Reducing nondeterminism while specializing logic programs

Abstract

Program specialization is a collection of program transformation techniques for improving program efficiency by exploiting some information available at compile-time about the input data. We show that current techniques for program specialization based on partial evaluation do not perform well on nondeterministic logic programs. We then consider a set of transformation rules which extend the ones used for partial evaluation, and we propose a strategy to direct the application of these extended rules so to derive very efficient specialized programs. The efficiency improvements which may even be exponential, are achieved because the derived programs are semi-deterministic and the operations which are performed by the initial programs in different branches of the computation trees, are performed in the specialized programs within single branches. We also make use of mode information to guide the unfolding process and to reduce nondeterminism. To exemplify our technique, we show that we can automatically derive very efficient semi-deterministic matching programs and semi-deterministic parsers for regular languages. The derivations we have performed could not have been done by previously known partial evaluation techniques.