Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2309.11808 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910547416449024 |
|---|---|
| author | Rogowski, Marcin Yeung, Brandon C. Y. Schmidt, Oliver T. Maulik, Romit Dalcin, Lisandro Parsani, Matteo Mengaldo, Gianmarco |
| author_facet | Rogowski, Marcin Yeung, Brandon C. Y. Schmidt, Oliver T. Maulik, Romit Dalcin, Lisandro Parsani, Matteo Mengaldo, Gianmarco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_11808 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Unlocking massively parallel spectral proper orthogonal decompositions in the PySPOD package Rogowski, Marcin Yeung, Brandon C. Y. Schmidt, Oliver T. Maulik, Romit Dalcin, Lisandro Parsani, Matteo Mengaldo, Gianmarco Computational Physics Distributed, Parallel, and Cluster Computing Mathematical Software 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. |
| title | Unlocking massively parallel spectral proper orthogonal decompositions in the PySPOD package |
| topic | Computational Physics Distributed, Parallel, and Cluster Computing Mathematical Software |
| url | https://arxiv.org/abs/2309.11808 |