (e.g., different FFTs or parts of an FFT).

If you want to parallelize the computation of the 1D FFT along the rows of a 2D matrix, you would need to implement this parallelization yourself. One approach is to use a parallel programming framework or library (e.g., OpenMP, pthreads) to distribute the computation of the 1D FFT across multiple threads.

Here's a high-level approach to parallelize the computation of the 1D FFT along the rows of a 2D matrix using OpenMP as an example:

#include <omp.h>
#include <fftw3.h>

void compute_fft_2d(fftw_complex* matrix, int nrows, int ncols) {
    #pragma omp parallel for
    for (int i = 0; i < nrows; ++i) {
        fftw_complex* row = matrix + i * ncols;
        fftw_plan plan = fftw_plan_dft_1d(ncols, row, row, FFTW_FORWARD, FFTW_ESTIMATE);
        fftw_execute(plan);
        fftw_destroy_plan(plan);
    }
}

int main() {
    int nrows = 1000;
    int ncols = 1000;
    fftw_complex* matrix = (fftw_complex*) fftw_malloc(sizeof(fftw_complex) * nrows * ncols);

    // Initialize matrix with data

    compute_fft_2d(matrix, nrows, ncols);

    // Process the transformed matrix

    fftw_free(matrix);
    return 0;
}

In this example, the compute_fft_2d function parallelizes the computation of the 1D FFT along the rows of the 2D matrix using OpenMP. Each row of the matrix is processed in parallel, and FFTW plans are created and executed for each row independently. This allows for efficient parallelization of the computation across multiple threads.