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Run-Time Component Allocation in CPU-GPU Embedded Systems
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9794-5497
Swedish Institute of Computer Science (SICS), Sweden.ORCID iD: 0000-0002-1512-0844
2017 (English)In: 32nd ACM SIGAPP Symposium On Applied Computing SAC2017, 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 solvedby 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 system, s GPU, GPU monitoring, Monitor
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-35427DOI: 10.1145/3019612.3019785Scopus ID: 2-s2.0-85020925797ISBN: 978-1-4503-4486-9 (print)OAI: oai:DiVA.org:mdh-35427DiVA: diva2:1108112
Conference
32nd ACM SIGAPP Symposium On Applied Computing SAC2017, 03 Apr 2017, Marrakesh, Morocco
Projects
DPAC - Dependable Platforms for Autonomous systems and Control
Available from: 2017-06-12 Created: 2017-06-12 Last updated: 2017-07-06Bibliographically approved

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Campeanu, Gabriel

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
  • en-GB
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  • fi-FI
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  • nn-NB
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
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