No abstract

Most current data dependence tests cannot handle loop bounds or array subscripts that are symbolic, nonlinear expressions (e.g. A(n i+j), where 0 j n). In this paper, we describe a dependence t e s t , c alled the range test, that can handle such expressions. Brie y, the range test proves independence by determining whether certain symbolic inequalities hold for a permutation of the loop nest. Powerful symbolic analyses and constraint propagation techniques were developed t o p r ove such inequalities. T h e r ange test has been implemented in Polaris, a parallelizing compiler being developed at the University of Illinois.

Abstract. It is the goal of the Polaris project to develop a new parallelizing compiler that will overcome limitations of current compilers. While current parallelizing compilers may succeed on small kernels, they often fail to extract any meaningful parallelism from large applications. After a study of application codes, it was concluded that by adding a few new techniques to current compilers, automatic parallelization becomes possible. The techniques needed are interprocedural analysis, scalar and array privatization, symbolic dependence analysis, and advanced induction and reduction recognition and elimination, along with run-time techniques to allow data dependent behavior.