TY - GEN
T1 - A distributed power grid analysis framework from sequential stream graph
AU - Lin, Chun Xun
AU - Huang, Tsung Wei
AU - Yu, Ting
AU - Wong, Martin D.F.
N1 - Funding Information:
This work is partially supported by the National Science Foundation under Grant CCF-1421563 and CCF-171883.
Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/5/30
Y1 - 2018/5/30
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85049431095&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049431095&partnerID=8YFLogxK
U2 - 10.1145/3194554.3194560
DO - 10.1145/3194554.3194560
M3 - Conference contribution
AN - SCOPUS:85049431095
T3 - Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI
SP - 183
EP - 188
BT - GLSVLSI 2018 - Proceedings of the 2018 Great Lakes Symposium on VLSI
PB - Association for Computing Machinery
T2 - 28th Great Lakes Symposium on VLSI, GLSVLSI 2018
Y2 - 23 May 2018 through 25 May 2018
ER -