Abstract
Logic Programming languages, such as Prolog, offer a great potential for the exploitation of implicit parallelism. One of the most noticeable sources of implicit parallelism in Prolog programs is or-parallelism. Or-parallelism arises from the simultaneous evaluation of a subgoal call against the clauses that match that call. Nowadays, multicores and clusters of multicores are becoming the norm and, although, many 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. Conceptually, an or-parallel Prolog system consists of two components: an or-parallel engine (i.e., a set of independent Prolog engines which we named a team of workers) and a scheduler. In this work, we propose a team-based scheduling model to efficiently exploit parallelism between different or-parallel engines running on top of clusters of multicores. Our proposal defines a layered approach where a second-level scheduler specifies a clean interface for scheduling work between the base or-parallel engines, thus enabling different scheduling combinations to be used for distributing work among workers inside a team and among teams.

Abstract
Rational terms or rational trees are terms with one or more infinite sub-terms but with a finite representation. Rational terms appeared as a side effect of omitting the occurs check in the unification of terms, but their support across Prolog systems varies and often fails to provide the expected functionality. A common problem is the lack of support for printing query bindings with rational terms. In this paper, we present a survey discussing the support of rational terms among different Prolog systems and we propose the integration of a Prolog predicate, that works in several existing Prolog systems, in order to overcome the technical problem of printing rational terms. Our rational term printing predicate could be easily adapted to work for the top query printouts, for user printing and for debugging purposes.

Abstract

Abstract

Abstract
Linear Meld is a concurrent forward-chaining linear logic programming language where logical facts can be asserted and retracted in a structured way. The database of facts is partitioned by the nodes of a graph structure which leads to parallelism if nodes are executed simultaneously. Communication arises whenever nodes send facts to other nodes by fact derivation. We present an overview of the virtual machine that we implemented to run Linear Meld on multicores, including code organization, thread management, rule execution and database organization for efficient fact insertion, lookup and deletion. Although our virtual machine is a work-in-progress, our results already show that Linear Meld is not only capable of scaling graph and machine learning programs but it also exhibits some interesting performance results when compared against other programming languages.

Abstract
Linear Meld is a concurrent forward-chaining linear logic programming language where logical facts can be asserted and retracted in a structured way. In Linear Meld, a program is seen as a database of logical facts and a set of derivation rules. The database of facts is partitioned by the nodes of a graph structure which leads to parallelism when nodes are executed simultaneously. Due to the foundations on linear logic, rules can retract facts in a declarative and structured fashion, leading to more expressive programs. We present the design and implementation of the virtual machine that we implemented to run Linear Meld on multicores, with particular focus on thread management, code organization, fact indexing, rule execution, and database organization for efficient fact insertion, lookup and deletion. Our results show that the virtual machine is capable of scaling programs with up to 16 threads and also exhibits interesting scalar performance results due to our indexing optimizations.