FFTW

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 450 articles , 1 standard article )

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  1. Bright, Curtis; Kotsireas, Ilias; Ganesh, Vijay: Applying computer algebra systems with SAT solvers to the Williamson conjecture (2020)
  2. Cantisán, Julia; Coccolo, Mattia; Seoane, Jesús M.; Sanjuán, Miguel A. F.: Delay-induced resonance in the time-delayed Duffing oscillator (2020)
  3. Chillotti, Ilaria; Gama, Nicolas; Georgieva, Mariya; Izabachène, Malika: TFHE: fast fully homomorphic encryption over the torus (2020)
  4. Ernesti, Felix; Schneider, Matti; Böhlke, Thomas: Fast implicit solvers for phase-field fracture problems on heterogeneous microstructures (2020)
  5. Héctor Andrade-Loarca, Gitta Kutyniok: tfShearlab: The TensorFlow Digital Shearlet Transform for Deep Learning (2020) arXiv
  6. Jose Manuel López; Daniel Feldmann; Markus Rampp; Alberto Vela-Martín; Liang Shie; Marc Avila: nsCouette - A high-performance code for direct numerical simulations of turbulent Taylor-Couette flow (2020) not zbMATH
  7. Khristenko, Ustim; Constantinescu, Andrei; Tallec, Patrick Le; Oden, J. Tinsley; Wohlmuth, Barbara: A statistical framework for generating microstructures of two-phase random materials: application to fatigue analysis (2020)
  8. Pardeep Kaur, Arko Roy, Sandeep Gautam: FORTRESS: FORTRAN programs for solving coupled Gross-Pitaevskii equations for spin-orbit coupled spin-1 Bose-Einstein condensate (2020) arXiv
  9. Wicht, Daniel; Schneider, Matti; Böhlke, Thomas: An efficient solution scheme for small-strain crystal-elasto-viscoplasticity in a dual framework (2020)
  10. Barnett, Alexander H.; Magland, Jeremy; af Klinteberg, Ludvig: A parallel nonuniform fast Fourier transform library based on an “exponential of semicircle” kernel (2019)
  11. Bittens, Sina; Plonka, Gerlind: Sparse fast DCT for vectors with one-block support (2019)
  12. Bright, Curtis; Đoković, Dragomir Ž.; Kotsireas, Ilias; Ganesh, Vijay: The SAT+CAS method for combinatorial search with applications to best matrices (2019)
  13. Candy, J.; Sfiligoi, I.; Belli, E.; Hallatschek, K.; Holland, C.; Howard, N.; D’Azevedo, E.: Multiscale-optimized plasma turbulence simulation on petascale architectures (2019)
  14. Cochard, A.; Lengliné, O.; Måløy, K. J.; Toussaint, R.: Thermally activated crack fronts propagating in pinning disorder: simultaneous brittle/creep behaviour depending on scale (2019)
  15. Corsaro, Stefania; Kyriakou, Ioannis; Marazzina, Daniele; Marino, Zelda: A general framework for pricing Asian options under stochastic volatility on parallel architectures (2019)
  16. 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)
  17. Eric W. Koch, Ryan D. Boyden, Blakesley Burkhart, Adam Ginsburg, Jason L. Loeppky, Stella S.R. Offner: TurbuStat: Turbulence Statistics in Python (2019) arXiv
  18. Frérot, Lucas; Bonnet, Marc; Molinari, Jean-François; Anciaux, Guillaume: A Fourier-accelerated volume integral method for elastoplastic contact (2019)
  19. Gadioli, Davide; Vitali, Emanuele; Palermo, Gianluca; Silvano, Cristina: mARGOt: a dynamic autotuning framework for self-aware approximate computing (2019)
  20. Garcia, Ferran; Seilmayer, Martin; Giesecke, André; Stefani, Frank: Modulated rotating waves in the magnetised spherical Couette system (2019)

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