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We propose a parallel (distributed) version of the spectral proper orthogonaldecomposition (SPOD) technique. The parallel SPOD algorithm distributes thespatial dimension of the dataset preserving time. This approach is adopted topreserve the non-distributed fast Fourier transform of the data in time,thereby avoiding the associated bottlenecks. The parallel SPOD algorithm isimplemented in the PySPOD (https://github.com/MathEXLab/PySPOD) library andmakes use of the standard message passing interface (MPI) library, implementedin Python via mpi4py (https://mpi4py.readthedocs.io/en/stable/). An extensiveperformance evaluation of the parallel package is provided, including strongand weak scalability analyses. The open-source library allows the analysis oflarge datasets of interest across the scientific community. Here, we presentapplications in fluid dynamics and geophysics, that are extremely difficult (ifnot impossible) to achieve without a parallel algorithm. This work opens thepath toward modal analyses of big quasi-stationary data, helping to uncover newunexplored spatio-temporal patterns.

Unlocking massively parallel spectral proper orthogonal decompositions in the PySPOD package