86 research outputs found
3rd EGEE User Forum
We have organized this book in a sequence of chapters, each chapter associated with an application or technical theme introduced by an overview of the contents, and a summary of the main conclusions coming from the Forum for the chapter topic. The first chapter gathers all the plenary session keynote addresses, and following this there is a sequence of chapters covering the application flavoured sessions. These are followed by chapters with the flavour of Computer Science and Grid Technology. The final chapter covers the important number of practical demonstrations and posters exhibited at the Forum. Much of the work presented has a direct link to specific areas of Science, and so we have created a Science Index, presented below. In addition, at the end of this book, we provide a complete list of the institutes and countries involved in the User Forum
High Energy Physics Forum for Computational Excellence: Working Group Reports (I. Applications Software II. Software Libraries and Tools III. Systems)
Computing plays an essential role in all aspects of high energy physics. As
computational technology evolves rapidly in new directions, and data throughput
and volume continue to follow a steep trend-line, it is important for the HEP
community to develop an effective response to a series of expected challenges.
In order to help shape the desired response, the HEP Forum for Computational
Excellence (HEP-FCE) initiated a roadmap planning activity with two key
overlapping drivers -- 1) software effectiveness, and 2) infrastructure and
expertise advancement. The HEP-FCE formed three working groups, 1) Applications
Software, 2) Software Libraries and Tools, and 3) Systems (including systems
software), to provide an overview of the current status of HEP computing and to
present findings and opportunities for the desired HEP computational roadmap.
The final versions of the reports are combined in this document, and are
presented along with introductory material.Comment: 72 page
Platform as a service integration for scientific computing using DIRAC
Cada día crece máis a demanda de recursos de computación requirida polos investigadores,
capacidades de cálculo que coexisten co crecente volume de datos xerado actualmente. Estes
investigadores están a demandar un servizo de Computación de Altas Prestacións (HPC) que
permita a execución das suas simulacións dunha forma na que se deslocalicen os recursos para
poder acceder aos máximos posibles, facilitandoo coa forma o máis cómoda e segura para eles.
Doutra banda, as universidades están conectadas con centros de investigación con redes que
pusuen unha velocidade e fiabilidade que posibilitan a execución de traballos de cálculo
científico. As capacidades de computo existentes en universidades van dende aulas informáticas
para usos docentes, laboratorios, etc., ata clusters de ordenadores pertencentes a grupos de
investigación. Usando tecnoloxías grid e cloud estes recursos computacionais heteroxéneos
poderían ser reutilizados polos investigadores para realizar simulacións, aportando unha maior
cantidade de cómputo a xa existente e deslocalizando os recursos entre distintos lugares ao
redor do planeta. O obxectivo desta tese é adaptar a contorna para computación distribuída
DIRAC, desenvolvida para o proxecto LHCb do CERN, para o seu uso por varias comunidades de
usuarios baseado nas tecnoloxías cloud e big data. Esta contorna pusuiría repositorios de
software centralizados que permitan proveer o software necesario para que a través dos
entornos na nube se poidan executar as aplicacións dos investigadores en calquera parte do
planeta dunha forma escalable, permitindo aprobeitar tanto recursos dedicados como nondedicados.
Avaliando así a execución desta plataforma para a realización de cálculos científicos.
Este traballo comezará coa obtención de requisitos, para pasar despois ao proceso de
integración básica. Posteriormente, optimizarase o uso do software cientifico empregado para as
contornas cloud, tratando de adaptalo aos entornos virtualizados. Para iso, será necesario
realizar un estudo estadístico que sexa o máis próximo posible aos entornos en producción para
poder determinar e crear as infraestructuras adaptadas evitando así a perda de rendemento
dentro de recursos. O seguinte caso sería utilizar as tecnoloxías virtualizadas, adaptando as
arquitecturas creadas, para a creación de sistemas que permitan o envío de traballos que
requiran de grandes cantidades de datos no eido do big data dunha forma distribuida
Machine learning at the energy and intensity frontiers of particle physics
Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics
Enabling parallel and interactive distributed computing data analysis for the ALICE experiment
AliEn (ALICE Environment) is the production environment developed by
the ALICE collaboration at CERN. It provides a set of Grid tools enabling
the full offline computational work-flow of the experiment (simulation, reconstruction
and data analysis) in a distributed and heterogeneous computing
environment.
