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