Task-to-Thread Mapping in OpenMP Using Fuzzy Decision Making

• Published in: ACM SIGAda Ada Letters 2024-12, Vol.44 (1), p.107-111
• Main Authors: Samadi, Mohammad, Carvalho, Tiago, Miguel Pinho, Luís, Royuela, Sara
• Format: Article
• Language: English
• Subjects: Computer systems organization; Computer systems organization / Embedded and cyber-physical systems; Computer systems organization / Embedded and cyber-physical systems / Embedded systems; Computer systems organization / Real-time systems; Computer systems organization / Real-time systems / Real-time system architecture; Computing methodologies; Computing methodologies / Parallel computing methodologies; Software and its engineering; Software and its engineering / Software organization and properties; Software and its engineering / Software organization and properties / Contextual software domains; Software and its engineering / Software organization and properties / Contextual software domains / Operating systems; Software and its engineering / Software organization and properties / Contextual software domains / Operating systems / Process management; Software and its engineering / Software organization and properties / Contextual software domains / Operating systems / Process management / Multithreading; Software and its engineering / Software organization and properties / Contextual software domains / Operating systems / Process management / Scheduling; Theory of computation; Theory of computation / Theory and algorithms for application domains; Theory of computation / Theory and algorithms for application domains / Machine learning theory
• ISSN: 1094-3641
• DOI: 10.1145/3706601.3706620

The performance of shared-resource multi-core hardware platforms in complex cyber-physical systems (CPSs), e.g., automotive industry, can be improved using task-based parallelism through OpenMP. However, most CPS require certain level of predictability, which challenges the efficient implementation of the task-to-thread mapping process. This exploratory work build on the fact that existing mapping methods mostly use elementary or heuristic algorithms, and the idea that artificial intelligence (AI) algorithms can be used to enhance the efficiency of such processes. Accordingly, this paper (1) evaluates the suitability of AI-based techniques in improving the performance of task-to-thread mapping in the OpenMP framework, and (2) proposes a hypothesis to perform an intelligent mapping using fuzzy logic for multi-queue schedulers to improve the predictability of the system.