mpiPython: Prospects for Node Performance

Judah Nava, Hanku Lee
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Abstract

Python as an interpreted language is limited in performance by its ability to optimize code. With it being a high-level programming language, it’s still a strong choice for data scientists to learn and use. If Python could be optimized for parallel programming, its full potential in parallel and cloud computing environments could be achieved. mpiPython is a message-passing module that gives Python the ability to be used in SPMD (Single Program Multiple Data) environments. In this paper, we review basic features of mpiPython, including its runtime communication libraries and design strategies. mpiPython also has new features to help mpiPython programmers, that includes simplifying traditional MPI initialization. During the development of mpiPython, we realized that individual node performance of mpiPython is uncertain and critical. mpiPython node performance will be analyzed within the benchmarks.
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mpiPython:展望节点性能
Python作为一种解释型语言,由于其优化代码的能力而在性能上受到限制。作为一种高级编程语言,它仍然是数据科学家学习和使用的一个强有力的选择。如果Python可以针对并行编程进行优化,那么它在并行和云计算环境中的全部潜力就可以实现。mpiPython是一个消息传递模块,它使Python能够在SPMD(单程序多数据)环境中使用。在本文中,我们回顾了mpiPython的基本特性,包括它的运行时通信库和设计策略。mpiPython还具有帮助mpiPython程序员的新特性,包括简化传统的MPI初始化。在mpiPython的开发过程中,我们意识到mpiPython的单个节点性能是不确定的和关键的。mpiPython节点的性能将在基准测试中进行分析。
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