Abstract
Logic programming provides a high-level view of programming, giving implementers a vast latitude into what techniques to explore to achieve the best performance for logic programs. Towards obtaining maximum performance, one of the holy grails of logic programming has been to design computational models that could be executed efficiently and that would allow both for a reduction of the search space and for exploiting all the available parallelism in the application. These goals have motivated the design of the Extended Andorra Model (EAM), a model where goals that do not constrain nondeterministic goals can execute first. In this work, we present and evaluate the Basic design for EAM, a system that builds upon David H. D. Warren's original EAM with Implicit Control. We provide a complete description and implementation of the Basic design for EAM System as a set of rewrite and control rules. We present the major data structures and execution algorithms that are required for efficient execution, and evaluate system performance. A detailed performance study of our system is included. Our results show that the system achieves acceptable base performance and that a number of applications benefit from the advanced search inherent to the EAM.

Abstract
Logic programming is based on the idea that computation is controlled inference. The Extended Andorra Model provides a very powerful framework that supports both co-routining and parallelism. We present the BEAM, a design that builds upon David H. D.Warren’s original EAM with Implicit Control. The BEAM supports Warren’s original EAM rewrite rules plus eager splitting and sequential conjunctions. We discuss the main issues in the implementation of the BEAM and show that the EAM with Implicit Control can perform quite well when compared with other implementations that use the Andorra principle. © Springer-Verlag Berlin Heidelberg 2001

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. The reason is that Prolog programs are highly recursive and generate multiple answers. This rather involved execution mechanism requires a more complex implementation than traditional Prolog. The declarative nature of tabled logic programming suggests that it might be amenable to parallel execution. On the other hand, the complexity of the tabling mechanism, and the existence of a shared resource, the table, argues that parallelism might be limited, and that performance for real applications might never scale. In this work we prove that parallel tabling is indeed scalable for real applications by experimenting the OPTYap parallel tabled system on a scalable shared-memory machine. © 2002 IEEE.

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.