A Distributed Power Grid Analysis Framework from Sequential Stream Graph

Chun-Xun Lin, Tsung-Wei Huang, Ting Yu, Martin D. F. Wong

Research output: Chapter in book/report/conference proceedingChapterpeer-review

5 Citations (Scopus)


The ever-increasing design complexities have overwhelmed what is offered by existing EDA tools. As a result, the recent EDA industry is driving the need for distributed computing to leverage large-scale compute-intensive problems, in particular, power grid analysis. In this paper, we introduce a distributed power grid analysis framework based on the stream graph model. We show that the stream graph model has better programmability over the MPI and enables flexible domain decomposition without limited by hardware resource. In addition, we design an efficient scheduling policy for this particular workload to maximize the cluster utilization to improve the performance. The experimental results demonstrated the promising performance of our framework that scales from single multi-core machines to a distributed computer cluster.
Original languageEnglish
Title of host publicationGLSVLSI '18: Great Lakes Symposium on VLSI 2018
PublisherAssociation for Computing Machinery (ACM)
Number of pages6
ISBN (Electronic)9781450357241
Publication statusPublished - May 2018
EventGLSVLSI '18: Great Lakes Symposium on VLSI 2018 - Chicago, United States
Duration: 23 May 201825 May 2018
https://dl.acm.org/doi/proceedings/10.1145/3194554 (Link to conference proceedings)

Publication series

NameProceedings of the Great Lakes Symposium on VLSI


ConferenceGLSVLSI '18: Great Lakes Symposium on VLSI 2018
Country/TerritoryUnited States
Internet address


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