A Distributed Power Grid Analysis Framework from Sequential Stream Graph

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

doi:10.1145/3194554.3194560

A Distributed Power Grid Analysis Framework from Sequential Stream Graph

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

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Research output: Chapter in book/report/conference proceeding › Chapter › peer-review

7 Citations (Scopus)

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 language	English
Title of host publication	GLSVLSI '18: Great Lakes Symposium on VLSI 2018
Publisher	Association for Computing Machinery (ACM)
Pages	183–188
Number of pages	6
ISBN (Electronic)	9781450357241
DOIs	https://doi.org/10.1145/3194554.3194560
Publication status	Published - May 2018
Event	GLSVLSI '18: Great Lakes Symposium on VLSI 2018 - Chicago, United States Duration: 23 May 2018 → 25 May 2018 https://dl.acm.org/doi/proceedings/10.1145/3194554 (Link to conference proceedings)

Publication series

Name	Proceedings of the Great Lakes Symposium on VLSI

Conference

Conference	GLSVLSI '18: Great Lakes Symposium on VLSI 2018
Country/Territory	United States
City	Chicago
Period	23/05/18 → 25/05/18
Internet address	https://dl.acm.org/doi/proceedings/10.1145/3194554 (Link to conference proceedings)

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10.1145/3194554.3194560

Cite this

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title = "A Distributed Power Grid Analysis Framework from Sequential Stream Graph",

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.",

author = "Chun-Xun Lin and Tsung-Wei Huang and Ting Yu and Wong, {Martin D. F.}",

note = "Funding information: This work is partially supported by the National Science Foundation under Grant CCF-1421563 and CCF-171883. Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery; GLSVLSI '18: Great Lakes Symposium on VLSI 2018 ; Conference date: 23-05-2018 Through 25-05-2018",

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doi = "10.1145/3194554.3194560",

language = "English",

series = "Proceedings of the Great Lakes Symposium on VLSI",

publisher = "Association for Computing Machinery (ACM)",

pages = "183–188",

booktitle = "GLSVLSI '18: Great Lakes Symposium on VLSI 2018",

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url = "https://dl.acm.org/doi/proceedings/10.1145/3194554",

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Lin, C-X, Huang, T-W, Yu, T & Wong, MDF 2018, A Distributed Power Grid Analysis Framework from Sequential Stream Graph. in GLSVLSI '18: Great Lakes Symposium on VLSI 2018. Proceedings of the Great Lakes Symposium on VLSI, Association for Computing Machinery (ACM), pp. 183–188, GLSVLSI '18: Great Lakes Symposium on VLSI 2018, Chicago, Illinois, United States, 23/05/18. 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 et al.
GLSVLSI '18: Great Lakes Symposium on VLSI 2018. Association for Computing Machinery (ACM), 2018. p. 183–188 (Proceedings of the Great Lakes Symposium on VLSI).

Research output: Chapter in book/report/conference proceeding › Chapter › peer-review

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AB - 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.

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BT - GLSVLSI '18: Great Lakes Symposium on VLSI 2018

PB - Association for Computing Machinery (ACM)

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Y2 - 23 May 2018 through 25 May 2018

ER -

A Distributed Power Grid Analysis Framework from Sequential Stream Graph

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