Heuristic-based task-to-thread mapping in multi-core processors
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
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ProjectAMPERE - Automated photovoltaic cell and Module industrial Production to regain and secure European Renewable Energy market (EC-H2020-745601)
OpenMP can be used in real-time applications to enhance system performance. However, predictability of OpenMP applications is still a challenge. This paper investigates heuristics for the mapping of OpenMP task graphs in underlying threads, for the development of time-predictable OpenMP programs. These approaches are based on a global scheduling queue, as well as per-thread allocation queues. The proposed method is divided into scheduling and allocation phases. In the former phase, OpenMP task-parts are discovered from OpenMP graph and placed in the scheduling queue. Afterwards, an appropriate allocation queue is selected for each task-part using four heuristic algorithms. In the latter phase, the best task-part is selected from the allocation queue to be allocated to and executed by an idle thread. Preliminary simulation results show that the new method overcomes BFS and WFS in terms of scheduling time and idle time.
CitationSamadi Gharajeh, M. [et al.]. Heuristic-based task-to-thread mapping in multi-core processors. A: IEEE International Conference on Emerging Technologies and Factory Automation. "2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA): 6-9 Sept. 2022". Institute of Electrical and Electronics Engineers (IEEE), 2022, ISBN 978-1-6654-9996-5. DOI 10.1109/ETFA52439.2022.9921453.