Abstract

Abstract
This work investigates the impact of hybrid coherence protocols on non-scientific applications. We use execution-driven simulation of a scalable multiprocessor and compare the results obtained with a hybrid protocol with a well-known invalidate protocol and an update-based protocol. Our results show that for our sample programs a hybrid protocol with high threshold outperforms both the invalidate-based and update-based protocols, regardless of the type of parallelism exhibited by the benchmarks. We conclude that our applications can benefit from a hybrid protocol, and that multiprocessors designed for running these systems efficiently should adopt some form of hybrid protocol.

Abstract
Breast screening is the regular examination of a woman's breasts to find breast cancer in an initial stage. The sole exam approved for this purpose is mammography that, despite the existence of more advanced technologies, is considered the cheapest and most efficient method to detect cancer in a preclinical stage. We investigate, using machine learning techniques, how attributes obtained from mammographies can relate to malignancy. In particular, this study focus is on how mass density can influence malignancy from a data set of 348 patients containing, among other information, results of biopsies. To this end, we applied different learning algorithms on the data set using the WEKA tools, and performed significance tests on the results. The conclusions are threefold: (1) automatic classification of a mammography can reach equal or better results than the ones annotated by specialists, which can help doctors to quickly concentrate on some specific mammogram for a more thorough study; (2) mass density seems to be a good indicator of malignancy, as previous studies suggested; (3) we can obtain classifiers that can predict mass density with a quality as good as the specialist blind to biopsy.

Abstract
From an application point of view, the Grid computing with its powerful processing power and large amounts of data storage offers the possibility to process large quantities of data, to run computationally-intensive operations, or both. For instance, in computational biological pipelines, one often has to process large quantities of data in individually computationally-intensive operations. To process this data in the Grid, hundreds, or even thousands of jobs need to be submitted and their results processed. Obviously, performing these tasks manually is unfeasible. On the other hand, developing software to this end, specifically for a single application, is unproductive because if the application changes, or the Grid submission engine changes, then the code needs to be rewritten. In this paper we present a middleware that facilitates the submission of jobs to grids (or clusters) and helps handling their results. The middleware, that we call UbiDis (Ubiquitous Distribution), copies all files necessary for running the program to the UI or front-end host (in a Grid or cluster), compiles programs on the UI or front-end (if necessary), generates and submits the jobs, and copies the outputs to the local machine. Furthermore, UbiDis transparently generates jobs to different job managers, allowing the user to easily and quickly change the location to where the jobs are submitted. Finally, we illustrate the usefulness of UbiDis using two applications.

Abstract
Several international Projects and Collaborations have emerged in the last years due to the increasing demand for Grid resources. One important aspect of these initiatives deals with the gridification of computing intensive scientific applications otherwise difficult to run efficiently. The EELA Project (E-Infrastructure shared between Europe and Latin America) is a collaboration of Latin America and Europe Institutions which has developed a performance e-Infrastructure for e-Science applications in the fields of Biomedicine, High Energy Physics, e-Learning and Climate. Nowadays many groups have already ported their applications on the EELA Grid and are obtaining first results. This paper describes the first year of EELA and the progress achieved so far.

Abstract
One of the most important advantages of logic programming systems is that they allow the transparent exploitation of parallelism. The different forms of parallelism available and the complex nature of logic programming applications present interesting problems to both the users and the developers of these systems. Graphical visualisation tools can give a particularly important contribution, as they are easier to understand than text based tools, and allow both for a general overview of an execution and for focusing on its important details. Towards these goals, we propose VisAll, anew tool to visualise the parallel execution of logic programs. VisAll benefits from a modular design centered in a graph that represents a parallel execution. A main graphical shell commands the different modules and presents VisAll as an unified system. Several input components, or translators, support the well-known VisAndor and VACE trace formats, plus a new format designed for independent and-parallel plus or-parallel execution in the SEA. Several output components, or visualisers, allow for different visualisations of the same execution.