@inproceedings{778c3a0d737443e5a6c852d78c68d330,
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 = "Lin, {Chun Xun} and Huang, {Tsung Wei} and Ting Yu and Wong, {Martin D.F.}",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 28th Great Lakes Symposium on VLSI, GLSVLSI 2018 ; Conference date: 23-05-2018 Through 25-05-2018",
year = "2018",
month = may,
day = "30",
doi = "10.1145/3194554.3194560",
language = "English (US)",
series = "Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI",
publisher = "Association for Computing Machinery",
pages = "183--188",
booktitle = "GLSVLSI 2018 - Proceedings of the 2018 Great Lakes Symposium on VLSI",
address = "United States",
}