Abstract
Tabling is a commonly used technique in logic programming for avoiding cyclic behavior of logic programs and enabling more declarative program definitions. Furthermore, tabling often improves computational performance. Rational term are terms with one or more infinite sub-terms but with a finite representation. Rational terms can be generated in Prolog by omitting the occurs check when unifying two terms. Applications of rational terms include definite clause grammars, constraint handling systems, and coinduction. In this paper, we report our extension of YAP's Prolog tabling mechanism to support rational terms. We describe the internal representation of rational terms within the table space and prove its correctness. We then use this extension to implement a tabling based approach to coinduction. We compare our approach with current coinductive transformations and describe the implementation. In addition, we present an algorithm that ensures a canonical representation for rational terms.

Abstract

Abstract
Interest in the MapReduce programming model has been rekindled by Google in the past 10 years; its popularity is mostly due to the convenient abstraction for parallelization details this framework provides. State-of-the-art systems such as Google's, Hadoop or SAGA often provide added features like a distributed file system, fault tolerance mechanisms, data redundancy and portability to the basic MapReduce framework. However, these features pose an additional overhead in terms of system performance. In this work, we present a MapReduce design for Prolog which can potentially take advantage of hybrid parallel environments; this combination allies the easy declarative syntax of logic programming with its suitability to represent and handle multi-relational data due to its first order logic basis. MapReduce for Prolog addresses efficiency issues by performing load balancing on data with different granularity and allowing for parallelization in shared memory, as well as across machines. In an era where multicore processors have become common, taking advantage of a cluster's full capabilities requires the hybrid use of parallelism.

Abstract
We have designed a new logic programming language called LM (Linear Meld) for programming graph- based algorithms in a declarative fashion. Our language is based on linear logic, an expressive logical system where logical facts can be consumed. Because LM integrates both classical and linear logic, LM tends to be more expressive than other logic programming languages. LM programs are naturally concurrent because facts are partitioned by nodes of a graph data structure. Computation is performed at the node level while communication happens between connected nodes. In this paper, we present the syntax and operational semantics of our language and illustrate its use through a number of examples.

Abstract
Tabling is an implementation technique that improves the declarativeness and expressiveness of Prolog in dealing with recursion and redundant sub-computations. A critical component in the implementation of an efficient tabling framework is the design of the data structures and algorithms to access and manipulate tabled data. One of the most successful data structures for tabling is tries. In previous work, our initial approach to deal with concurrent table accesses, implemented on top of the Yap Prolog system, was to use lock-based trie data structures. In this work, we propose a new design based on lock-free data structures and, in particular, we focus our discussion on the correctness and efficiency of extending Yap's tabling framework to support lock-free expandable tries. Experimental results show that our new lock-free design can effectively reduce the execution time and scale better, when increasing the number of threads, than the original lock-based design. © 2014 Springer International Publishing.

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.