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Developing CPU-GPU Embedded Systems using Platform-Agnostic Components
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9794-5497
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-8461-0230
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-0165-3743
2017 (English)In: Proceedings - 43rd Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2017, 2017, 176-180 p., 8051345Conference paper, Published paper (Refereed)
Abstract [en]

Nowadays, there are many embedded systems with different architectures that have incorporated GPUs. However, it is difficult to develop CPU-GPU embedded systems using component-based development (CBD), since existing CBD ap- proaches have no support for GPU development. In this context, when targeting a particular CPU-GPU platform, the component developer is forced to construct hardware-specific components, which are problematic to (re-)use in different contexts. More- over, hard-coding specific GPU-usage characteristics (e.g., the number of utilized GPU threads) inside the component is not possible without making detailed assumptions about the system in which the component is used, which conflicts with separation- of-concerns CBD principle. The paper presents a solution to allow component-based development of platform-agnostic CPU-GPU embedded systems through: i) high-level API, ii) adapters, and iii) code template. The API abstracts the specifics of the different platforms, while the adapters externalize hardware-specific activities outside components. We also raise the decision regarding the GPU- usage specifications, from the component to the system level. Furthermore, to minimize the development effort, we provide a code template that contains ready-made code fragments required for GPU development. As a case study, we examine the feasibility of our solution applied on a component-based vision system of an underwater robot.

Place, publisher, year, edition, pages
2017. 176-180 p., 8051345
Keyword [en]
embedded systems, GPU, CBD, component-based development, CPU-GPU embedded systems
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-37012DOI: 10.1109/SEAA.2017.20Scopus ID: 2-s2.0-85034422676ISBN: 9781538621400 (electronic)OAI: oai:DiVA.org:mdh-37012DiVA: diva2:1160393
Conference
43rd Euromicro Conference on Software Engineering and Advanced Applications SEAA'17, 30 Aug 2017, Vienna, Austria
Projects
RALF3 - Software for Embedded High Performance Architectures
Available from: 2017-11-27 Created: 2017-11-27 Last updated: 2017-12-07Bibliographically approved

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Campeanu, GabrielCarlson, JanSentilles, Séverine

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