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ScaBIA: Scalable brain image analysis in the cloud
KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz). KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.
KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz). KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.
KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz). KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.ORCID iD: 0000-0002-9901-9857
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2013 (English)In: CLOSER 2013 - Proceedings of the 3rd International Conference on Cloud Computing and Services Science, 2013, 329-336 p.Conference paper, Published paper (Refereed)
Abstract [en]

The use of cloud computing as a new paradigm has become a reality. Cloud computing leverages the use of on-demand CPU power and storage resources while eliminating the cost of commodity hardware ownership. Cloud computing is now gaining popularity among many different organizations and commercial sectors. In this paper, we present the scalable brain image analysis (ScaBIA) architecture, a new model to run statistical parametric analysis (SPM) jobs using cloud computing. SPM is one of the most popular toolkits in neuroscience for running compute-intensive brain image analysis tasks. However, issues such as sharing raw data and results, as well as scalability and performance are major bottlenecks in the "single PC"-execution model. In this work, we describe a prototype using the generic worker (GW), an e-Science as a service middleware, on top of Microsoft Azure to run and manage the SPM tasks. The functional prototype shows that ScaBIA provides a scalable framework for multi-job submission and enables users to share data securely using storage access keys across different organizations.

Place, publisher, year, edition, pages
2013. 329-336 p.
Keyword [en]
Brain imaging, Cloud computing, E-Science as a service, FMRI, Microsoft azure, SPM
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-133248Scopus ID: 2-s2.0-84884483231ISBN: 978-989856552-5 (print)OAI: oai:DiVA.org:kth-133248DiVA: diva2:660576
Conference
3rd International Conference on Cloud Computing and Services Science, CLOSER 2013; Aachen; Germany; 8 May 2013 through 10 May 2013
Note

QC 20131030

Available from: 2013-10-30 Created: 2013-10-29 Last updated: 2013-10-30Bibliographically approved

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Laure, Erwin

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