@inproceedings{009aeef57de94d79b3b6befac51fea0c,
title = "Performance Evaluation of Python Parallel Programming Models: And mpi4py",
abstract = "Python is rapidly becoming the lingua franca of machine learning and scientific computing. With the broad use of frameworks such as Numpy, SciPy, and TensorFlow, scientific computing and machine learning are seeing a productivity boost on systems without a requisite loss in performance. While high-performance libraries often provide adequate performance within a node, distributed computing is required to scale Python across nodes and make it genuinely competitive in large-scale high-performance computing. Many frameworks, such as Charm4Py, DaCe, Dask, Legate Numpy, mpi4py, and Ray, scale Python across nodes. However, little is known about these frameworks' relative strengths and weaknesses, leaving practitioners and scientists without enough information about which frameworks are suitable for their requirements. In this paper, we seek to narrow this knowledge gap by studying the relative performance of two such frameworks: Charm4Py and mpi4py.We perform a comparative performance analysis of Charm4Py and mpi4py using CPU and GPU-based microbenchmarks other representative mini-apps for scientific computing.",
keywords = "Charm++, GPU, HPC, MPI, Python, analysis, benchmark, parallel programming, performance",
author = "Zane Fink and Simeng Liu and Jaemin Choi and Matthias Diener and Kale, {Laxmikant V.}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 6th International IEEE/ACM Workshop on Extreme Scale Programming Models and Middleware, ESPM2 2021 ; Conference date: 15-11-2021",
year = "2021",
doi = "10.1109/ESPM254806.2021.00010",
language = "English (US)",
series = "Proceedings of ESPM2 2021: 6th International IEEE Workshop on Extreme Scale Programming Models and Middleware, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "38--44",
booktitle = "Proceedings of ESPM2 2021",
address = "United States",
}