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 proceedingConference contribution

Abstract

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 (US)
Title of host publicationGLSVLSI 2018 - Proceedings of the 2018 Great Lakes Symposium on VLSI
PublisherAssociation for Computing Machinery
Pages183-188
Number of pages6
ISBN (Electronic)9781450357241
DOIs
StatePublished - May 30 2018
Event28th Great Lakes Symposium on VLSI, GLSVLSI 2018 - Chicago, United States
Duration: May 23 2018May 25 2018

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI

Other

Other28th Great Lakes Symposium on VLSI, GLSVLSI 2018
CountryUnited States
CityChicago
Period5/23/185/25/18

Fingerprint

Distributed computer systems
Scheduling
Decomposition
Hardware
Industry

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Lin, C. X., Huang, T. W., Yu, T., & Wong, M. D. F. (2018). A distributed power grid analysis framework from sequential stream graph. In GLSVLSI 2018 - Proceedings of the 2018 Great Lakes Symposium on VLSI (pp. 183-188). (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI). Association for Computing Machinery. https://doi.org/10.1145/3194554.3194560

A distributed power grid analysis framework from sequential stream graph. / Lin, Chun Xun; Huang, Tsung Wei; Yu, Ting; Wong, Martin D.F.

GLSVLSI 2018 - Proceedings of the 2018 Great Lakes Symposium on VLSI. Association for Computing Machinery, 2018. p. 183-188 (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lin, CX, Huang, TW, Yu, T & Wong, MDF 2018, A distributed power grid analysis framework from sequential stream graph. in GLSVLSI 2018 - Proceedings of the 2018 Great Lakes Symposium on VLSI. Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI, Association for Computing Machinery, pp. 183-188, 28th Great Lakes Symposium on VLSI, GLSVLSI 2018, Chicago, United States, 5/23/18. https://doi.org/10.1145/3194554.3194560
Lin CX, Huang TW, Yu T, Wong MDF. A distributed power grid analysis framework from sequential stream graph. In GLSVLSI 2018 - Proceedings of the 2018 Great Lakes Symposium on VLSI. Association for Computing Machinery. 2018. p. 183-188. (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI). https://doi.org/10.1145/3194554.3194560
Lin, Chun Xun ; Huang, Tsung Wei ; Yu, Ting ; Wong, Martin D.F. / A distributed power grid analysis framework from sequential stream graph. GLSVLSI 2018 - Proceedings of the 2018 Great Lakes Symposium on VLSI. Association for Computing Machinery, 2018. pp. 183-188 (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI).
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