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

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

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

Content Provider	ACM Digital Library
Author	Brown, Nick
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.
Starting Page	59
Ending Page	66
Page Count	8
ISBN	9781509052202
DOI	10.1109/PyHPC.2016.8
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-11-13
Access Restriction	Subscribed
Subject Keyword	Computer languages Multicore processing Parallel programming Runtime library Scientific computing Accelerator architectures Parallel machines Supercomputers
Content Type	Text
Resource Type	Article

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