Paper Info
Title
Probabilistic points-to analysis
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.
Year
DOI
Venue
2001
10.1007/3-540-35767-X_19
LCPC
Keywords
Field
DocType
modern compiler optimizations,definitely-points-to relationship,probabilistic points-to analysis technique,pointer analysis,benchmark program,probabilistic data flow analysis,existing pointer analysis technique,points-to relationship,probability distribution,aggressive optimizations,compiler optimization,data flow analysis
Pointer analysis,Pointer (computer programming),Programming language,Computer science,Parallel computing,Data-flow analysis,Optimizing compiler,Theoretical computer science,Compiler,Probability distribution,Probabilistic logic,Distributed computing
Conference
Volume
ISSN
ISBN
2624
0302-9743
3-540-04029-3
Citations 
PageRank 
References 
12
0.93
9
Authors
4
Name
Order
Citations
PageRank
Yuan-Shin Hwang140340.55
Peng-Sheng Chen2593.47
Jenq Kuen Lee345948.71
Roy Dz-ching Ju432621.37