Buffer Minimization for Rate-Optimal Scheduling of Synchronous Dataflow Graphs on Multicore Systems

Ma, Mingze; Sakellariou, Rizos

doi:10.1007/978-3-319-49583-5_25

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Gu [26] presented a parallelized Pareto optimal scheduling method for scheduling SDFGs on heterogeneous multiprocessor platforms. This method makes a [27] proposed an exact scheduling algorithm based on an extension of self-timed execution (STE). The STE-based algorithm can maximize throughout and minimize buffer usage when execute an SDFG on a multicore system.…”

Section: Related Workmentioning

confidence: 99%

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Genetic scheduling policy on codelet model

Pei

Wang

Jiang

et al. 2018

Concurrency and Computation

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The Codelet Model is a fine-grained, event-driven hybrid parallel model inspired by dataflow, whose computing performance depends on the scheduling policy. An approximate optimal codelet scheduling policy based on the features of the task graphs is important to accelerate the performance of dataflow computer system. Therefore, we have proposed an adaptive genetic scheduling policy (GSP) for codelet by improving a "pure" genetic algorithm (IPGA) for given tasks with complex dependencies. It is verified that the genetic scheduling policy is effective according to bunches of experimental results.

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Section: Related Workmentioning

confidence: 99%

“…This method makes a trade‐off between throughput and energy consumption. Ma and Sakellariou 19 proposed an exact scheduling algorithm based on an extension of self‐timed execution (STE). The STE‐based algorithm can maximize throughout and minimize buffer usage when execute an SDFG on a multicore system.…”

Section: Related Workmentioning

confidence: 99%

Genetic scheduling policy on codelet model

Pei

Wang

Jiang

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

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Code-size-aware Scheduling of Synchronous Dataflow Graphs on Multicore Systems

Sakellariou

2021

ACM Trans. Embed. Comput. Syst.

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Synchronous dataflow graphs are widely used to model digital signal processing and multimedia applications. Self-timed execution is an efficient methodology for the analysis and scheduling of synchronous dataflow graphs. In this article, we propose a communication-aware self-timed execution approach to solve the problem of scheduling synchronous dataflow graphs on multicore systems with communication delays. Based on this communication-aware self-timed execution approach, four communication-aware scheduling algorithms are proposed using different allocation rules. Furthermore, a code-size-aware mapping heuristic is proposed and jointly used with a proposed scheduling algorithm to reduce the code size of SDFGs on multicore systems. The proposed scheduling algorithms are experimentally evaluated and found to perform better than existing algorithms in terms of throughput and runtime for several applications. The experiments also show that the proposed code-size-aware mapping approach can achieve significant code size reduction with limited throughput degradation in most cases.

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Buffer Minimization for Rate-Optimal Scheduling of Synchronous Dataflow Graphs on Multicore Systems

Cited by 2 publications

References 18 publications

Genetic scheduling policy on codelet model

Genetic scheduling policy on codelet model

Code-size-aware Scheduling of Synchronous Dataflow Graphs on Multicore Systems

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