Technical support for Life Sciences communities on a production grid infrastructure

Production operation of large distributed computing infrastructures (DCI) still requires a lot of human intervention to reach acceptable quality of service. This may be achievable for scientific communities with solid IT support, but it remains a show-stopper for others. Some application execution environments are used to hide runtime technical issues from end users. But they mostly aim at fault-tolerance rather than incident resolution, and their operation still requires substantial manpower. A longer-term support activity is thus needed to ensure sustained quality of service for Virtual Organisations (VO). This paper describes how the biomed VO has addressed this challenge by setting up a technical support team. Its organisation, tooling, daily tasks, and procedures are described. Results are shown in terms of resource usage by end users, amount of reported incidents, and developed software tools. Based on our experience, we suggest ways to measure the impact of the technical support, perspectives to decrease its human cost and make it more community-specific.Comment: HealthGrid'12, Amsterdam : Netherlands (2012

Порівняння ефективності групового навчання багатошарового персептрону на паралельному комп’ютері та обчислювальному кластері

Паралельний метод групового навчання багатошарового персептрону (БШП) на основі алгоритму зворотного поширення помилки та дослідження ефективності розпаралелення цього методу на паралельному комп’ютері та обчислювальних кластерах представлено в цій статті. Модель БШП та послідовний метод групового навчання описані теоретично. Представлено алгоритмічний опис паралельного методу групового навчання. Ефективність розпаралелення методу досліджена на поступово збільшуваних розмірностях сценаріїв навчання. Результати експериментальних досліджень показали, що (I) обчислювальний кластер з комунікаційним інтерфейсом Infiniband показав кращу ефективність розпаралелення, ніж паралельний комп’ютер загального призначення з ccNuma архітектурою через менші комунікаційні втрати та (II) ефективність розпаралелення представленого методу є достатньо високою для його успішного застосування на паралельних комп’ютерах та кластерах загального призначення, наявних в сучасних обчислювальних ГРІД-системах.The development of a parallel method for batch pattern training of a multilayer perceptron with the back propagation training algorithm and the research of its efficiency on general-purpose parallel computer and computational clusters are presented in this paper. The model of a multilayer perceptron and the usual sequential batch pattern training method are theoretically described. An algorithmic description of the parallel version of the batch pattern training method is presented. The efficiency of parallelization of the developed method is investigated on the progressive increasing the dimension of the parallelized problem. The results of the experimental researches show that (I) the computational cluster with Infiniband interconnection shows better values of parallelization efficiency in comparison with general-purpose parallel computer with ccNuma architecture due to lower communication overhead and (II) the parallelization efficiency of the method is high enough for its appropriate usage on general-purpose parallel computers and clusters available within modern computational grids

NetRevealer : uma ferramenta gráfica para a análise do tráfego de redes

O problema da falta de uma ferramenta gráfica de análise de redes em tempo real levou ao trabalho de desenvolvimento de uma aplicação que colmatasse esta lacuna. Desta forma, desenvolveu-se o NetRevealer, uma ferramenta multi-plataforma que tem como objetivo analisar o tráfego numa rede de computadores. Para isso, a aplicação acede às interfaces de rede existentes num computador e representa através de um ícone cada equipamento que envia ou recebe pacotes nessa rede. Obtém-se assim um mapa que exibe o tráfego em tempo real e permite detectar atividades nessa rede, nomeadamente atividades indesejadas como portas clandestinas e utilizadores não autorizados a utilizar a rede.The need of a graphical tool for network analysis in real time is the reason of the development of an application to fulfil this gap. Thus, we developed the NetRevealer, a cross-platform tool that aims to analyze traffic on a computer network. The application accesses the existing network interfaces on a computer and uses an icon to represent each device that sends or receives packets on this network. The application tool generates a map that display in real-time the data traffic and allows to detect the activity in this network, including undesirable activities such backdoors and users allowed to use the network

