P81: Offloading Python Kernels to Micro-Core
Architectures
SessionPoster Reception
Author
Event Type
ACM Student Research Competition
Poster
Reception
TimeTuesday, November 14th5:15pm -
7pm
LocationFour Seasons Ballroom
DescriptionMicro-core architectures combine many low memory, low
power computing cores together in a single package.
These can be used as a co-processor or standalone but
due to limited on-chip memory and esoteric nature of the
hardware, writing efficient parallel codes for them is
challenging. We previously developed ePython, a low
memory (24Kb) implementation of Python supporting the
rapid development of parallel Python codes and education
for these architectures. In this poster we present our
work on an offload abstraction to support the use of
micro-cores as an accelerator. Programmers decorate
specific functions in their Python code, running under
any Python interpreter on the host, with our underlying
technology then responsible for the low-level data
movement, scheduling and execution of kernels on the
micro-cores. Aimed at education and fast prototyping, a
machine learning code for detecting lung cancer, where
computational kernels are offloaded to micro-cores, is
used to illustrate the approach.
