  We propose a parallel (distributed) version of the spectral proper orthogonal
decomposition (SPOD) technique. The parallel SPOD algorithm distributes the
spatial dimension of the dataset preserving time. This approach is adopted to
preserve the non-distributed fast Fourier transform of the data in time,
thereby avoiding the associated bottlenecks. The parallel SPOD algorithm is
implemented in the PySPOD (https://github.com/MathEXLab/PySPOD) library and
makes use of the standard message passing interface (MPI) library, implemented
in Python via mpi4py (https://mpi4py.readthedocs.io/en/stable/). An extensive
performance evaluation of the parallel package is provided, including strong
and weak scalability analyses. The open-source library allows the analysis of
large datasets of interest across the scientific community. Here, we present
applications in fluid dynamics and geophysics, that are extremely difficult (if
not impossible) to achieve without a parallel algorithm. This work opens the
path toward modal analyses of big quasi-stationary data, helping to uncover new
unexplored spatio-temporal patterns.
