Mpi4py Fft Save

mpi4py-fft

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mpi4py-fft is a Python package for computing Fast Fourier Transforms (FFTs). Large arrays are distributed and communications are handled under the hood by MPI for Python (mpi4py). To distribute large arrays we are using a new and completely generic algorithm <https://arxiv.org/abs/1804.09536>_ that allows for any index set of a multidimensional array to be distributed. We can distribute just one index (a slab decomposition), two index sets (pencil decomposition) or even more for higher-dimensional arrays.

mpi4py-fft comes with its own Python interface to the serial FFTW <http://www.fftw.org>_ library. This interface can be used much like pyfftw <https://hgomersall.github.io/pyFFTW/>_, and even for real-to-real transforms, like discrete cosine or sine transforms.

Further documentation is found at readthedocs <https://mpi4py-fft.readthedocs.io/en/latest/>_.

Installation

The mpi4py-fft package can be installed using::

pip install mpi4py-fft

or, to get the latest version from GitHub::

pip install git+https://github.com/mpi4py/mpi4py-fft@master

Install with conda from the coda-forge channel::

conda install -c conda-forge mpi4py-fft

or build it with conda build from the main source directory::

conda build -c conda-forge conf/
conda create --name mpi4py_fft mpi4py_fft --use-local

which will pull in the required dependencies from the conda-forge channel.

Note that mpi4py-fft depends on Python packages

* mpi4py
* numpy
* cython

and the serial C-library

* `FFTW <http://www.fftw.org>`_

Note in particular that mpi4py requires that you have a working MPI installation, with the compiler wrapper mpicc, on your search path. The FFTW header and libraries must also be available on the search path, and we will build wrappers for any precision found of the FFTW libraries.

All of the above dependencies are available and will be downloaded through the conda-forge channel if conda is used for installation. However, pip will not help you with MPI or FFTW.

For IO you need to install either h5py <https://www.h5py.org>_ or netCDF4 <http://unidata.github.io/netcdf4-python/>_ with support for MPI. Both are available from the coda-forge channel through::

conda install -c conda-forge h5py=*=mpi* netcdf4=*=mpi*