New Shape Analysis and Interprocedural Techniques for Automatic Parallelization of C Codes

Abstract

Automatic parallelization of codes with complex data structures is becoming very important. These complex, and often recursive, data structures are widely used in scientific computing. Shape analysis is one of the key steps in the automatic parallelization of such codes. In this paper we extend the Static Shape Graph (SSG) method to enable the successful and accurate detection of complex doubly-linked structures. We have also included an interprocedural analysis in our framework. These techniques have been implemented in a compiler, which has been validated for several C codes. In particular, we present the results the compiler achieves for the C sparse LU factorization algorithm and for a recursive code generating a tree data structure. The output SSG perfectly describes the complex data structure used in these codes.