SPM management using Markov chain based data access prediction

  • Authors:

  • Taylan Yemliha;Shekhar Srikantaiah;Mahmut Kandemir;Ozcan Ozturk

  • Affiliations:

  • Syracuse University, Syracuse, NY;Pennsylvania State University, University Park, PA;Pennsylvania State University, University Park, PA;Bilkent University, Ankara, Turkey

  • Venue:
  • Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
  • Year:
  • 2008

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Abstract

Leveraging the power of scratchpad memories (SPMs) available in most embedded systems today is crucial to extract maximum performance from application programs. While regular accesses like scalar values and array expressions with affine subscript functions have been tractable for compiler analysis (to be prefetched into SPM), irregular accesses like pointer accesses and indexed array accesses have not been easily amenable for compiler analysis. This paper presents an SPM management technique using Markov chain based data access prediction for such irregular accesses. Our approach takes advantage of inherent, but hidden reuse in data accesses made by irregular references. We have implemented our proposed approach using an optimizing compiler. In this paper, we also present a thorough comparison of our different dynamic prediction schemes with other SPM management schemes. SPM management using our approaches produces 12.7% to 28.5% improvements in performance across a range of applications with both regular and irregular access patterns, with an average improvement of 20.8%.