FluidFFT: Common API (C++ and Python) for Fast Fourier Transform HPC Libraries. Mohanan, A. V., Bonamy, C., & Augier, P. Journal of Open Research Software, 7(1):10, April, 2019. ![FluidFFT: Common API (C++ and Python) for Fast Fourier Transform HPC Libraries [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The Python package fluidfft provides a common Python API for performing Fast Fourier Transforms (FFT) in sequential, in parallel and on GPU with different FFT libraries (FFTW, P3DFFT, PFFT, cuFFT). fluidfft is a comprehensive FFT framework which allows Python users to easily and efficiently perform FFT and the associated tasks, such as computing linear operators and energy spectra. We describe the architecture of the package composed of C++ and Cython FFT classes, Python “operator” classes and Pythran functions. The package supplies utilities to easily test itself and benchmark the different FFT solutions for a particular case and on a particular machine. We present a performance scaling analysis on three different computing clusters and a microbenchmark showing that fluidfft is an interesting solution to write efficient Python applications using FFT. Funding statement: This project has indirectly benefited from funding from the foundation Simone et Cino Del Duca de l’Institut de France, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 647018-WATU and Euhit consortium) and the Swedish Research Council (Vetenskapsrådet): 2013–5191. We have also been able to use supercomputers of CIMENT/GRICAD, CINES/GENCI (grant 2018-A0040107567) and the Swedish National Infrastructure for Computing (SNIC).
@article{fluidfft,
title = {{FluidFFT}: {Common} {API} ({C}++ and {Python}) for {Fast} {Fourier} {Transform} {HPC} {Libraries}},
volume = {7},
copyright = {Authors who publish with this journal agree to the following terms: Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access ). All third-party images reproduced on this journal are shared under Educational Fair Use. For more information on Educational Fair Use , please see this useful checklist prepared by Columbia University Libraries . All copyright of third-party content posted here for research purposes belongs to its original owners. Unless otherwise stated all references to characters and comic art presented on this journal are ©, ® or ™ of their respective owners. No challenge to any owner’s rights is intended or should be inferred.},
issn = {2049-9647},
shorttitle = {{FluidFFT}},
url = {http://openresearchsoftware.metajnl.com/articles/10.5334/jors.238/},
doi = {10.5334/jors.238},
abstract = {The Python package fluidfft provides a common Python API for performing Fast Fourier Transforms (FFT) in sequential, in parallel and on GPU with different FFT libraries (FFTW, P3DFFT, PFFT, cuFFT). fluidfft is a comprehensive FFT framework which allows Python users to easily and efficiently perform FFT and the associated tasks, such as computing linear operators and energy spectra. We describe the architecture of the package composed of C++ and Cython FFT classes, Python “operator” classes and Pythran functions. The package supplies utilities to easily test itself and benchmark the different FFT solutions for a particular case and on a particular machine. We present a performance scaling analysis on three different computing clusters and a microbenchmark showing that fluidfft is an interesting solution to write efficient Python applications using FFT.
Funding statement: This project has indirectly benefited from funding from the foundation Simone et Cino Del Duca de l’Institut de France, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 647018-WATU and Euhit consortium) and the Swedish Research Council (Vetenskapsrådet): 2013–5191. We have also been able to use supercomputers of CIMENT/GRICAD, CINES/GENCI (grant 2018-A0040107567) and the Swedish National Infrastructure for Computing (SNIC).},
language = {en},
number = {1},
urldate = {2019-04-25},
journal = {Journal of Open Research Software},
author = {Mohanan, Ashwin Vishnu and Bonamy, Cyrille and Augier, Pierre},
month = apr,
year = {2019},
keywords = {\#cv, \#paper\_04\_swe, \#phd, Computer Science - Mathematical Software, FFT, Physics - Fluid Dynamics, Python, fluid dynamics, simulations},
pages = {10},
}
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