I recently created two notebooks showing the performance of FiPy in parallel. The aims of the notebooks are
to clearly demonstrate that FiPy scales reasonably well in parallel at least up to 48 nodes,
to demonstrate that the differences between PySparse and Trilinos are not that important for larger problems,
to have some publicly available data for FiPy’s performance in parallel and
to demonstrate the use of FiPy with IPython’s native parallel infrastructure (still MPI based).
The first notebook demonstrates how to use IPython’s native parallel infrastructure with FiPy and presents parallel results on a laptop. The second notebook presents results for up to 48 parallel processes running on a cluster.
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