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A Practical Guide to Phylogenetics for Nonexperts
Published on: February 5, 2014
Gridifying phylogeny and medical applications on the volunteer computing platform XtremWeb-CH
Nabil Abdennadher1, Claude Evéquoz, Cédric Billat
1University of Applied Sciences Western Switzerland, Geneva, Switzerland. Nabil.Abdennadher@hesge.ch
Studies in Health Technology and Informatics
|June 19, 2008
Summary
XtremWeb-CH (XWCH) middleware enables scientists to run demanding parallel applications on public computing resources. Lessons learned from deploying Phylip and NeuroWeb inform XWCH enhancements for broader high-performance computing support.
Area of Science:
- Computer Science
- Computational Biology
- Neuroscience
Background:
- Volunteer computing middleware simplifies deployment of parallel and distributed applications on public infrastructure.
- High-performance applications with significant storage and communication needs present deployment challenges.
- Existing middleware may not adequately support data and CPU-intensive scientific applications.
Purpose of the Study:
- To describe the XtremWeb-CH (XWCH) platform components.
- To detail the process and outcomes of porting and deploying Phylip and NeuroWeb on XWCH.
- To identify necessary XWCH extensions for supporting diverse high-performance applications.
Main Methods:
- Deployment of the Phylip package for phylogenetic tree inference on the XWCH platform.
- Integration of the NeuroWeb application for generating dynamic neuronal maps within the XWCH environment.
- Analysis of performance and resource utilization for both applications on the volunteer computing infrastructure.
Main Results:
- Successful deployment of Phylip, addressing its CPU-intensive module limitations for large datasets.
- Functional execution of NeuroWeb, a data and CPU-intensive medical application, on XWCH.
- Identification of specific challenges and requirements for gridifying scientific applications.
Conclusions:
- XWCH effectively supports high-performance, resource-intensive scientific applications.
- Lessons learned from Phylip and NeuroWeb deployments are guiding the development of new XWCH features.
- Ongoing enhancements aim to broaden XWCH's applicability to a wider range of parallel and distributed computing tasks.
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