Implementing FFTs in Practice

Fuente: arXiv
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Auteurs principaux: Johnson, Steven G., Frigo, Matteo
Format: Preprint
Publié: 2026
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author Johnson, Steven G.
Frigo, Matteo
author_facet Johnson, Steven G.
Frigo, Matteo
contents This review article was first published in 2008 as chapter 11 in the book "Fast Fourier Transforms," edited by C. S. Burrus, for the Connexions project at Rice University, which is sadly no longer online. It gives a high-level overview of some of the engineering considerations that arise in high-performance implementations of fast Fourier trasnforms (FFTs). It explains why optimized FFTs are very different from textbook "radix-2 Cooley-Tukey" FFT algorithms, in order to compensate for the memory hierarchy and exploit the large register sets and deep pipelines of modern CPUs. Using the FFTW library as a case study, it talks about tradeoffs in the use of recursion, generation of twiddle factors, code generation, and other algorithmic choices.
format Preprint
id arxiv_https___arxiv_org_abs_2602_23525
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Implementing FFTs in Practice
Johnson, Steven G.
Frigo, Matteo
Numerical Analysis
This review article was first published in 2008 as chapter 11 in the book "Fast Fourier Transforms," edited by C. S. Burrus, for the Connexions project at Rice University, which is sadly no longer online. It gives a high-level overview of some of the engineering considerations that arise in high-performance implementations of fast Fourier trasnforms (FFTs). It explains why optimized FFTs are very different from textbook "radix-2 Cooley-Tukey" FFT algorithms, in order to compensate for the memory hierarchy and exploit the large register sets and deep pipelines of modern CPUs. Using the FFTW library as a case study, it talks about tradeoffs in the use of recursion, generation of twiddle factors, code generation, and other algorithmic choices.
title Implementing FFTs in Practice
topic Numerical Analysis
url https://arxiv.org/abs/2602.23525