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CBRAIN An Integrated Web Platform for Neuroimaging Tarek Sherif - PowerPoint PPT Presentation

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CBRAIN An Integrated Web Platform for Neuroimaging Tarek Sherif EGI User Forum April 2011 Dr. Alan Evans Laboratory Funded by CANARIE CANADA'S ADVANCED RESEARCH AND INNOVATION NETWORK http://www.canarie.ca Network: CAnet & Global

  1. CBRAIN An Integrated Web Platform for Neuroimaging Tarek Sherif EGI User Forum April 2011 Dr. Alan Evans Laboratory

  2. Funded by CANARIE CANADA'S ADVANCED RESEARCH AND INNOVATION NETWORK http://www.canarie.ca

  3. Network: CAnet & Global Lambda Facility

  4. Canadian Brain Imaging Research Network Global Brain Imaging Research Network

  5. Summary - Neuroimaging Overview - Scientific Data Flow - CBRAIN: A Distributed Computing Platform for Neuroimaging - CBRAIN as a Web Service

  6. What is Neuroimaging? Clinical Expertise Physical Basic Sciences Neuroscience 3 Tesla MRI Brain Imaging Techniques: - Magnetic Resonance Imaging (MRI) - Functional MRI (fMRI) - Position Emission Tomography (PET) - Magnetoencephalography (MEG) Imaging High Performance Technology Computing

  7. Neuroimaging Research Population Studies: Alzheimer’s Disease  Multiple Sclerosis  Autism  Schizophrenia  Normal brain development  Alzheimer loss of cortical thickness Multiple Sclerosis lesions Normal Brain Development in Children

  8. Population Studies - Hundreds/thousands brain scans - Images not aligned across subjects or scans - Difficult to compare one brain to another Not Registered Registered

  9. Extracting Features from Data - Skull Masking - Registration in Stereotaxic Space - Tissue Classification lobe-based cortical thickness - 3D Volumes - Cortical Thickness - Gyrification Index complexity - Lobe-based Complexity - 3-5 hours per scan MS Lesion Model - Hundreds of scans per study - GBs of data - 1000s of CPU hours

  10. Tools Many valuable tools exist for Neuroimaging - Desktop based - Hard to install - Hard to use - Compute intensive - Not collaborative

  11. “Modern” Data Flow Analysis Data Compute (visualisation) Knowledge

  12. “Modern” Data Flow Analysis Data Compute (visualisation) Knowledge Data: - Lots of formats (some are weird)! - Lots of it (GB - TB+) - Security - Acquisition quality - Completeness - Annotation

  13. “Modern” Data Flow Analysis Data Compute (visualisation) Knowledge

  14. “Modern” Data Flow Analysis Data Compute (visualisation) Knowledge Compute: - Lots of tools of various quality - Open source & proprietary - Large amounts of compute - Compute access - Data transfer - Cost

  15. “Modern” Data Flow Analysis Data Compute (visualisation) Knowledge

  16. “Modern” Data Flow Analysis Data Compute (visualisation) Knowledge Analysis: - Lots of tools of various quality - Open source & proprietary - 3D is often desktop based (not collaborative) - Large data often requires infrastructure (cost)

  17. CBRAIN: An Integrated Web Platform

  18. Goal: Lightweight Distributed Architecture Nothing specific to Neuroimaging Distributed Data Distributed Computing Distributed Users

  19. Simple Web Interface Data

  20. Simple Web Interface Compute

  21. Simple Web Interface Results Visualisation

  22. Distributed Components Separation of work is key CBRAIN Portal Presentation Models, Logic, Coordination LIGHT network & compute HTTP SSL XML SQL SSH Database (MySQL) MetaData Instances: data, users, jobs, tools, HPCs DB States Execution Servers Control of resources (HPC, Web Services...) HEAVY network & compute Data sync SSH Data Providers Files Networked File Servers, Databases

  23. Distributed Platform

  24. Distributed Platform Scientist Montréal DB PORTAL

  25. Distributed Platform Vancouver DB Data Provider DATA Scientist Montréal DB PORTAL

  26. Distributed Platform Vancouver DB Data Provider DATA Scientist Montréal Sherbrooke RQCHP DB PORTAL COMPUTE

  27. Distributed Platform Vancouver DB Data Provider DATA Scientist Montréal Sherbrooke 1 RQCHP DB PORTAL COMPUTE

  28. Distributed Platform Vancouver DB Data Provider DATA Scientist Montréal Sherbrooke 1 RQCHP DB 2 PORTAL Execution Controller Cluster Head Node COMPUTE

  29. Distributed Platform Vancouver DB Data Provider DATA Scientist Montréal Sherbrooke 1 RQCHP DB Workers 3 2 PORTAL Execution Controller Cluster Head Node COMPUTE

  30. Distributed Platform Vancouver DB Data Provider DATA Scientist Montréal Sherbrooke 1 4 RQCHP DB Workers 3 2 PORTAL Execution Controller Cluster Head Node COMPUTE

  31. Distributed Platform Vancouver DB Data Provider DATA Scientist Montréal Sherbrooke 1 4 RQCHP DB 5 Workers 3 Scheduler 2 PORTAL Execution Controller Cluster Head Node COMPUTE

