Abstract
This paper evaluates the performance of three popular technologies used to interconnect machines on clusters: Fast Ethernet, Myrinet and Giganet. To achieve this purpose, we used the NAS Parallel Benchmarks. Surprisingly, for the LU application, the performance of Fast Ethernet was better than Myrinet. We also evaluate the performance gains provided by VIA, a user lever communication protocol, when compared with TCP/IP, a traditional, stacked-based communication protocol. The impacts caused by the use of Remote DMA Write are also evaluated. The results show that Fast Ethernet, when combined with a high performance communication protocol, such as VIA, has a good cost-benefit ratio, and can be a good choice to connect machines on a small cluster environment where bandwidth is not crucial for applications.

Abstract

Abstract
In this paper we use execution-driven simulation of a scalable multiprocessor to evaluate the performance of the Andorra-I parallel logic programming system under invalidate and update-based protocols. We use two versions of Andorra-I. One of them was originally designed for bus-based multiprocessors, while the other is optimised for scalable architectures. We study a well-known invalidate protocol and two different update-based protocols. Our results show that for our sample logic programs the update-based protocols outperform their invalidate-based counterpart for the original version of Andorra-I. In contrast, the optimised version of Andorra-I benefits the most from the invalidate-based protocol, but a hybrid update-based protocol performs as well as the invalidate protocol in most cases. We conclude that parallel logic programming systems can consistently benefit from hybrid update-based protocols. © Springer-Verlag Berlin Heidelberg 2000.

Abstract
There has been significant recent progress in the integration of probabilistic reasoning with first order logic representations (SRL). So far, the learning algorithms developed for these models all learn from scratch, assuming an invariant background knowledge. As an alternative, theory revision techniques have been shown to perform well on a variety of machine learning problems. These techniques start from an approximate initial theory and apply modifications in places that performed badly in classification. In this work we describe the first revision system for SRL classification, PFORTE, which addresses two problems: all examples must be classified, and they must be classified well. PFORTE uses a two step-approach. The completeness component uses generalization operators to address failed proofs and the classification component addresses classification problems using generalization and specialization operators. Experimental results show significant benefits from using theory revision techniques compared to learning from scratch. © Springer-Verlag Berlin Heidelberg 2006.

Abstract
We propose a new approach to Inductive Logic Programming i that systematically exploits caching and offers a number of advantages over current systems. It avoids redundant computation, is more amenable to the use of set-oriented generation and evaluation of hypotheses, and allows relational DBMS technology to be more easily applied to ILP systems. Further, our approach opens up new avenues such as probabilistically scoring rules during search and the generation of probabilistic rules. As a first example of the benefits of our ILP framework, we propose a scheme for denning the hypothesis search space through Inverse Entailment using multiple example seeds. © Springer-Verlag Berlin Heidelberg 2005.

Abstract
Recently, there has been significant work in the integration of probabilistic reasoning with first order logic representations. Learning algorithms for these models have been developed and they all considered modifications in the entire structure. In a previous work we argued that when the theory is approximately correct the use of techniques from theory revision to just modify the structure in places that failed in classification can be a more adequate choice. To score these modifications and choose the best one the log likelihood was used. However, this function was shown not to be well-suited in the propositional Bayesian classification task and instead the conditional log likelihood should be used. In the present paper, we extend this revision system showing the necessity of using specialization operators even when there are no negative examples. Moreover, the results of a theory modified only in places that are responsible for the misclassification of some examples are compared with the one that was modified in the entire structure using three databases and considering four probabilistic score functions, including conditional log likelihood. © Springer-Verlag Berlin Heidelberg 2005.