Accelerated path-based timing analysis with MapReduce

Tsung Wei Huang, Martin D.F. Wong

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

Abstract

Path-based timing analysis (PBA) is a pivotal step to achieve accurate timing signoff. A core primitive extracts a large set of paths subject to path-specific or less-pessimistic timing update. However, this process in nature demands a very high computational complexity and thus has been a major bottleneck in accelerating timing closure. Therefore, we introduce in this paper a fast and scalable PBA framework with MapReduce - a recent programming paradigm invented by Google for big-data processing. Inspired by the spirit of MapReduce, we formulate our problem into tasks that are associated with keys and values and perform massively-parallel map and reduce operations on a distributed system. Experimental results demonstrated that our approach can easily analyze million nodes in a single minute.

Original languageEnglish (US)
Title of host publicationISPD 2015 - Proceedings of the ACM International Symposium on Physical Design 2015
PublisherAssociation for Computing Machinery
Pages103-110
Number of pages8
ISBN (Electronic)9781450333993
DOIs
StatePublished - Mar 29 2015
Event18th ACM International Symposium on Physical Design, ISPD 2015 - Monterey, United States
Duration: Mar 29 2015Apr 1 2015

Publication series

NameProceedings of the International Symposium on Physical Design
Volume29-March-2015

Other

Other18th ACM International Symposium on Physical Design, ISPD 2015
CountryUnited States
CityMonterey
Period3/29/154/1/15

Keywords

  • MapReduce
  • Path-based static timing analysis

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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  • Cite this

    Huang, T. W., & Wong, M. D. F. (2015). Accelerated path-based timing analysis with MapReduce. In ISPD 2015 - Proceedings of the ACM International Symposium on Physical Design 2015 (pp. 103-110). (Proceedings of the International Symposium on Physical Design; Vol. 29-March-2015). Association for Computing Machinery. https://doi.org/10.1145/2717764.2717771