Simplifying non-contiguous data transfer with MPI for Python.
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| Title: | Simplifying non-contiguous data transfer with MPI for Python. |
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| Authors: | Nölp, Klaus1 (AUTHOR) klaus.noelp@fernuni-hagen.de, Oden, Lena1 (AUTHOR) lena.oden@fernuni-hagen.de |
| Source: | Journal of Supercomputing. Nov2023, Vol. 79 Issue 17, p20019-20040. 22p. |
| Subjects: | Pythons, Scientific computing, Parallel programming |
| Abstract: | Python is becoming increasingly popular in scientific computing. The package MPI for Python (mpi4py) allows writing efficient parallel programs that scale across multiple nodes. However, it does not support non-contiguous data via slices, which is a well-known feature of NumPy. In this work, we therefore evaluate several methods to support the direct transfer of non-contiguous arrays in mpi4py. This significantly simplifies the code, while the performance basically stays the same. In a PingPong-, Stencil- and Lattice-Boltzmann-Benchmark, we compare the common manual copying, a NumPy-Copy design and a design that is based on MPI derived datatypes. In one case, the MPI derived datatype design could achieve a speedup of 15% in a Stencil-Benchmark on four compute nodes. Our designs are superior to naive manual copies, but for maximum performance manual copies with pre-allocated buffers or MPI persistent communication will be a better choice. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Python is becoming increasingly popular in scientific computing. The package MPI for Python (mpi4py) allows writing efficient parallel programs that scale across multiple nodes. However, it does not support non-contiguous data via slices, which is a well-known feature of NumPy. In this work, we therefore evaluate several methods to support the direct transfer of non-contiguous arrays in mpi4py. This significantly simplifies the code, while the performance basically stays the same. In a PingPong-, Stencil- and Lattice-Boltzmann-Benchmark, we compare the common manual copying, a NumPy-Copy design and a design that is based on MPI derived datatypes. In one case, the MPI derived datatype design could achieve a speedup of 15% in a Stencil-Benchmark on four compute nodes. Our designs are superior to naive manual copies, but for maximum performance manual copies with pre-allocated buffers or MPI persistent communication will be a better choice. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 09208542 |
| DOI: | 10.1007/s11227-023-05398-7 |
