Nordine Melab

ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics

In this paper, we present the ParadisEO white-box object-oriented framework dedicated to the reusable design of parallel and distributed metaheuristics (PDM). ParadisEO provides a broad range of features including evolutionary algorithms (EA), local searches (LS), the most common parallel and distributed models and hybridization mechanisms, etc. This high content and utility encourages its use at European level. ParadisEO is based on a clear conceptual separation of the solution methods from the problems they are intended to solve. This separation confers to the user a maximum code and design reuse. Furthermore, the fine-grained nature of the classes provided by the framework allow a higher flexibility compared to other frameworks. ParadisEO is of the rare frameworks that provide the most common parallel and distributed models. Their implementation is portable on distributed-memory machines as well as on shared-memory multiprocessors, as it uses standard libraries such as MPI, PVM and PThreads. The models can be exploited in a transparent way, one has just to instantiate their associated provided classes. Their experimentation on the radio network design real-world application demonstrate their efficiency.

Building with paradisEO reusable parallel and distributed evolutionary algorithms

Numerous parallel and distributed evolutionary algorithms (PDEAs) and their implementations have been proposed and are available on the Web. A robust approach to make easier their code and design reuse is the framework approach. In this paper, we present some existing frameworks for PDEAs and their development requirements, and propose a new C++ open source framework, named Parallel and distributed Evolving Objects (ParadisEO). ParadisEO is basically devoted to the reusable and flexible design of parallel and distributed metaheuristics, but we focus here only on PDEAs. Compared to other related frameworks, ParadisEO allows more reuse flexibility, and provides more implemented parallel and distributed models. Furthermore, these models can be exploited by the user in a transparent way, and deployed as well on shared memory multi-processors as on distributed memory machines. The architecture has been experimented on two real-world applications: the radio network design and the spectroscopic data mining. The experimental results demonstrate the efficiency and robustness of the different models.

A parallel adaptive version of the block-based Gauss-Jordan algorithm

This paper presents a parallel adaptive version of the block-based Gauss-Jordan algorithm used in numerical analysis to invert matrices. This version includes a characterization of the workload of processors and a mechanism of its adaptive folding/unfolding. The application is implemented and experimented with MARS in dedicated and non-dedicated environments. The results show that an absolute efficiency of 92% is possible on a cluster of DEC/ALPHA processors interconnected by a Gigaswitch network and an absolute efficiency of 67% can be obtained on an Ethernet network of SUN-Sparc4 workstations. Moreover the adaptability of the algorithm is experimented on a non-dedicated meta-system including both the two parks of machines.

ParadisEO: a framework for parallel and distributed biologically inspired heuristics

In this paper we present ParadisEO, an open source framework for flexible parallel and distributed design of hybrid metaheuristics. Flexibility means that the parameters such as data representation and variation operators can be evolved. It is inherited from the EO object-oriented library for evolutionary computation. ParadisEO provides different parallel and/or distributed models and allows a transparent multi-threaded implementation. Moreover, it supplies different natural hybridization mechanisms mainly for metaheuristics including evolutionary algorithms and local search methods. The framework is experimented here in the spectroscopic data mining field. The flexibility property allowed an easy and straightforward development of a geneticalgorithm-based attribute selection for models discovery in NIR spectroscopic data. Experiments on a cluster of SMPs (IBM SP3) show that a good speed-up is achieved by using the provided parallel distributed models and multi-threading. Furthermore, the hybridization of the GA with the efficient PLS method allows to discover high-quality models. Indeed, their accuracy and understandability are improved respectively by 37% and 88%.

Parallel GA-based wrapper feature selection for spectroscopic data mining

Mining predictive models in dense databases is CPU time consuming and I/O intensive. In this paper, we propose a taxonomy of existing techniques allowing to achieve high performance. We propose a hybrid approach allowing to exploit four of them: feature selection, GA-based exploration space reduction, parallelism and concurrency. The approach is experimented on a near-infrared (NIR) spectroscopic application. It consists of predicting the concentration of a given component in a given product from its absorbances to NIR radiations. Statistical methods, like PLS, are well-suited and efficient for such data mining task. The experimental results show that preceding those methods with a feature selection allows to withdraw a significant number of irrelevant features and at the same time to enhance significantly the accuracy of the discovered predictive model. It is also shown that for the considered task the GA-based approach allows to build more accurate models than neural networks. Moreover, the parallel multithreaded implementation of the approach allows a linear speed-up.

A parallel genetic algorithm for rule mining

Rule mining consists of discovering valid and useful rules in large databases. As other data mining tasks, it is known to be time-consuming and I/O intensive. Evolutionary algorithms and parallelism are two important ways to deal with that performance problem. In this paper, we propose a parallel genetic algorithm for rule discovery, namely . We evaluated it on the Nursery School public domain data set available from the UCI Repository of Machine Learning databases. The results show that is efficient and allows to discover high quality rules.

ParaDisEO-Based Design of Parallel and Distributed Evolutionary Algorithms

ParaDisEO is a framework dedicated to the design of parallel and distributed metaheuristics including local search methods and evolutionary algorithms. This paper focuses on the latter aspect. We present the three parallel and distributed models implemented in ParaDisEO and show how these can be exploited in a user-friendly, flexible and transparent way. These models can be deployed on distributed memory machines as well as on shared memory multi-processors, taking advantage of the shared memory in the latter case. In addition, we illustrate the instantiation of the models through two applications demonstrating the efficiency and robustness of the framework.

Parallel adaptive computing on meta-systems including NOWs

Abstract Load analysis of meta-systems including NOWs or COWs has shown that only a few percentage of the available power is used during long periods of time. Therefore, in order to exploit the idle time when executing a parallel application work load must be sent to a machine as soon as the latter becomes available. Furthermore, in order to keep respected the ownership of workstations work has to be stopped and resumed later as soon as the machine executing it is requisitioned by its owner. As a consequence, users need an adaptive system allowing to return events related to the goings and comings of workstations. On the other hand, it is necessary to provide them a parallel adaptive programming methodology that plans the handling of these events. In this paper, we present the MARS (MARS: multi-user adaptive resource scheduler, developed at LIFL laboratory, Universite de Lille I) system and its parallel adaptive programming methodology through the block-based Gauss–Jordan algorithm used in numerical analysis to invert large matrices. Moreover, we propose a work scheduling strategy and an application-oriented solution for the fault tolerance issue. Furthermore, we present some experimental results obtained on a DEC/ALPHA COW and a SUN/Sparc4 NOW. The results show that very high absolute efficiencies can be obtained if the size of the blocks is well chosen. We also present some experimentations related to the adaptability of the application in a meta-system including the DEC/ALPHA COW and the SUN/Sparc4 NOW. The results show that the management of the adaptability consumes just a short percentage of execution time.