High Performance Computing in Biomedicine-Reference-Cited by-同舟云学术

High Performance Computing in Biomedicine

Published:2010 Issue: Volume: Page:106-118
ISSN:2327-7033
Container-title:Biocomputation and Biomedical Informatics
language:
Short-container-title:

Author:

Kyriazis Dimosthenis¹,Menychtas Andreas¹,Tserpes Konstantinos¹,Athanaileas Theodoros¹,Varvarigou Theodora¹

Affiliation:

1. National Technical University of Athens, Greece

Abstract

A constantly increasing number of applications from various scientific fields are finding their way towards adopting Grid technologies in order to take advantage of their capabilities: the advent of Grid environments made feasible the solution of computational intensive problems in a reliable and cost-effective way. This book chapter focuses on presenting and describing how high performance computing in general and specifically Grids can be applied in biomedicine. The latter poses a number of requirements, both computational and sharing / networking ones. In this context, we will describe in detail how Grid environments can fulfill the aforementioned requirements. Furthermore, this book chapter includes a set of cases and scenarios of biomedical applications in Grids, in order to highlight the added-value of the distributed computing in the specific domain.

Publisher

IGI Global

Reference34 articles.

1. Abramson, D., Giddy, J., & Kotler, L. (2000), “High Performance Parametric Modeling with Nimrod/G: Killer Application for the Global Grid?” IPDPS'2000, Mexico, USA.

2. G. Aloisio, M. Cafaro, S. Fiore, M. Mirto, (2005), “ProGenGrid: A Workflow Service Infrastructure for Composing and Executing Bioinformatics Grid Services”

3. Aloisio, G., Cafaro, M., Fiore, S., & Mirto, M. (2006), “A Split & Merge Data Management Architecture for a Grid Environment”, 19th IEEE Symposium on Computer-Based Medical Systems.

4. Ardaiz, L., Diaz de Cerio, L., Gallardo, A., Messeguer, R., & Sanjeevan, K. (2004), “ULabGrid Framework for Computationally Intensive Remote and Collaborative Learning Laboratories”, IEEE International Symposium on Cluster Computing and the Grid.

5. Boss, G., Malladi, P., Quan, D., Legregni, L., & Hall, H. (2007), “Cloud Computing”, http://www.ibm.com/developerworks/websphere/zones/hipods/