Abstract
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, and several are by nature non-deterministic. This raises the question of whether parallel search techniques can be used to 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 tabled computations, and that resulting systems can achieve good performance on multi-processors. To do so, we present the OPT Yap 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. © Springer-Verlag Berlin Heidelberg 2001.

Abstract
YapOr is an or-parallel system that extends the Yap Prolog system to exploit implicit or-parallelism in Prolog programs. It is based on the environment copying model, as first implemented in Muse. The development of YapOr required solutions for some important issues, such as designing the data structures to support parallel processing, implementing incremental copying technique, developing a memory organization able to answer with efficiency to parallel processing and to incremental copying in particular, implementing the scheduler strategies, designing an interface between the scheduler and the engine, implementing the sharing work process, and implementing support to the cut builtin. An initial evaluation of YapOr performance showed that it achieves very good performance on a large set of benchmark programs. Indeed, YapOr compares favorably with a mature parallel Prolog system such as Muse, both in terms of base speed and in terms of speedups. © Springer-Verlag Berlin Heidelberg 1999.

Abstract
Inductive logic programming (ILP) is a sub-field of machine learning that provides an excellent framework for multi-relational data mining applications. The advantages of ILP have been successfully demonstrated in complex and relevant industrial and scientific problems. However, to produce valuable models, ILP systems often require long running times and large amounts of memory. In this paper we address fundamental issues that have direct impact on the efficiency of ILP systems. Namely, we discuss how improvements in the indexing mechanisms of an underlying logic programming system benefit ILP performance. Furthermore, we propose novel data structures to reduce memory requirements and we suggest a new lazy evaluation technique to search the hypothesis space more efficiently. These proposals have been implemented in the April ILP system and evaluated using several well-known data sets. The results observed show significant improvements in running time without compromising the accuracy of the models generated. Indeed, the combined techniques achieve several order of magnitudes speedup in some data sets. Moreover, memory requirements are reduced in nearly half of the data sets. Copyright (C) 2008 John Wiley & Sons, Ltd.

Abstract
In this work we perform a detailed study of different or-scheduling strategies varying several parameters in two or-parallel systems, YapOr and ThOr, running on multi-core machines. Our results show that some kinds of applications are sensitive to the choice of scheduling strategy adopted. In particular, the choice of scheduling parameters mostly affect applications that have short execution times, which, despite having speedups, have their performance significantly affected. Our results also show that topmost dispatching can be more advantageous than bottommost dispatching, a finding that contradicts previous works in this area. One last finding is that YapOr and ThOr are affected differently by changes in scheduling with ThOr performing significantly better than YapOr in several applications.

Abstract
Prolog programs have explicit parallelism, that is, parallelism which can be exploited by a machine with minimal user effort. Or-parallelism is one such form of parallelism, and is particularly useful in that it is present in the many Prolog applications where several alternatives need to be considered. Or-parallelism has been exploited successfully in several systems, and especially in the Aurora and Muse systems. In this paper we analyze the portability of these two parallel systems onto a commercial shared memory parallel computer, a Sun SPARCcenter 2000 with 8 processors, running the Solaris 2.2 Operating System. We also analyze both systems' performance for classical benchmark programs and for two large Prolog applications.

Abstract
One important advantage of logic programming is that it allows the implicit exploitation of parallelism. Towards this goal, we suggest that or-parallelism can be efficiently exploited in tabling systems and propose two alternative approaches, Or-Parallelism within Tabling (OPT) and Tabling within Or-Parallelism (TOP). We concentrate on the fundamental concepts of an environment copying based model to implement the OPT approach and introduce the data structures and algorithms necessary to extend the YapOr Or-Parallel system, in order to obtain a parallel tabling system.