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A Back-End for the SkePU Skeleton Programming Library targeting the Low-Power Multicore Vision Processor Myriad 2
Linköping University, Department of Computer and Information Science, Software and Systems.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The SkePU skeleton programming library utilises algorithmic skeletons to offer a high-level approach for creating parallel applications. By using different back-ends, SkePU applications can run on multicore systems, GPGPU systems, and computer clusters.

Myriad 2 is a low-power multicore vision processor for embedded systems, capable of running parallel applications energy-efficiently. Myriad 2 is heterogeneous, containing two different processor architectures and memories with different characteristics.

In this thesis, we implement a back-end for SkePU, that allows SkePU applications to run on Myriad 2. We describe how the back-end is designed and evaluate the performance of SkePU applications running on Myriad 2. By conducting a series of benchmarks, we show that our back-end achieves enough performance to make SkePU a useful tool for creating applications for Myriad 2. We also show that SkePU applications can run more energy-efficiently on Myriad 2, compared to a GPGPU system.

Place, publisher, year, edition, pages
2016. , p. 39
Keywords [en]
parallel
National Category
Computer Engineering Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-132970ISRN: LIU-IDA/LITH-EX-A--16/055--SEOAI: oai:DiVA.org:liu-132970DiVA, id: diva2:1052541
Subject / course
Computer Engineering
Presentation
2016-11-15, Donald Knuth, 10:15 (English)
Supervisors
Examiners
Available from: 2016-12-12 Created: 2016-12-06 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf