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 437 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. Chillotti, Ilaria; Gama, Nicolas; Georgieva, Mariya; Izabachène, Malika: TFHE: fast fully homomorphic encryption over the torus (2020)
  3. 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
  4. 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
  5. Barnett, Alexander H.; Magland, Jeremy; af Klinteberg, Ludvig: A parallel nonuniform fast Fourier transform library based on an “exponential of semicircle” kernel (2019)
  6. Bittens, Sina; Plonka, Gerlind: Sparse fast DCT for vectors with one-block support (2019)
  7. Bright, Curtis; Đoković, Dragomir Ž.; Kotsireas, Ilias; Ganesh, Vijay: The SAT+CAS method for combinatorial search with applications to best matrices (2019)
  8. Candy, J.; Sfiligoi, I.; Belli, E.; Hallatschek, K.; Holland, C.; Howard, N.; D’Azevedo, E.: Multiscale-optimized plasma turbulence simulation on petascale architectures (2019)
  9. Corsaro, Stefania; Kyriakou, Ioannis; Marazzina, Daniele; Marino, Zelda: A general framework for pricing Asian options under stochastic volatility on parallel architectures (2019)
  10. 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)
  11. Eric W. Koch, Ryan D. Boyden, Blakesley Burkhart, Adam Ginsburg, Jason L. Loeppky, Stella S.R. Offner: TurbuStat: Turbulence Statistics in Python (2019) arXiv
  12. Gadioli, Davide; Vitali, Emanuele; Palermo, Gianluca; Silvano, Cristina: mARGOt: a dynamic autotuning framework for self-aware approximate computing (2019)
  13. Green, Kevin R.; Bohn, Tanner A.; Spiteri, Raymond J.: Direct function evaluation versus lookup tables: when to use which? (2019)
  14. Grimm-Strele, Hannes; Kabel, Matthias: Runtime optimization of a memory efficient CG solver for FFT-based homogenization: implementation details and scaling results for linear elasticity (2019)
  15. Herrmann, Lukas: Strong convergence analysis of iterative solvers for random operator equations (2019)
  16. Kabelitz, C.; Linz, S. J.: The dynamics of geometric PDEs: surface evolution equations and a comparison with their small gradient approximations (2019)
  17. Khusnutdinova, K. R.; Tranter, M. R.: Weakly-nonlinear solution of coupled Boussinesq equations and radiating solitary waves (2019)
  18. Lambers, James V.; Sumner, Amber C.: Explorations in numerical analysis (2019)
  19. Mang, Andreas; Gholami, Amir; Davatzikos, Christos; Biros, George: CLAIRE: a distributed-memory solver for constrained large deformation diffeomorphic image registration (2019)
  20. Ma, Suna; Li, Huiyuan; Zhang, Zhimin: Novel spectral methods for Schrödinger equations with an inverse square potential on the whole space (2019)

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