High Level Programming for Heterogeneous Architectures
Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA
arXiv:1408.4964 [cs.PF], (21 Aug 2014)
@article{2014arXiv1408.4964S,
author={Segal}, O. and {Margala}, M. and {Rahul Chalamalasetti}, S. and {Wright}, M.},
title={"{High Level Programming for Heterogeneous Architectures}"},
journal={ArXiv e-prints},
archivePrefix={"arXiv"},
eprint={1408.4964},
primaryClass={"cs.PF"},
keywords={Computer Science – Performance, Computer Science – Programming Languages},
year={2014},
month={aug},
adsurl={http://adsabs.harvard.edu/abs/2014arXiv1408.4964S},
adsnote={Provided by the SAO/NASA Astrophysics Data System}
}
This work presents an effort to bridge the gap between abstract high level programming and OpenCL by extending an existing high level Java programming framework (APARAPI), based on OpenCL, so that it can be used to program FPGAs at a high level of abstraction and increased ease of programmability. We run several real world algorithms to assess the performance of the framework on both a low end and a high end system. On the low end and high end systems respectively we observed up to 78-80 percent power reduction and 4.8X-5.3X speed increase running NBody simulation, as well as up to 65-80 percent power reduction and 6.2X-7X speed increase for a KMeans, MapReduce algorithm running on top of the Hadoop framework and APARAPI.
August 23, 2014 by hgpu
