ePython: An implementation of Python for the many-core Epiphany coprocessor

TL;DR

This paper introduces ePython, a Python subset tailored for the Epiphany many-core architecture, enabling easier parallel programming and HPC exploration despite hardware constraints.

Contribution

The work presents a new Python interpreter optimized for Epiphany's limited memory, supporting parallelism and interoperability, facilitating HPC education and prototyping.

Findings

ePython enables quick parallel programming on Epiphany.

Supports hybrid execution on host CPU and Epiphany.

Community adoption demonstrates practical utility.

Abstract

The Epiphany is a many-core, low power, low on-chip memory architecture and one can very cheaply gain access to a number of parallel cores which is beneficial for HPC education and prototyping. The very low power nature of these architectures also means that there is potential for their use in future HPC machines, however there is a high barrier to entry in programming them due to the associated complexities and immaturity of supporting tools. In this paper we present our work on ePython, a subset of Python for the Epiphany and similar many-core co-processors. Due to the limited on-chip memory per core we have developed a new Python interpreter and this, combined with additional support for parallelism, has meant that novices can take advantage of Python to very quickly write parallel codes on the Epiphany and explore concepts of HPC using a smaller scale parallel machine. The high level nature of Python opens up new possibilities on the Epiphany, we examine a computationally intensive Gauss-Seidel code from the programmability and performance perspective, discuss running Python hybrid on both the host CPU and Epiphany, and interoperability between a full Python interpreter on the CPU and ePython on the Epiphany. The result of this work is support for developing Python on the Epiphany, which can be applied to other similar architectures, that the community have already started to adopt and use to explore concepts of parallelism and HPC.