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30 results on '"Carballeira, A."'

4. Fall Risk and the Use of Exercise as a Fall Prevention Strategy

Author

Nogueira Rodrigues, Rafael, primary, Carballeira, Eduardo, additional, Silva, Fernanda M., additional, Caldo, Adriana, additional, Ceschini, Fabio, additional, Giráldez-García, Manuel A., additional, Abreu, Cidalina da Conceição Ferreira de, additional, Furtado, Guilherme Eustaquio, additional, and Teixeira, Ana Maria, additional

Published
2021
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14. Biotransformations

Author

Carballeira, J.D., primary, Fernandez-Lucas, J., additional, Quezada, M.A., additional, Hernaiz, M.J., additional, Alcantara, A.R., additional, Simeó, Y., additional, and Sinisterra, J.V., additional

Published
2009
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28. Fault Tolerant File Models for MPI-IO Parallel File Systems.

Author

Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Cappello, Franck, Herault, Thomas, Dongarra, Jack, Calderón, A., and García-Carballeira, F.

Abstract

Parallelism in file systems is obtained by using several independent server nodes supporting one or more secondary storage devices. This approach increases the performance and scalability of the system, but a fault in one single node can make the whole system fail. In order to avoid this problem, data must be stored using some kind of redundant technique, so that it can be recovered in case of failure. Fault tolerance can be provided in I/O systems by using replication or RAID based schemes. However, most of the current systems apply the same technique of fault tolerant at disk or file system level. This paper describes how fault tolerance support can be used by MPI applications based on PVFS version 2 [1], a well-know parallel file system for clusters. This support can be applied to other parallel file systems with many benefits: fault tolerance at file level, flexible definition of new fault tolerance scheme, and dynamic reconfiguration of the fault tolerance policy. [ABSTRACT FROM AUTHOR]

Published
2007
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29. A New I/O Architecture for Improving the Performance in Large Scale Clusters.

Author

Gavrilova, Marina, Gervasi, Osvaldo, Kumar, Vipin, Tan, C. J. Kenneth, Taniar, David, Laganà, Antonio, Mun, Youngsong, Choo, Hyunseung, García, L. M. Sánchez, Isaila, Florin D., Carballeira, Félix García, Pérez, Jesús Carretero, Rabenseifner, Rolf, and Adamidis, Panagiotis

Abstract

The technology advances made in supercomputers and high performance computing clusters over the past few years have been tremendous. Clusters are the most common solution for high performance computing at the present time. In this kind of systems, an important subject is the parallel I/O subsystem design. Parallel file systems (GPFS, PVFS, Lustre, etc) have been the solution used to obtain high performance I/O. Parallel file systems increase performance by distributing data file across several I/O nodes. However, cluster's size is increasing continuously, specially for compute nodes, becoming the I/O nodes in a possible bottleneck of the system. In this paper, we propose a new architecture that solves the problem pointed out before: new hierarchical I/O architecture based on parallel I/O proxies. Those I/O proxies execute on the compute nodes offering an intermediate parallel file system between the applications and the storage system of the cluster. That architecture reduces the load on the I/O nodes increasing the global performance. This paper shows the design of the proposed solution and a preliminary evaluation, using a cluster located in the Stuttgart HLRS center. keywords: Parallel File Systems, MPI-IO, High Performance Computing, Flash-IO, clusters. [ABSTRACT FROM AUTHOR]

Published
2006
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30. PSO vs. ACO, Data Grid Replication Services Performance Evaluation.

Author

Méndez, Víctor and Carballeira, Felix García

Abstract

Data Grid replication is critical for improving data intensive applications performance, providing fault tolerance and load balancing. Most of the techniques for data replication use Giggle as a framework for Replica Location Services (RLS), combined with other services for replica selection and optimization. Our previous work have proposed an enhanced Giggle framework, that simplify the location service using a flat catalogue structure, that combined with appropriate heuristic, obtain much better performances than traditional approaches. With this aim, we propose the use of Emergent Artificial Intelligence (EAI) techniques on data replication: Particle Swarm Optimisation(PSO) and Ant Colony Optimisation(ACO). This paper contribution is an experiment comparison between PSO, ACO, a canonical replication algorithm and other state of the art economic model replication algorithm. The experiments are design on two different network topologies. The simulation results confirm that PSO and ACO using the enhanced Giggle, improve performance over traditional solutions. [ABSTRACT FROM AUTHOR]

Published
2006
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