Abstract
Scheduling of tasks in multicore parallel architectures is challenging due to the execution time being a nondeterministic value. We propose a task-affinity real-time scheduling heuristics algorithm (TARTSH) for periodic and independent tasks in a homogeneous multicore system based on a Parallel Execution Time Graph (PETG) to minimize the execution time. The main contributions of the paper include: construction of a Task Affinity Sequence through real experiment, finding the best parallel execution pairs and scheduling sequence based on task affinity, providing an efficient method to distinguish memory-intensive and memory-unintensive task. For experimental evaluation of our algorithm, a homogeneous multicore platform called NewBeehive with private L1 Cache and sharable L2 Cache has been designed. Theoretical and experimental analysis indicates that it is better to allocate the memory-intensive task and memory-unintensive task for execution in parallel. The experimental results demonstrate that our algorithm can find the optimal solution among all the possible combinations. The Maximum improvement of our algorithm is 15.6%).
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This work was supported by the National Natural Science Foundation of China (61572060, 61190125, 61472024), 973 Program (2013CB035503), and CERNET Innovation Project 2015 (NGII20151004).
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Li, Y., Niu, J., Zhang, J., Atiquzzaman, M., Long, X. (2017). Real-Time Scheduling for Periodic Tasks in Homogeneous Multi-core System with Minimum Execution Time. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_16
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DOI: https://doi.org/10.1007/978-3-319-59288-6_16
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