Data locality enhancement by memory reduction

  • Authors:

  • Yonghong Song;Rong Xu;Cheng Wang;Zhiyuan Li

  • Affiliations:

  • Sun Microsystems, Inc., 901 San Antonio Rd., Palo Alto, CA;Department of Computer Sciences, Purdue University, West Lafayette, IN;Department of Computer Sciences, Purdue University, West Lafayette, IN;Department of Computer Sciences, Purdue University, West Lafayette, IN

  • Venue:
  • ICS '01 Proceedings of the 15th international conference on Supercomputing
  • Year:
  • 2001

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Abstract

In this paper, we propose memory reduction as a new approach to data locality enhancement. Under this approach, we use the compiler to reduce the size of the data repeatedly referenced in a collection of nested loops. Between their reuses, the data will more likely remain in higher-speed memory devices, such as the cache. Specifically, we present an optimal algorithm to combine loop shifting, loop fusion and array contraction to reduce the temporary array storage required to execute a collection of loops. When applied to 20 benchmark programs, our technique reduces the memory requirement, counting both the data and the code, by 51% on average. The transformed programs gain a speedup of 1.40 on average, due to the reduced footprint and, consequently, the improved data locality.