  32. Distributed Platform Vancouver DB Data Provider 6 DATA Scientist Montréal Sherbrooke 1 4 RQCHP DB 5 Workers 3 Scheduler 2 PORTAL Execution Controller Cluster Head Node COMPUTE

  33. Distributed Platform Vancouver Vancouver 5 Workers 4 3 DB Scheduler 6 Data Provider Execution Controller Cluster Head Node 6 DATA COMPUTE Scientist Montréal 2 Sherbrooke 1 4 RQCHP DB 5 Workers 3 Scheduler 2 PORTAL Execution Controller Cluster Head Node COMPUTE

  34. Distributed Platform Vancouver Vancouver 5 Workers 4 3 DB Scheduler 6 Data Provider Execution Controller Cluster Head Node 6 DATA COMPUTE Scientist Montréal 2 Sherbrooke 1 Status 4 Job Control 7 RQCHP DB 5 Workers 3 Scheduler 2 PORTAL Execution Controller Cluster Head Node COMPUTE

  35. Achievements Illustrative Performance Comparison NIH-Pediatric-Obj1 : up to 3 visits per subject 866 CIVET pipeline runs to generate cortical thickness maps Input : 866 x 3 x 5Mb = 15Gb Output : 866 x 250 Mb = 211Gb Maximum Performance Maximum Performance Typical Performance ypical Performance Cluster Total CPU-hrs # cores Execution # cores Execution time (h) time (h) mammouth-ms2 866 x 4 = 3464 ~500 3 176 20 (RQCHP -Sherbrooke) CLUMEQ-Krylov 866 x 6 = 5196 ~90 58 24 216 (McGill) BIC (Linux) 866 x 8 = 6928 ~100 69 40 173 In general, studies which use to takes 1 week to 1 month now take 1 day.

  36. HPC Integration (8 compute installations, 80,000+ core) JUROPA – Julich (26304 cores) Colosse - CLUMEQ (7616 cores) Orcinus - Westgrid (3072 cores) Mammouth II - RQCHP (2464 cores) Kraken - SHARCNET McGill - CLUMEQ & Local Servers GPC - SciNET (3774 cores) (350 - 16000 cores) (30240 cores)

  37. Collaboration - The integrative approach of CBRAIN makes sharing resources extremely simple. - CBRAIN uses Projects to define permissions, similar to groups in Unix. • Each resource in the system (files, HPCs, Data Providers) is assigned a Project. • All users in a given Project have access to any resources associated with that Project.

  38. Infrastructures Continental Access for Communities CBRAIN LONI Community Community McGILL neuGRID Community UCLA FBF outGRID

  39. CBRAIN as a Web Service - Allow interactions from clients other than web browsers. - RESTful API. - XML and JSON-based interactions.

  40. CBRAIN-LONI Interoperability Demo

  41. CBRAIN-LONI Interoperability Demo

  42. CBRAIN-LONI Interoperability Demo

  43. CBRAIN-LONI Interoperability Demo

  44. CBRAIN as a Web Service - What’s been done: • Most key CBRAIN resources are now available through a RESTful interface. • Outside applications can now get lists of files/tasks, submit jobs, etc. - What’s left to be done: • CBRAIN’s integrated framework is meant to handle data already in the system. • I.e. Files are meant to be registered with the system, so that CBRAIN can track them, avoid redundancy, etc. • It must be decided how external systems will handle getting their data into and out of CBRAIN in a reasonable manner.

  45. Team: tsherif@bic.mni.mcgill.ca alan.evans@mcgill.ca Montreal Neurological Institute, McGill University (Lead) http://cbrain.mcgill.ca Principal Investigator: Alan Evans Program Manager: Reza Adalat System Architect: Marc Rousseau Developers: Pierre Rioux, Tarek Sherif, Angela McCloskey, Nicolas Kassis, Samir Das, David Brownlee System Administrator: Tien Duc Nguyen McGill Office of Technology Transfer (OTT): Francoys Labonte Canada National Research Council: Louis Borgeat Consultants: Rosanne Aleong, Claude Lepage, Pierre Bellec, Andrew Janki, Robert Vincent Remote Sites: Rotman Research Institute, University of Toronto Principal Investigators: Stephen Strother and Randy McIntosh Developers: Anda Pacurar, Anita Oder, Jacques Waller Robarts Research Institute, University of Western Ontario Principal Investigators: Ravi Menon and Mel Goodale Developers: Martyn Klassen, Ronghai Tu Unité de Neuroimagerie Fonctionnelle, Université de Montréal Principal Investigators: Julien Doyon and Rick Hoge Developer: Mathieu Desrosiers Division of Neurology, University of British Colombia Principal Investigators: Jon Stoessl and Max Cynader Developers: Ryan Thomson, Nasim Vafai NCMIR, University of California San Diego, USA Principal Investigators: Mark Ellisman System Administrator: Raj Singh INM, Julich Forschungszentrum, Germany Principal Investigators: Karl Ziles and Uwe Pietrzyk Scientist: Hartmut Mohlberg CNA, Hanyang University, South Korea Principal Investigators: Jong-min Lee LONI, University of California Los Angeles, USA Principal Investigators: Arthur Toga

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