@inproceedings{3c20e281c3a14d37abcf12a94cde2c1e,
title = "Embedded Accelerators for Scientific High-Performance Computing: An Energy Study of OpenCL Gaussian Elimination Workloads",
abstract = "Energy efficient High-Performance Computing (HPC) is becoming increasingly important. Recent ventures into this space have introduced an unlikely candidate to achieve exascale scientific computing hardware with a small energy footprint. ARM processors and embedded GPU accelerators originally developed for energy efficiency in mobile devices, where battery life is critical, are being repurposed and deployed in the next generation of supercomputers. Unfortunately, the performance of executing scientific workloads on many of these devices is largely unknown, yet the bulk of computation required in high-performance supercomputers is scientific. We present an analysis of one such scientific code, in the form of Gaussian Elimination, and evaluate both execution time and energy used on a range of embedded accelerator SoCs. These include three ARM CPUs and two mobile GPUs.",
keywords = "Accelerators, Energy Efficiency, HPC",
author = "Beau Johnston and Brian Lee and Luke Angove and Alistair Rendell",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 46th International Conference on Parallel Processing Workshops, ICPPW 2017 ; Conference date: 14-08-2017",
year = "2017",
month = sep,
day = "5",
doi = "10.1109/ICPPW.2017.22",
language = "English",
series = "Proceedings of the International Conference on Parallel Processing Workshops",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "59--68",
booktitle = "Proceedings - 46th International Conference on Parallel Processing Workshops, ICPPW 2017",
address = "United States",
}