Designing programming languages for the analyzability of pointer data structures

Abstract

In this paper we propose a programming language mechanism and associated compiler techniques which significantly enhance the analyzability of pointer-based data structures frequently used in non-scientific programs. Our approach is based on exploiting two important properties of pointer data structures: structural inductivity and speculative traversability. Structural inductivity facilitates the application of a static interference analysis method for such pointer data structures based on path matrices, and speculative traversability is utilized by a novel loop unrolling technique for while loops that exploit fine-grain parallelism by speculatively traversing such data structures. The effectiveness of this approach is demonstrated by applying it to a collection of loops found in typical non-scientific C programs.