Locality-Driven Parallel Static Analysis for Power Delivery Networks

Zhiyu Zeng, Zhuo Feng, Peng Li, Vivek Sarin

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Large VLSI on-chip Power Delivery Networks (PDNs) are challenging to analyze due to the sheer network complexity. In this article, a novel parallel partitioning-based PDN analysis approach is presented. We use the boundary circuit responses of each partition to divide the full grid simulation problem into a set of independent subgrid simulation problems. Instead of solving exact boundary circuit responses, a more efficient scheme is proposed to provide near-exact approximation to the boundary circuit responses by exploiting the spatial locality of the flip-chip-type power grids. This scheme is also used in a block-based iterative error reduction process to achieve fast convergence. Detailed computational cost analysis and performance modeling is carried out to determine the optimal (or near-optimal) number of partitions for parallel implementation. Through the analysis of several large power grids, the proposed approach is shown to have excellent parallel efficiency, fast convergence, and favorable scalability. Our approach can solve a 16-million-node power grid in 18 seconds on an IBM p5-575 processing node with 16 Power5+ processors, which is 18.8X faster than a state-of-the-art direct solver. © 2011 ACM.
Original languageEnglish (US)
Pages (from-to)1-17
Number of pages17
JournalACM Transactions on Design Automation of Electronic Systems
Issue number3
StatePublished - Jun 1 2011
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This material is based on work supported by the National Science Foundation under grant no. 0903485 and grant no. 0747423, and SRC under contract 2009-TJ-1987. It is also based on work supported by award number KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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