On the Implementation of GNU Prolog

GNU Prolog is a general-purpose implementation of the Prolog language, which distinguishes itself from most other systems by being, above all else, a native-code compiler which produces standalone executables which don't rely on any byte-code emulator or meta-interpreter. Other aspects which stand out include the explicit organization of the Prolog system as a multipass compiler, where intermediate representations are materialized, in Unix compiler tradition. GNU Prolog also includes an extensible and high-performance finite domain constraint solver, integrated with the Prolog language but implemented using independent lower-level mechanisms. This article discusses the main issues involved in designing and implementing GNU Prolog: requirements, system organization, performance and portability issues as well as its position with respect to other Prolog system implementations and the ISO standardization initiative.Comment: 30 pages, 3 figures, To appear in Theory and Practice of Logic Programming (TPLP); Keywords: Prolog, logic programming system, GNU, ISO, WAM, native code compilation, Finite Domain constraint

Multidimensional poverty in the EU: rethinking AROPE through a multi-criteria analysis

At risk of poverty or social exclusion rate (AROPE) constitutes the pivotal indicator of living conditions and poverty in the European Union. Nevertheless, as a multidimensional poverty measure, it has some drawbacks that significantly reduce its utility. In this paper, we propose an alternative multi-criteria approach that provides some innovations for the computation of multidimensional poverty in the European countries. We first propose a normalization formula for each dimension by using a double point of reference. We then put forward alternative aggregation functions that permit diverse degrees of substitutability across dimensions. This new formulation allows us to go beyond focusing merely on the rate of people classified as AROPE, making it possible to evaluate aspects such as the intensity of multidimensional poverty and how changes over time are distributed across population in terms of shared prosperity, as showed in an illustration for the EU28 countries.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Solving the Quadratic Assignment Problem with Cooperative Parallel Extremal Optimization

International audienceSeveral real-life applications can be stated in terms of the Quadratic Assignment Problem. Finding an optimal assignment is com-putationally very difficult, for many useful instances. We address this problem using a local search technique, based on Extremal Optimization and present experimental evidence that this approach is competitive. Moreover, cooperative parallel versions of our solver improve performance so much that large and hard instances can be solved quickly

Towards a Parallel Hierarchical Adaptive Solver Tool

International audienceConstraint satisfaction and combinatorial optimization problems , even when modeled with efficient metaheurisics such as local search remain computationally very intensive. Solvers stand to benefit significantly from execution on parallel systems, which are increasingly available. The architectural diversity and complexity of the latter means that these systems pose ever greater challenges in order to be effectively used, both from the point of view of the modeling effort and from that of the degree of coverage of the available computing resources. In this article we discuss impositions and design issues for a framework to make efficient use of various parallel architectures

Hybridization as Cooperative Parallelism for the Quadratic Assignment Problem

International audienceThe Quadratic Assignment Problem is at the core of several real-life applications. Finding an optimal assignment is computationally very difficult, for many useful instances. The best results are obtained with hybrid heuristics, which result in complex solvers. We propose an alternate solution where hybridization is obtain by means of parallelism and cooperation between simple single-heuristic solvers. We present experimental evidence that this approach is very efficient and can effectively solve a wide variety of hard problems, often surpassing state-of-the-art systems

Extending the Finite Domain Solver of GNU Prolog

International audienceThis paper describes three significant extensions for the Finite Domain solver of GNU Prolog. First, the solver now supports negative integers. Second, the solver detects and prevents integer overflows from occurring. Third, the internal representation of sparse domains has been redesigned to overcome its current limitations. The preliminary performance evaluation shows a limited slowdown factor with respect to the initial solver. This factor is widely counterbalanced by the new possibilities and the robustness of the solver. Furthermore these results are preliminary and we propose some directions to limit this overhead

The Potential for Biogas Production from Agriculture Wastes in Mexico

An important objective of the Mexican Energy National Strategy (ENS) is to produce around 35% of energy from clean technologies in 2024. This goal implies challenges from scientific and technologic perspectives. Besides solar and wind energies, different initiatives have been implemented to promote biofuels, mainly, biodiesel, bioethanol and biogas. Agriculture and livestock wastes are being used as biogas source to produce energy in small and medium scale. Also, some industries use biogas to provide a part of the energy required in their processes. But in general, the potential of biomethane production is not well seized yet. In the context of the ENS, biogas should be considered as an important topic due to the existence of several economical activities producing a lot of organic wastes. In this document, an analysis of the biogas from agricultural wastes is performed in order to identify the current status and opportunities for the next years

Parallel constraint-based local search on the HA8000 supercomputer (abstract)

We present a parallel implementation of a constraint-based local search algorithm and investigate its performance re- sults on hardware with several hundreds of processors