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Self-timed periodic scheduling of data-dependent tasks in embedded streaming applications

Abstract : Developers increasingly use streaming languages to write embedded many-core applications that process large volumes of data with high throughput. Because they enable periodic scheduling, cyclo-static models of computation and their variants are well fitted to modern real-time applications. Nevertheless, most existing works have proposed periodic scheduling that ignore latency or can even have a negative impact on it: the results are quite far from those obtained under Self-Timed scheduling (STS). In this paper, we introduce a new scheduling policy noted Self-Timed Periodic (STP), which is an execution model combining self-timed scheduling with periodic scheduling: STS improves the performance metrics of the programs, while the periodic model captures the timing aspects. We evaluate the performance of our scheduling policy for a set of 10 real-life streaming applications. We find that in most of the cases, our approach gives a significant improvement in latency compared to the Strictly Periodic Schedule (SPS), and competes well with STS. The experiments also show that, for more than 90% of the benchmarks, STP scheduling results in optimal throughput.
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Submitted on : Monday, July 9, 2018 - 8:20:38 AM
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X.K. Do, A. Dkhil, S. Louise. Self-timed periodic scheduling of data-dependent tasks in embedded streaming applications. Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science, Nov 2015, Zhangjiajie, China. pp.458-478, ⟨10.1007/978-3-319-27122-4_32⟩. ⟨cea-01832764⟩



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