Pareto Optimal Scheduling for Synchronous Data Flow Graphs on Heterogeneous Multiprocessor

Gu, Yulei; Zhu, Xun; Zhang, Guangquan; He, Yifan

doi:10.1109/iceccs.2016.020

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…In the recent years, most scheduling methods of the dataflow target the multi-objective optimization on the synchronous dataflow graph (SDFG). 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).…”

Section: Related Workmentioning

confidence: 99%

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%

Genetic scheduling policy on codelet model

Pei

Wang

Jiang

et al. 2018

Concurrency and Computation

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A Unified Framework for Throughput Analysis of Streaming Applications under Memory Constraints

Zhu

2017

2017 22nd International Conference on Engineering of Complex Computer Systems (ICECCS)

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Optimal Offloading for Streaming Applications in Mobile Edge Computing

Sun

Zhu

Gao

2021

J CIRCUIT SYST COMP

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With the rapid development of smart mobile devices, mobile applications are becoming more and more popular. Since mobile devices usually have constrained computing capacity, computation offloading to mobile edge computing (MEC) to achieve a lower latency is a promising paradigm. In this paper, we focus on the optimal offloading problem for streaming applications in MEC. We present solutions to find offloading policies of streaming applications to achieve an optimal latency. Streaming applications are modeled with synchronous data flow graphs. Two architecture assumptions are considered — with sufficient processors on both the local device and the MEC server, and with a limited number of processors on both sides. The problem is generally NP-complete. We present an exact algorithm and a heuristic algorithm for the former architecture assumption and a heuristic method for the latter. We carry out our experiments on a practical application and thousands of synthetic graphs to comprehensively evaluate our methods. The experimental results show that our methods are effective and computationally efficient.

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Pareto Optimal Scheduling for Synchronous Data Flow Graphs on Heterogeneous Multiprocessor

Cited by 3 publications

References 24 publications

Genetic scheduling policy on codelet model

Genetic scheduling policy on codelet model

A Unified Framework for Throughput Analysis of Streaming Applications under Memory Constraints

Optimal Offloading for Streaming Applications in Mobile Edge Computing

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