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GA-Based Mapping and Scheduling of HSDF Graphs on Multiprocessor Platforms

  • Hao Wu
  • Nenggan Zheng
  • Hong Li
  • Zonghua GuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11204)

Abstract

Synchronous Dataflow (SDF) is a widely-used model-of-computation for signal processing and multimedia applications. We address the problem of mapping a Homogeneous Synchronous Dataflow (HSDF) graph onto a multiprocessor platform with the objective of maximizing system throughput. Since the problem is a NP-hard combinatorial optimization problem, it computationally infeasible to use exhaustive search to obtain optimal solutions for large applications. In this paper, we apply Genetic Algorithms to search the design space of all possible actor-to-processor mappings and static-order schedules on each processor to find a near-optimal solution, and compare the performance and scalability of GA with the exact solution technique based on SAT solving.

Keywords

Synchronous Dataflow (SDF) Genetic Algorithms Multiprocessor systems 

Notes

Acknowledgements

This work is partially supported by NSFC Project # 61672454 and # 61602404; Zhejiang Provincial Natural Science Foundation Project # LY16F020007.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Computer ScienceZhejiang UniversityHangzhouChina
  2. 2.Qiushi Academy for Advanced StudiesZhejiang UniversityHangzhouChina

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