Quantized State Simulation of Spiking Neural Networks

In this work, we explore the usage of quantized state system (QSS) methods in the simulation of networks of spiking neurons. We compare the simulation results obtained by these discrete-event algorithms with the results of the discrete time methods in use by the neuroscience community. We found that the computational costs of the QSS methods grow almost linearly with the size of the network, while they grows at least quadratically in the discrete time algorithms. We show that this advantage is mainly due to the fact that QSS methods only perform calculations in the components of the system that experience activity. © 2012, Simulation Councils Inc. All rights reserved.Fil: Grinblat, Guillermo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Ahumada, Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Kofman, Ernesto Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentin

Design of a GF(64)-LDPC Decoder Based on the EMS Algorithm

International audienceThis paper presents the architecture, performance and implementation results of a serial GF(64)-LDPC decoder based on a reduced-complexity version of the Extended Min-Sum algorithm. The main contributions of this work correspond to the variable node processing, the codeword decision and the elementary check node processing. Post-synthesis area results show that the decoder area is less than 20% of a Virtex 4 FPGA for a decoding throughput of 2.95 Mbps. The implemented decoder presents performance at less than 0.7 dB from the Belief Propagation algorithm for different code lengths and rates. Moreover, the proposed architecture can be easily adapted to decode very high Galois Field orders, such as GF(4096) or higher, by slightly modifying a marginal part of the design

GRID AND CLOUD COMPUTING FOR E-SCIENCE APPLICATIONS

eScience fields which include areas such as spatial data, electromagnetic,bioinformatics, energy, social sciences, simulation, physical science have on the course of recent years a significant development regarding the complexity of algorithms and applications for data analysis. Information data has also evolved with an explosion in term of data volume and datasets for the scientific community. This has led researchers to identify new necessity regarding tools analysis, applications, by a profound change in computing infrastructures utilization. The field of eScience is constantly evolving through the creation of ever more growing scientific community who have a real needs in availability in computational resources ever more powerful calculations. Another important issue is the ability to be able to share results, this is why cloud technology through virtualization can be an important help for the scientist community for giving a flexible and scalable IT infrastructure depending on necessities. Indeed, cloud computing allows for the provision of computing resources, storage in an easy configurable way and adaptable in functions of real needs. Researchers often do not have all the computing capacities to meet their needs, so cloud technology and cloud models as Private, Public and Hybrid is an enable technology for having a guarantee of service availability, scalability and flexibility. The transition from traditional infrastructure to new virtualized with distributed models allows researchers to have access to an environment extremely flexible allowing an optimization of the use of hardware for having more available resources. However, the computational needs on e-Science have a direct effect regarding the way that applications are developed. The approach of writing algorithm and applications is still too tied to a model centered on a workstation for example. The vast majority of researchers conducts the writing process of their applications on their laptop or workstation in a limited context of computing power, storage and in a non-distributed wa

GRID AND CLOUD COMPUTING FOR E-SCIENCE APPLICATIONS

eScience fields which include areas such as spatial data, electromagnetic,bioinformatics, energy, social sciences, simulation, physical science have on the course of recent years a significant development regarding the complexity of algorithms and applications for data analysis. Information data has also evolved with an explosion in term of data volume and datasets for the scientific community. This has led researchers to identify new necessity regarding tools analysis, applications, by a profound change in computing infrastructures utilization. The field of eScience is constantly evolving through the creation of ever more growing scientific community who have a real needs in availability in computational resources ever more powerful calculations. Another important issue is the ability to be able to share results, this is why cloud technology through virtualization can be an important help for the scientist community for giving a flexible and scalable IT infrastructure depending on necessities. Indeed, cloud computing allows for the provision of computing resources, storage in an easy configurable way and adaptable in functions of real needs. Researchers often do not have all the computing capacities to meet their needs, so cloud technology and cloud models as Private, Public and Hybrid is an enable technology for having a guarantee of service availability, scalability and flexibility. The transition from traditional infrastructure to new virtualized with distributed models allows researchers to have access to an environment extremely flexible allowing an optimization of the use of hardware for having more available resources. However, the computational needs on e-Science have a direct effect regarding the way that applications are developed. The approach of writing algorithm and applications is still too tied to a model centered on a workstation for example. The vast majority of researchers conducts the writing process of their applications on their laptop or workstation in a limited context of computing power, storage and in a non-distributed way