Probabilistic points-to analysis

  • Authors:

  • Yuan-Shin Hwang;Peng-Sheng Chen;Jenq Kuen Lee;Roy Dz-Ching Ju

  • Affiliations:

  • Department of Computer Science, National Taiwan Ocean University, Taiwan;Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan;Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan;Microprocessor Research Lab., Intel Corporation, Santa Clara, CA

  • Venue:
  • LCPC'01 Proceedings of the 14th international conference on Languages and compilers for parallel computing
  • Year:
  • 2001

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Abstract

Information gathered by the existing pointer analysis techniques can be classified as must aliases or definitely-points-to relationships, which hold for all executions, and may aliases or possibly-points-to relationships, which might hold for some executions. Such information does not provide quantitative descriptions to tell how likely the conditions will hold for the executions, which are needed for modern compiler optimizations, and thus has hindered compilers from more aggressive optimizations. This paper addresses this issue by proposing a probabilistic points-to analysis technique to compute the probability of each points-to relationship. Initial experiments are done by incorporating the probabilistic data flow analysis algorithm into SUIF and MachSUIF, and preliminary experimental results show the probability distributions of points-to relationships in several benchmark programs. This work presents a major enhancement for pointer analysis to keep up with modern compiler optimizations.