Pydron: semi-automatic parallelization for multi-core and the cloud
Stefan C Müller, Gustavo Alonso, Adam Amara, Andre Csillaghy · Operating Systems Design and Implementation · 2014
The cloud, rack-scale computing, and multi-core are the basis for today's computing platforms. Their intrinsic parallelism is a challenge for programmers, specially in areas lacking the necessary economies of scale in application/ code reuse because of the small number of potential users and frequently changing code and data. In this paper, based on an on-going collaboration with several projects in astrophysics, we present Pydron, a system to parallelize and execute sequential Python code on a cloud, cluster, or multi-core infrastructure. While focused on scientific applications, the solution we propose is general and provides a competitive alternative to moving the development effort to application specific platforms. Pydron uses semi-automatic parallelization and can parallelize with an API of only two decorators. Pydron also supports the scheduling and run-time management of the parallel code, regardless of the target platform. First experiences with real astrophysics data pipelines indicate Pydron significantly simplifies development without sacrificing the performance gains of parallelism at the machine or cluster level.