Parallel multi-level analytical global placement on graphics processing units

  • Authors:
  • Jason Cong;Yi Zou

  • Affiliations:
  • University of California, Los Angeles, CA;University of California, Los Angeles, CA

  • Venue:
  • Proceedings of the 2009 International Conference on Computer-Aided Design
  • Year:
  • 2009

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Abstract

GPU platforms are becoming increasingly attractive for implementing accelerators because they feature a larger number of cores with improved programmability. In this paper, we describe our implementation of a state-of-the-art academic multi-level analytical placer mPL [8] on Nvidia's massively parallel GT200 series platforms. We detail our efforts on performance tuning and optimizations. When compared to software implementation on Intel's recent generation Xeon CPU, the speed of the global placement part of mPL is 15X faster on average using a Tesla C1060 card, with comparable WL. (less than 1% WL degradation on average)