FFTW is a C subroutine library for computing the discrete Fourier transform (DFT) in one or more dimensions, of arbitrary input size, and of both real and complex data (as well as of even/odd data, i.e. the discrete cosine/sine transforms or DCT/DST). We believe that FFTW, which is free software, should become the FFT library of choice for most applications. The latest official release of FFTW is version 3.3.3, available from our download page. Version 3.3 introduced support for the AVX x86 extensions, a distributed-memory implementation on top of MPI, and a Fortran 2003 API. Version 3.3.1 introduced support for the ARM Neon extensions.

References in zbMATH (referenced in 426 articles , 1 standard article )

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  1. Barnett, Alexander H.; Magland, Jeremy; af Klinteberg, Ludvig: A parallel nonuniform fast Fourier transform library based on an “exponential of semicircle” kernel (2019)
  2. Bittens, Sina; Plonka, Gerlind: Sparse fast DCT for vectors with one-block support (2019)
  3. Bright, Curtis; Kotsireas, Ilias; Ganesh, Vijay: The SAT+CAS paradigm and the Williamson conjecture (2019)
  4. Candy, J.; Sfiligoi, I.; Belli, E.; Hallatschek, K.; Holland, C.; Howard, N.; D’Azevedo, E.: Multiscale-optimized plasma turbulence simulation on petascale architectures (2019)
  5. Corsaro, Stefania; Kyriakou, Ioannis; Marazzina, Daniele; Marino, Zelda: A general framework for pricing Asian options under stochastic volatility on parallel architectures (2019)
  6. Dyachenko, Sergey A.: On the dynamics of a free surface of an ideal fluid in a bounded domain in the presence of surface tension (2019)
  7. Eric W. Koch, Ryan D. Boyden, Blakesley Burkhart, Adam Ginsburg, Jason L. Loeppky, Stella S.R. Offner: TurbuStat: Turbulence Statistics in Python (2019) arXiv
  8. Gadioli, Davide; Vitali, Emanuele; Palermo, Gianluca; Silvano, Cristina: mARGOt: a dynamic autotuning framework for self-aware approximate computing (2019)
  9. Green, Kevin R.; Bohn, Tanner A.; Spiteri, Raymond J.: Direct function evaluation versus lookup tables: when to use which? (2019)
  10. Herrmann, Lukas: Strong convergence analysis of iterative solvers for random operator equations (2019)
  11. Kabelitz, C.; Linz, S. J.: The dynamics of geometric PDEs: surface evolution equations and a comparison with their small gradient approximations (2019)
  12. Lambers, James V.; Sumner, Amber C.: Explorations in numerical analysis (2019)
  13. Mang, Andreas; Gholami, Amir; Davatzikos, Christos; Biros, George: CLAIRE: a distributed-memory solver for constrained large deformation diffeomorphic image registration (2019)
  14. Ma, Suna; Li, Huiyuan; Zhang, Zhimin: Novel spectral methods for Schrödinger equations with an inverse square potential on the whole space (2019)
  15. Matteo Ravasi, Ivan Vasconcelos: PyLops - A Linear-Operator Python Library for large scale optimization (2019) arXiv
  16. Neumüller, Martin; Smears, Iain: Time-parallel iterative solvers for parabolic evolution equations (2019)
  17. Rey, Valentine; Krumscheid, S.; Nobile, F.: Quantifying uncertainties in contact mechanics of rough surfaces using the Multilevel Monte Carlo method (2019)
  18. Schneider, Matti; Wicht, Daniel; Böhlke, Thomas: On polarization-based schemes for the FFT-based computational homogenization of inelastic materials (2019)
  19. Yazdanbakhsh, Omolbanin; Dick, Scott: FANCFIS: fast adaptive neuro-complex fuzzy inference system (2019)
  20. Ammon, Martin; Baggioli, Matteo; Jiménez-Alba, Amadeo; Moeckel, Sebastian: A smeared quantum phase transition in disordered holography (2018)

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