FFT benchmark

A benchmark for comparison of FFT algorithms performance. Supports 1D, 2D, and 3D transforms with float and double precision. Measures the performance of real/complex, in-place/out-of-place, forward/inverse FFT.

Supported libraries

Library	Float	Double	1D	2D & 3D	Notes	Installation
KFR	✅	✅	✅	✅	Real transforms require even sizes	Manual
Intel IPP	✅	✅	✅	❌		Manual
Intel MKL	✅	✅	✅	✅		Manual
FFTW	✅	✅	✅	✅		Vcpkg
Sleef	✅	✅	✅	❌		Vcpkg
PFFFT	✅	❌	✅	❌	Float-only	Vcpkg
JUCE	✅	❌	✅	❌	Float-only, power-of-2 sizes only	Vcpkg
KissFFT	✅	✅	✅	❌	No real inverse; out-of-place only	Bundled

Note: Multithreading is disabled for fair comparison, as only a few libraries support it.

All libraries are optional — if not found via CMake's find_package, they are simply disabled.

Building

Requirements

• C++17 compiler (Clang 12.0+ recommended)
• CMake 3.12 or newer
• Python 3.5 or newer (for plotting)
  • matplotlib
  • numpy

Setup

See .github/workflows/build.yml for an example of how to set up the environment for building and running the benchmarks.

Some libraries are available as packages in vcpkg and they will be found automatically on cmake configuration as vcpkg is bundled as submodule. Some libraries (e.g. KFR, Intel libraries) require manual installation, so you need to specify the paths to their CMake configs in CMAKE_PREFIX_PATH.

Example:

C:/Program Files (x86)/Intel/oneAPI/ipp/2021.9.0/lib/cmake/ipp
C:/Program Files (x86)/Intel/oneAPI/mkl/2026.0/lib/cmake/mkl
kfr-install-dir/lib/cmake

Build

cmake -B build -DCMAKE_PREFIX_PATH="path1;path2;..."
cmake --build build

A separate executable is produced for each library found (e.g. fft_benchmark_kfr, fft_benchmark_ipp, etc.).

Usage

fft_benchmark_<library> [options] <size> [<size> ...]

Example:

fft_benchmark_kfr --save results.json 262144 512x512 64x64x64
fft_benchmark_kfr --save - 262144   # print JSON to stdout

Options

Option	Description
SIZE	1D FFT
SIZExSIZE	2D FFT. Example: 64x32
SIZExSIZExSIZE	3D FFT. Example: 64x32x16
--complex flags	y (complex tests), yn (all tests), n (real tests)
--inverse flags	y (IDFT tests), ny (DFT/IDFT tests), n (DFT tests)
--inplace flags	y (inplace tests), ny (all tests), n (out-of-place tests)
--save data.json	Save results in JSON
--save -	Print resulting JSON to stdout
--avx2-only	Enable only AVX2 (supported in KFR, IPP, MKL)
--no-progress	Disable verbose progress output
--no-banner	Disable banner

Plotting results

Use plot.py to generate comparison charts from the JSON output of multiple benchmark runs:

python plot.py results_kfr.json results_ipp.json results_fftw.json

This generates SVG plots for every combination of data type, transform type, direction, and buffer mode (e.g. float-complex-forward-inplace.svg).

JSON output format

Each benchmark run produces a JSON file with the following structure:

{
    "cpu": "...",
    "clock_MHz": 3600.0,
    "library": "...",
    "results": [
        {
            "size": 1024,
            "data": "float",
            "type": "complex",
            "direction": "forward",
            "buffer": "outofplace",
            "mflops": 12345.67,
            "best_time": 0.83,
            "median_time": 0.91
        }
    ]
}

For multidimensional transforms, size is an array (e.g. [512, 512]).

Legal Disclaimer

All trademarks, product names, and company names are the property of their respective owners and are used for identification purposes only.

Author

Dan Casarín, the author of KFR

License

MIT