On the Implementation of an Or-Parallel Prolog System for Clusters of Multicores

TL;DR

This paper presents a novel implementation of an or-parallel Prolog system tailored for clusters of multicores, combining shared and distributed memory architectures to improve parallel execution efficiency.

Contribution

It introduces a layered computational model with two scheduling levels and extends the YapOr system to exploit or-parallelism across teams of workers in multicore clusters.

Findings

Speedups increase with more workers per team.

Speedups improve with more teams across nodes.

The platform effectively exploits implicit or-parallelism.

Abstract

Nowadays, clusters of multicores are becoming the norm and, although, many or-parallel Prolog systems have been developed in the past, to the best of our knowledge, none of them was specially designed to explore the combination of shared and distributed memory architectures. In recent work, we have proposed a novel computational model specially designed for such combination which introduces a layered model with two scheduling levels, one for workers sharing memory resources, which we named a team of workers, and another for teams of workers (not sharing memory resources). In this work, we present a first implementation of such model and for that we revive and extend the YapOr system to exploit or-parallelism between teams of workers. We also propose a new set of built-in predicates that constitute the syntax to interact with an or-parallel engine in our platform. Experimental results show that our implementation is able to increase speedups as we increase the number of workers per team, thus taking advantage of the maximum number of cores in a machine, and to increase speedups as we increase the number of teams, thus taking advantage of adding more computer nodes to a cluster. We thus argue that our platform is an efficient and viable alternative for exploiting implicit or-parallelism in the currently available clusters of low cost multicore architectures.