The Fastest Fourier Transform on the Web!

Our library is essentially a metalibrary containing many FFT libraries, both javascript and webassembly based. We'll refer to these as sub-libraries.

There will be a default sub-library that is used, but if you run profile(), it will benchmark them all and use the best for future calls.

As part of importing the library we will run a check to see if wasm is even supported, so the profiler and default can know which pool to pull from.

const webfft = require('webfft'); // or do we need something like https://github.com/AWSM-WASM/PulseFFT#instantiate-pulse

// Instantiate
const fftsize = 1024; // must be power of 2
const fft = new webfft(fftsize); // provide size.  assumed to be complex

// Profile
fft.profile(); // optional params like number of trials or how long to spend profiling
// or
profileResults = fft.profile(); // profile results object can be used to make visualizations of the benchmarking results
console.log("Best one:", profileResults[0]['SubLibraryName']);

// (will likely be tweaked later) profileResults object/list, in order of speed:
/*
[{'SubLibraryName': 'indutny', 'fftsPerSecond': 10.34},
 {'SubLibraryName': 'indutny', 'fftsPerSecond': 8.34} 
...]
*/

// Create Input
const input = new Float32Array(2048); // interleaved complex array (IQIQIQIQ...), so it's twice the size
input.fill(0);

// Run FFT
const out = fft.fft(input); // out will be a Float32Array of size 2048
// or
const out = fft.fft(input, 'indutny');
//or 
const out = fft.fft(input, profileResults[0]['SubLibraryName']); // profileResults obj will likely be changed later

We'll use the same out = x.fft(in) approach for the API used internally, to call individual sub-libraries.

A file webfftWrapper.js will exist in all sub-library dirs, so that we can avoid modifying the actual sub-library code, making it easier to update them if need be.

• fft.js aka indutny
  • https://github.com/indutny/fft.js/ (last changed 2021)
  • What IQEngine used before WebFFT
• fft-js aka vail
  • https://www.npmjs.com/package/fft-js
  • https://github.com/vail-systems/node-fft (last changed 2019)
• jsfft aka dntj
  • https://github.com/dntj/jsfft (last changed 2019)
• fourier-transform
  • https://github.com/scijs/fourier-transform (last changed 2018)
  • author compared it to several others https://github.com/scijs/fourier-transform/blob/master/benchmark.md
• ndarray-fft
  • https://github.com/scijs/ndarray-fft (last changed 2016)
• DSP.js
  • https://github.com/corbanbrook/dsp.js (no longer maintained but last changed 2022)
• digitalsignals (fork of DSP.js)
  • https://github.com/zewemli/dsp.js (last changed 2014)
• fourier
  • https://github.com/drom/fourier (last changed 2021)
• ml-fft
  • https://github.com/mljs/fft (last changed 2020)
• Nockert
  • https://github.com/auroranockert/fft.js
  • not on npm

• PulseFFT
  • https://github.com/AWSM-WASM/PulseFFT (last change in 2018)
  • KissFFT based
• kissfft-wasm
  • https://github.com/iVilja/kissfft-wasm (last change Oct 2022)
  • KissFFT based
• Mozilla's implementation used in webkit audio
  • appears to be a copy of kissFFT
  • https://github.com/mozilla/gecko-dev/tree/6dd16cb77552c7cec8ab7e4e3b74ca7d5e320339/media/kiss_fft
• fftw-js
  • FFTW (extremely popular for desktop) ported with webasm
  • https://github.com/j-funk/fftw-js
  • GPL licensed!!!
• pffft.wasm
  • https://github.com/JorenSix/pffft.wasm (last change June 2022)
  • SIMD support

1. npm install
2. npm run build
3. npm install --global serve
4. npm run serve
5. Open your browser to http://localhost:8080

• https://github.com/j-funk/js-dsp-test
• https://github.com/scijs/fourier-transform/blob/HEAD/benchmark.md
• https://thebreakfastpost.com/2015/10/18/ffts-in-javascript/
• https://toughengineer.github.io/demo/dsp/fft-perf/

Instead of npm link you can use this to install the package locally: npm install -g i .