Sm@rtConfig: A context-aware runtime and tuning system using an aspect-oriented approach for data intensive engineering applications

Distributing the workload upon all available Processing Units (PUs) of a high-performance heterogeneous platform (e.g., PCs composed by CPU–GPUs) is a challenging task, since the execution cost of a task on distinct PUs is non-deterministic and affected by parameters not known a priori. This paper p...

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Bibliographic Details
Published inControl engineering practice Vol. 21; no. 2; pp. 204 - 217
Main Authors Binotto, Alécio Pedro Delazari, Wehrmeister, Marco Aurélio, Kuijper, Arjan, Pereira, Carlos Eduardo
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.02.2013
Subjects
Aspect Oriented Software Development
GPUs
Load-balancing
Model-Driven Engineering
Scheduling
Solvers for linear equations systems
Model-Driven Engineering
Aspect Oriented Software Development
Scheduling
Load-balancing
GPUs
Solvers for linear equations systems
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Summary:Distributing the workload upon all available Processing Units (PUs) of a high-performance heterogeneous platform (e.g., PCs composed by CPU–GPUs) is a challenging task, since the execution cost of a task on distinct PUs is non-deterministic and affected by parameters not known a priori. This paper presents Sm@rtConfig, a context-aware runtime and tuning system based on a compromise between reducing the execution time of engineering applications and the cost of tasks' scheduling on CPU–GPUs' platforms. Using Model-Driven Engineering and Aspect Oriented Software Development, a high-level specification and implementation for Sm@rtConfig has been created, aiming at improving modularization and reuse in different applications. As case study, the simulation subsystem of a CFD application has been developed using the proposed approach. These system's tasks were designed considering only their functional concerns, whereas scheduling and other non-functional concerns are handled by Sm@rtConfig aspects, improving tasks modularity. Although Sm@rtConfig supports multiple PUs, in this case study, these tasks have been scheduled to execute on an platform composed by one CPU and one GPU. Experimental results show an overall performance gain of 21.77% in comparison to the static assignment of all tasks only to the GPU.
ISSN:0967-0661
1873-6939
DOI:10.1016/j.conengprac.2012.10.001