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High-Performance and Grid Computing for Neuroinformatics: NIC and Cerebral Data Systems Allen D. Malony University of Oregon Professor Department of Computer.

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Presentation on theme: "High-Performance and Grid Computing for Neuroinformatics: NIC and Cerebral Data Systems Allen D. Malony University of Oregon Professor Department of Computer."— Presentation transcript:

1 High-Performance and Grid Computing for Neuroinformatics: NIC and Cerebral Data Systems Allen D. Malony University of Oregon Professor Department of Computer and Information Science Director NeuroInformatics Center Computational Science Institute

2 NIA VisitApril 6, 2006 High-Performance and Grid Computing for Neuroinformatics Neuroinformatics  Understanding brain function requires the integration of information across many levels  Physical and functional  Gene to behavior  Microscopic and macroscopic  Challenges in brain observation and modeling  Structure and organization  Operational and functional dynamics  Physical, functional, and cognitive  Challenges in scale  How to create and maintain of integrated views of the brain for both scientific and clinical purposes?

3 NIA VisitApril 6, 2006 High-Performance and Grid Computing for Neuroinformatics  Problem solving environment for brain analysis Computational Science  Human Neuroscience  Computational methods applied to scientific research  High-performance simulation of complex phenomena  Large-scale data analysis and visualization  Understand functional activity of the human cortex  Multiple cognitive, clinical, and medical domains  Multiple experimental paradigms and methods  Need for coupled/integrated modeling and analysis  Multi-modal (electromagnetic, MR, optical)  Physical brain models and theoretical cognitive models  Need for robust tools: computational and informatic

4 NIA VisitApril 6, 2006 High-Performance and Grid Computing for Neuroinformatics UO Brain, Biology, and Machine Initiative  University of Oregon interdisciplinary research in cognitive neuroscience, biology, computer science  Human neuroscience focus  Understanding of cognition and behavior  Relation to anatomy and neural mechanisms  Linking with molecular analysis and genetics  Enhancement and integration of neuroimaging facilities  Magnetic Resonance Imaging (MRI) systems  Dense-array EEG system  Computation clusters for high-end analysis  Establish and support UO institutional centers

5 NIA VisitApril 6, 2006 High-Performance and Grid Computing for Neuroinformatics NeuroInformatics Center (NIC) at UO  Application of computational science methods to human neuroscience problems  Tools to help understand dynamic brain function  Tools to help diagnosis brain-related disorders  HPC simulation, large-scale data analysis, visualization  Integration of neuroimaging methods and technology  Need for coupled modeling (EEG/ERP, MR analysis)  Apply advanced statistical signal analysis (PCA, ICA)  Develop computational brain models (FDM, FEM)  Build source localization models (dipole, linear inverse)  Optimize temporal and spatial resolution  Internet-based capabilities for brain analysis services, data archiving, and data mining

6 NIA VisitApril 6, 2006 High-Performance and Grid Computing for Neuroinformatics NIC Organization  Allen D. Malony, Director  Don M. Tucker, Associate Director  Sergei Turovets, Computational Physicist  Bob Frank, Mathematician  Dan Keith, Software Engineer (distributed systems grid)  Chris Hoge, Software Engineer (computational)  Ryan Martin / Brad Davidson, Systems administrators  Gwen Frishkoff, Research Associate, U. Pittsburgh  Kai Li, Ph.D. student (brain segmentation)  Adnan Salman, Ph.D. student (computational modeling)  Performance Research Lab

7 NIA VisitApril 6, 2006 High-Performance and Grid Computing for Neuroinformatics ICONIC Grid SMP Server IBM p655 Graphics SMP SGI Prism Shared Storage System Gbit Campus Backbone NICCIS Internet 2 Shared Memory IBM p690 Distributed Memory IBM JS20 CNI Distributed Memory Dell Pentium Xeon NIC 4x816 2x82x16 graphics workstationsinteractive, immersive vizother campus clusters 40 Terabytes Tape Backup 112 total processors

8 NIA VisitApril 6, 2006 High-Performance and Grid Computing for Neuroinformatics GEMINI Project  “Grid-based Electromagnetic Integrated Neuroimaging”  Goals  Dynamic neuroimaging algorithms and visualization  High-end tool integration and environments  High-performance computational server integration  Grid-based processing, data sharing, and collaboration  Neuroinformatics data ontologies

9 NIA VisitApril 6, 2006 High-Performance and Grid Computing for Neuroinformatics GEMINI Architecture

10 NIA VisitApril 6, 2006 High-Performance and Grid Computing for Neuroinformatics Computational Integrated Neuroimaging System

11 NIA VisitApril 6, 2006 High-Performance and Grid Computing for Neuroinformatics Cerebral Data Systems  Partnership between EGI and University of Oregon  Develop and market neuroinformatics services  Neurological medical data transfer, storage, and analysis  High-performance and sophisticated EEG and MR analysis  Telemedicine and distributed collaboration  Shared brain repositories  Target markets  Research and clinical  Epilepsy diagnosis and pre-surgical planning  MR image segmentation  Technology integration  Internet and computional grids  High-performance computing

12 NIA VisitApril 6, 2006 High-Performance and Grid Computing for Neuroinformatics CDS Computational Server and Imaging Clients


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