In addition to the analysis on the Grid, ALICE users perform local interactive
analysis using ROOT and the Parallel ROOT Facility (PROOF).
PROOF enables physicists to analyse in parallel medium-sized (200-300
TB) data sets in a short time scale.
The default installation of PROOF is on a static dedicated cluster, typically
200-300 cores. This well-proven approach is not devoid of limitations,
more specifically for analysis of larger datasets or when the installation of
a dedicated cluster is not possible. Using a new framework called Proof on
Demand (PoD), PROOF can be used directly on Grid-enabled clusters, by
dynamically assigning interactive nodes on user request.
This thesis presents the PoD on AliEn project. The integration of Proof on
Demand in the AliEn framework provides private dynamic PROOF clusters
as a Grid service. This functionality is transparent to the user who will
submit interactive jobs to the AliEn system.
The ROOT framework, among other things, is used by physicists to carry
out the Monte Carlo Simulation of the detector. The engineers working on
the mechanical design of the detector need to collaborate with the physicists.
However, the softwares used by the engineers are not compatible with
ROOT.
This thesis describes a second result obtained during this PhD project: the
implementation of the TGeoCad Interface that allows the conversion of
ROOT geometries to STEP format, compatible with CAD systems. The
interface provides an important communication and collaboration tool between
physicists and engineers, dealing with the simulation and the design
of the detector geometry
A Roadmap for HEP Software and Computing R&D for the 2020s
Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.Peer reviewe
Optimisation of LHCb Applications for Multi- and Manycore Job Submission
Nowadays, the Worldwide LHC Computing Grid mainly consists of multi- and manycore processors. The thesis investigates how such resources can be used more efficiently at the example of the LHCb experiment. It analyses how to improve software in terms of memory requirements and concurrency. The research involves the implementation of a moldable job scheduler and a supervised learning algorithm which helps to better predict LHCb workloads
Monitoring and Optimization of ATLAS Tier 2 Center GoeGrid
The demand on computational and storage resources is growing along with the amount of infor-
mation that needs to be processed and preserved. In order to ease the provisioning of the digital
services to the growing number of consumers, more and more distributed computing systems and
platforms are actively developed and employed. The building block of the distributed computing
infrastructure are single computing centers, similar to the Worldwide LHC Computing Grid, Tier
2 centre GoeGrid. The main motivation of this thesis was the optimization of GoeGrid perfor-
mance by efficient monitoring. The goal has been achieved by means of the GoeGrid monitoring
information analysis. The data analysis approach was based on the adaptive-network-based
fuzzy inference system (ANFIS) and machine learning algorithm such as Linear Support Vector
Machine (SVM).
The main object of the research was the digital service, since availability, reliability and ser-
viceability of the computing platform can be measured according to the constant and stable
provisioning of the services. Due to the widely used concept of the service oriented architecture
(SOA) for large computing facilities, in advance knowing of the service state as well as the quick
and accurate detection of its disability allows to perform the proactive management of the com-
puting facility. The proactive management is considered as a core component of the computing
facility management automation concept, such as Autonomic Computing. Thus in time as well
as in advance and accurate identification of the provided service status can be considered as a
contribution to the computing facility management automation, which is directly related to the
provisioning of the stable and reliable computing resources.
Based on the case studies, performed using the GoeGrid monitoring data, consideration of the
approaches as generalized methods for the accurate and fast identification and prediction of the
service status is reasonable. Simplicity and low consumption of the computing resources allow
to consider the methods in the scope of the Autonomic Computing component
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