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Run-time component allocation in CPU-GPU embedded systems
Mälardalen University, Sweden.
RISE - Research Institutes of Sweden, ICT, SICS.
2017 (English)In: Proceedings of the ACM Symposium on Applied Computing, 2017, 1259-1265 p.Conference paper, Published paper (Refereed)
Abstract [en]

Nowadays, many of the modern embedded applications such as vehicles and robots, interact with the environment and receive huge amount of data through various sensors such as cameras and radars. The challenge of processing large amount of data, within an acceptable performance, is solved by employing embedded systems that incorporate complementary attributes of CPUs and Graphics Processing Units (GPUs), i.e., sequential and parallel execution models.component-based development (CBD) is a software engineering methodology that augments the applications development through reuse of software blocks known as components. In developing a CPU-GPU embedded application using CBD, allocation of components to different processing units of the platform is an important activity which can affect the overall performance of the system. In this context, there is also often the need to support and achieve run-time component allocation due to various factors and situations that can happen during system execution, such as switching off parts of the system for energy saving. In this paper, we provide a solution that dynamically allocates components using various system information such as the available resources (e.g., available GPU memory) and the software behavior (e.g., in terms of GPU memory usage). The novelty of our work is a formal allocation model that considers GPU system characteristics computed on-the-fly through software monitoring solutions. For the presentation and validation of our solution, we utilize an existing underwater robot demonstrator.

Place, publisher, year, edition, pages
2017. 1259-1265 p.
Keyword [en]
Component allocation, Component-based development, CPU-GPU, Dynamic allocation, Embedded systems, GPU, GPU monitoring, Monitor
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-30969DOI: 10.1145/3019612.3019785Scopus ID: 2-s2.0-85020925797ISBN: 9781450344869 OAI: oai:DiVA.org:ri-30969DiVA: diva2:1138602
Conference
32nd Annual ACM Symposium on Applied Computing, SAC 2017, 4 April 2017 through 6 April 2017
Available from: 2017-09-06 Created: 2017-09-06 Last updated: 2017-09-06Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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
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