Abstract

Abstract
In this paper we present an implementation of the general system for type inference algorithms HM(X), using Prolog and Constraint Handling Rules. In our implementation the difference between the general aspects of the type inference algorithms and the constraint resolution module becomes clearer, when compared to other implementations of the same systems, usually made in a functional programming language. In the constraint module, solving equality constraints, here implemented by Prolog unification, is completely separated from constraint simplification, which is made by a solver implemented in CHR for each system. CHR rules become a clear and natural way of specifying the simplification mechanism. © 2002 Publishd by Elsevier Science B.V.

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 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
This paper presents a multithreaded abstract machine for the TyCO process calculus. We argue that process calculi provide a powerful framework to reason about fine-grained parallel computations. They allow for the construction of formally verifiable systems on which to base high-level programming idioms, combined with efficient compilation schemes into multithreaded architectures.

Abstract
In the fields of data mining and machine learning the amount of data available for building classifiers is growing very fast. Therefore, there is a great need for algorithms that are capable of building classifiers from very-large datasets and, simultaneously, being computationally efficient and scalable. One possible solution is to employ parallelism to reduce the amount of time spent in building classifiers from very-large datasets and keeping the classification accuracy. This work first overviews some strategies for implementing decision tree construction algorithms in parallel based on techniques such as task parallelism, data parallelism and hybrid parallelism. We then describe a new parallel implementation of the C4.5 decision tree construction algorithm. Even though the implementation of the algorithm is still in final development phase, we present some experimental results that can be used to predict the expected behavior of the algorithm. © Springer-Verlag Berlin Heidelberg 2001.