LiU Electronic Press
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Author:
Maghazeh, Arian (Linköping University, Department of Computer and Information Science, Software and Systems) (Linköping University, The Institute of Technology)
Bordoloi, Unmesh D. (Linköping University, Department of Computer and Information Science, Software and Systems) (Linköping University, The Institute of Technology)
Eles, Petru (Linköping University, Department of Computer and Information Science, Software and Systems) (Linköping University, The Institute of Technology)
Peng, Zebo (Linköping University, Department of Computer and Information Science, Software and Systems) (Linköping University, The Institute of Technology)
Title:
General Purpose Computing on Low-Power Embedded GPUs: Has It Come of Age?
Department:
Linköping University, Department of Computer and Information Science, Software and Systems
Linköping University, The Institute of Technology
Publication type:
Report (Other academic)
Language:
English
Place of publ.: Linköping Publisher: Linköping University Electronic Press
Pages:
10
Year of publ.:
2013
URI:
urn:nbn:se:liu:diva-89993
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-89993
Subject category:
Engineering and Technology
Abstract(en) :

In this paper we evaluate the promise held by lowpower GPUs for non-graphic workloads that arise in embedded systems. Towards this, we map and implement 5 benchmarks, that find utility in very different application domains, to an embedded GPU. Our results show that apart from accelerated performance, embedded GPUs are promising also because of their energy efficiency which is an important design goal for battery-driven mobile devices. We show that adopting the same optimization strategies as those used for programming high-end GPUs might lead to worse performance on embedded GPUs. This is due to restricted features of embedded GPUs, such as, limited or no user-defined memory, small instruction-set, limited number of registers, among others. We propose techniques to overcome such challenges, e.g., by distributing the workload between GPUs and multi-core CPUs, similar to the spirit of heterogeneous computation.

Available from:
2013-03-13
Created:
2013-03-13
Last updated:
2013-03-13
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