On applying or-parallelism and tabling to logic programs

2002

Abstract Tabling or memoing is a technique where one stores intermediate answers to a problem so that they can be reused in further calls. Tabling is of interest to logic programming because it addresses some of the most significant weaknesses of Prolog. Namely, it can guarantee termination for programs with the bounded term-size property. Tabled programs exhibit a more complex execution mechanism than traditional Prolog's left-to-right search with backtracking.

2000

One of the advantages of logic programming is the fact that it offers many sources of implicit parallelism, such as and-parallelism and or-parallelism. Arguably, or-parallel systems, such as Aurora and Muse, have been the most successful parallel logic programming systems so far. Or-parallel systems rely on techniques such as Environment Copying to address the problem that branches being explored in parallel may need to assign different bindings for the same shared variable.

2001

A b stract O ne of the advantages of logic programming is that it o ff ers several sources of implicit parallelism, such as and-parallelism (AN DP) and or-parallelism (OR P). R ecent research has concentrated on integrating the diff erent forms of parallelism into a single combined system. In this work we deal with the problem of integrating OR P and independent and-parallelism (Ia P), the two forms of parallelism most suitable for parallel Prolog systems.

Abstract One of the advantages of logic programming (LP) and constraint logic programming (CLP) is the fact that one can exploit implicit parallelism in logic programs. Logic programs have two major forms of implicit parallelism: orparallelism (ORP) and and-parallelism (ANDP). In this work we survey some of work that has been taking place within the C LoPn project towards fast execution of logic programs.

2001

Tabling is an implementation technique that improves the declarativeness and expressiveness of Prolog by reusing solutions to goals. Quite a few interesting applications of tabling have been developed in the last few years, andsev eral are by nature non-deterministic. This raises the question of whether parallel search techniques can be usedto improve the performance of tabled applications. In this work we demonstrate that the mechanisms proposed to parallelize search in the context of SLD resolution naturally generalize to parallel tabledcomputations, and that resulting systems can achieve goodp erformance on multi-processors. To do so, we present the OPTYap parallel engine. In our system individual SLG engines communicate data through stack copying. Completion is detected through a novel parallel completion algorithm that builds upon the data structures proposed for or-parallelism. Scheduling is simplified by building on previous research on or-parallelism. We show initial performance results for our implementation. Our best result is for an actual application, model checking, where we obtain linear speedups.