Performance Analysis of Symbolic Analysis Techniques for Parallelizing Compilers

Abstract

Understanding symbolic expressions is an important capability of advanced program analysis techniques. Many current compiler techniques assume that coefficients of program expressions, such as array subscripts and loop bounds, are integer constants. Advanced symbolic handling capabilities could make these techniques amenable to real application programs. Symbolic analysis is also likely to play an important role in supporting higher–level programming languages and optimizations. For example, entire algorithms may be recognized and replaced by better variants. In pursuit of this goal, we have measured the degree to which symbolic analysis techniques affect the behavior of current parallelizing compilers. We have chosen the Polaris parallelizing compiler and studied the techniques such as range analysis – which is the core symbolic analysis in the compiler – expression propagation, and symbolic expression manipulation. To measure the effect of a technique, we disabled it individually, and compared the performance of the resulting program with the original, fully-optimized program. We found that symbolic expression manipulation is important for most programs. Expression propagation and range analysis is important in few programs only, however they can affect these programs significantly. We also found that in all but one programs, a simpler form of range analysis – control range analysis – is sufficient.

This material is based upon work supported by the National Science Foundation under Grant, No. 9974976-EIA, 9975275-EIA, and 0103582-EIA. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.