Catálogo de publicaciones - libros

In this paper a particle gradient evolutionary algorithm is presented for solving complex single-objective optimization problems based on statistical mechanics theory, the principle of gradient descending, and the law of evolving chance ascending of particles. Numerical experiments show that we can easily solve complex single-objective optimization problems that are difficult to solve by using traditional evolutionary algorithms and avoid the premature phenomenon of these problems. In addition, a convergence analysis of the algorithm indicates that it can quickly converge to optimal solutions of the optimization problems. Hence this algorithm is more reliable and stable than traditional evolutionary algorithms.

Cardiac modeling is here to stay. Computer models are being used in a variety of ways and support the tests of drugs, the development of new medical devices and non-invasive diagnostic techniques. Computer models have become valuable tools for the study and comprehension of the complex phenomena of cardiac electrophysiology. However, the complexity and the multidisciplinary nature of cardiac models still restrict its use to a few specialized research centers in the world. We propose a computational framework that provides support for cardiac electrophysiology modeling. This framework integrates different computer tools and allows one to bypass many complex steps during the development and use of cardiac models. The implementation of cardiac cell models is automatically provided by a tool that translates models described in CellML language to executable code that allows one to manipulate and solve the models numerically. The automatically generated cell models are integrated in an efficient 2-dimensional parallel cardiac simulator. The set up and use of the simulator is supported by a user-friendly graphical interface that offers the tasks of simulation configuration, parallel execution in a pool of connected computer clusters, storage of results and basic visualization. All these tools are being integrated in a Web portal that is connected to a pool of clusters. The Web portal allows one to develop and simulate cardiac models efficiently via this user-friendly integrated environment. As a result, the complex techniques and the know-how behind cardiac modeling are all taken care of by the web distributed applications.

Identifying functional modules is believed to reveal most cellular processes. There have been many computational approaches to investigate the underlying biological structures[1,4,9,13]. A spectral clustering method plays a critical role identifying functional modules in a yeast protein-protein network in [9]. One of major obstacles clustering algorithms face and deal with is the limited information on how close two proteins with or without interactions are. We present an unweighted-graph version of a multilevel spectral algorithm which identifies more protein complexes with less computational time [8]. Existing multilevel approaches are hampered with no preliminary knowledge how many levels should be used to expect the best or near best results. While existing matching based multilevel algorithms try to merge pairs of nodes, we here present a new multilevel algorithms which merges groups of three nodes in triangular cliques. These new algorithms produce as good clustering results as previously best known matching based coarsening algorithms. Moreover, our algorithms use only one or two levels of coarsening, so we can avoid a major weakness of matching based algorithms.

Computing in Biosciences, Data Processing, Numerical Methods.

Over last few years, interest on biotechnology has increased dramatically. With the completion of sequencing of the human genome, such interest is likely to expand even more rapidly. The size of genetic information database doubles every 14 months, overwhelming explosion of information in related bioscience disciplines and consequently, overtaxing any existing computational tool for data analysis. There is a persistent and continuous search for new alternatives or new technologies, all with the common goal of improving overall computational performance. Grid infrastructures are characterized by interconnecting a number of heterogeneous hosts through the internet, by enabling large-scale aggregation and sharing of computational, data and other resources across institutional boundaries. In this research paper, we present BioPortal, a user friendly and web-based GUI that eases the deployment of well-known bioinformatics applications on large-scale cluster and grid computing environments. The major motivation of this research is to enable biologists and geneticists, as also biology students and investigators, to access to high performance computing without specific technical knowledge of the means in which are handled by these computing environments and no less important, without introducing any additional drawback, in order to accelerate their experimental and sequence data analysis. As result, we could demonstrate the viability of such design and implementation, involving solely freely available softwares.

Simulating and optimizing complex physical systems is known to be a task of considerable time and computational complexity. As a result, metamodeling techniques for the efficient exploration of the design space have become standard practice since they reduce the number of simulations needed. However, conventionally such metamodels are constructed sequentially in a one-shot manner, without exploiting inherent parallelism. To tackle this inefficient use of resources we present an adaptive framework where modeler and simulator interact through a distributed environment, thus decreasing model generation and simulation turnaround time. This paper provides evidence that such a distributed approach for adaptive sampling and modeling is worthwhile investigating. Research in this new field can lead to even more innovative automated modeling tools for complex simulation systems.

The registry of information about the activity of applications is an important issue in Grid environments. There are different projects which have developed tools for supporting the track of the resources. Nevertheless, most of them are mainly focused in measuring CPU usage, memory, disk, etc. because they are oriented to the classical use of the Grid to share computational power and storage capacity. This paper proposes a distributed architecture which provides logging facilities in service oriented Grid environments (DiLoS). This architecture is structured so that can fit to hierarchical, flat, etc. grid deployments. The information generated during the activity in the services are scattered among the different levels of a Grid deployment, providing features such as backup of the information, delegation of the storage, etc. In order to create the hierarchy of log services, the architecture is based on discovery facilities that can be implemented in different ways, which the user may configure according to the specific deployment. A case of use is described, where the DiLoS architecture has been applied to the gCitizen project.

The interoperability among different science grid systems is indispensable to worldwide use of a large-scale experimental facility as well as a large-scale supercomputer. One of the simplest ways to achieve the interoperability is to convert message among different science grid systems without modifying themselves. Under such consideration, the interoperability between UNICORE and ITBL (IT-Based Laboratory) has been achieved without modifying these grid systems by adopting a connection server which works as a mediator. Until international standardization is established, the method of message conversion among different science grid systems is promising as a way to establish the interoperability.

Computing grids are large-scale, highly-distributed, often hierarchical, platforms. At such scales, failures are no longer exceptions, but part of the normal behavior. When designing software for grids, developers have to take failures into account. It is crucial to make experiments at a large scale, with various volatility conditions, in order to measure the impact of failures on the whole system. This paper presents an experimental tool allowing the user to inject failures during a practical evaluation of fault-tolerant systems. We illustrate the usefulness of our tool through an evaluation of a hierarchical grid failure detector.

One of the great benefit of computational grids is to provide access to a wide range of scientific software and computers with different architectures. It is then possible to use a variety of tools for solving the same problem and even to combine these tools in order to obtain the best solution technique.

Grid service trading (searching for the best combination of software and execution platform according to the user requirements) is thus a crucial issue. Trading relies both on the description of available services and computers, on the current state of the grid, and on the user requirements. Given the large amount of services available on the Grid, this description cannot be reduced to a simple service name.

We present in this paper a more sophisticated service description similar to algebraic data type. We then illustrate how it can be used to determine the combinations of services that answer a user request. As a side effect, users do not make direct explicit calls to grid-services but talk to a more applicative-domain specific service trader.

We illustrate this approach and its possible limitations within the framework of dense linear algebra. More precisely we focus on Level 3 BLAS ([DDDH90a, DDDH90b]) and LAPACK [ABB+99] type of basic operations.

The goal of the GRID-TLSE Project is to design an expert site that provides an easy access to a number of tools allowing comparative analysis of sparse matrix packages on a user-submitted problem, as well as on particular matrices from the matrix collection also available on the site.

When making available a large amount of software over a computational Grid, facilitating its deployment and its exploitation become crucial. Within the GRID-TLSE Project, we use a software component approach based on a high level semantic description of the scientific computing services. In this paper, we focus on one aspect of this description of the computational services: the use of meta-data called . Our approach allows the automatic discovery and the exploitation of new services throught the concept of .