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Parallel distributed computing using Python
Open accessAbstract
Abstract This work presents two software components aimed to relieve the costs of accessing high-performance parallel computing resources within a Python programming environment: MPI for Python and PETSc for Python. MPI for Python is a general-purpose Python package that provides bindings for the Message Passing Interface (MPI) standard using any back-end MPI implementation. Its facilities allow parallel Python programs to easily exploit multiple processors using the message passing paradigm. PETSc for Python provides access to the Portable, Extensible Toolkit for Scientific Computation (PETSc) libraries. Its facilities allow sequential and parallel Python applications to exploit state of the art algorithms and data structures readily available in PETSc for the solution of large-scale problems in science and engineering. MPI for Python and PETSc for Python are fully integrated to PETSc-FEM, an MPI and PETSc based parallel, multiphysics, finite elements code developed at CIMEC laboratory. This software infrastructure supports research activities related to simulation of fluid flows with applications ranging from the design of microfluidic devices for biochemical analysis to modeling of large-scale stream/aquifer interactions.
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Citations
Cited by
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Federated learning on non-IID data: A survey
Zhu, H., Xu, J., Liu, S., & Jin, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.098 · Cited by 1,062
Firedrake: Automating the Finite Element Method by Composing Abstractions
Rathgeber, F., Ham, D. A., Mitchell, L., Lange, M., Luporini, F., Mcrae, A. T. T., Bercea, G.-T., Markall, G. R., & Kelly, P. H. J. (2017) · ACM Transactions on Mathematical Software · doi:10.1145/2998441 · Cited by 508
Yger, P., Spampinato, G. L., Esposito, E., Lefebvre, B., Deny, S., Gardella, C., Stimberg, M., Jetter, F., Zeck, G., Picaud, S., et al. (2018) · eLife · doi:10.7554/elife.34518 · Cited by 441
Proceedings of the 19th Python in Science Conference
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Metadata
- Authors
- Dalcin, L. D., Paz, R. R., Kler, P. A., & Cosimo, A.
- Venue
- Advances in Water Resources
- Publisher
- Elsevier BV
- Published
- 2011-09
- Volume
- 34
- Issue
- 9
- Pages
- 1124-1139
- ISSN
- 0309-1708 (ISSN-L)
- OpenAlex
- W2001032894
- Cited by
- 590
- References
- 0
- FWCI
- 5.38
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Dalcin, L. D., Paz, R. R., Kler, P. A., & Cosimo, A. (2011). Parallel distributed computing using Python. Advances in Water Resources. https://doi.org/10.1016/j.advwatres.2011.04.013
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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