GPU-accelerated Path-based Timing Analysis

Guannan Guo, Tsung-Wei Huang, Yibo Lin, Martin Wong

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

21 Citations (Scopus)


Path-based Analysis (PBA) is an important step in the design closure flow for reducing slack pessimism. However, PBA is extremely time-consuming. Recent years have seen many parallel PBA algorithms, but most of them are architecturally constrained by the CPU parallelism and do not scale beyond a few threads. To overcome this challenge, we propose in this paper a new fast and accurate PBA algorithm by harnessing the power of graphics processing unit (GPU). We introduce GPU-efficient data structures, high-performance kernels, and efficient CPU-GPU task decomposition strateiges, to accelerate PBA to a new performance milestone. Experimental results show that our method can speed up the state-of-the-art algorithm by 543× on a design of 1.6 million gates with exact accuracy. At the extreme, our method of 1 CPU and 1 GPU outperforms the state-of-the-art algorithm of 40 CPUs by 25-45×.

Original languageEnglish
Title of host publication58th ACM/IEEE Design Automation Conference - Proceedings 2021
Number of pages6
ISBN (Electronic)9781665432740
ISBN (Print)9781665432757
Publication statusPublished - 13 Dec 2021
Event58th ACM/IEEE Design Automation Conference, DAC 2021 - Online and , San Francisco, United States
Duration: 5 Dec 202131 Dec 2021 (Conference website) (Conference programme ) (Conference proceedings)

Publication series

NameACM/IEEE Design Automation Conference - Proceedings
ISSN (Print)0738-100X


Conference58th ACM/IEEE Design Automation Conference, DAC 2021
Country/TerritoryUnited States
CitySan Francisco
Internet address

Scopus Subject Areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modelling and Simulation


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