WP10 Goals and accomplishments from WP10 point of view J. Montagnat, CNRS, CREATIS V. Breton, CNRS/IN2P3 DataGrid Biomedical Work Package.

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Presentation transcript:

WP10 Goals and accomplishments from WP10 point of view J. Montagnat, CNRS, CREATIS V. Breton, CNRS/IN2P3 DataGrid Biomedical Work Package

WP10 Initial goals  Make a list of requirements for biomedical grids (Task 10.1 ending PM6)  Genomics and medical imaging  Identify and characterize a first biomedical application to deploy on DataGrid testbed (Task 10.2 ending PM12)  Deploy this first application on DataGrid testbed2 and testbed3 (Task 10.3 ending PM 36)

WP10 Challenges  The biomedical community has NO strong center of gravity in Europe  No equivalent of CERN (High-Energy Physics) or ESA (Earth Observation)  Many high-level laboratories of comparable size and influence without a practical activity backbone (EMB-net, national centers,…) leading to:  Little awareness of common needs  Few common standards  Weak common long-term investment  The biomedical community is very large  The biomedical community is often distant to computer science issues

WP10 The WP10 policy during 2001  Because of the specific challenges, WP10 initial goals were less ambitious than the goals of the other application working groups (WP8-9) at the beginning of the project  We chose however to follow closely their path and rhythm :  To demonstrate ASAP to the biomedical community that grids are useful for genomics and medical imaging  To convince more people to join the Working Group  To participate actively to the design of the grid architecture and services from the beginning

WP10 WP10 first technical accomplishments  Deliverables on time  D10.1 « Requirements for grid-aware biology applications” in August 2001  D10.2 “Grid-aware biomedical applications for DataGrid testbed assessment” in January 2002  7 applications identified (5 in genomics, 2 in medical imaging)  Demonstrator « DataGrid for human health » (shown at IST2001)

WP10 WP10 first technical accomplishments  Creation of the Biomedical Virtual Organization on DataGrid testbed1  « Gridification » of a first representative algorithm for genomics comparative analysis  See demo this afternoon  Participation to testbed1 testing

WP10 3 layers for Biomedical applications  Distributed Algorithms. New distributed "grid-aware" algorithms (c.f. this afternoon's demonstration).  Grid Service Portals. Service providers taking advantage of the DataGrid computational power and storage capacity.  Cooperative Framework. Use the DataGrid as a cooperative framework for sharing resources, algorithms, and organize experiments in a cooperative manner. Cooperative Framework Grid Service Portals Distributed Algorithms EDG Middleware WP10 applications

WP10 Biomedical requirements  Large user community  anonymous/group login  Data management  data updates and data versioning  Security  disk / network encryption  Limited response time  fast queues  High priority jobs  privileged users  Interactivity  communication between user interface and CE’s  Parallelization  MPI site-wide / grid-wide  Pipeline processing  pipeline description language / scheduling These requirements were discussed with Middleware Work Packages : some should be addressed by testbed2.

WP10 Conclusions  Key achievements  We have started to demonstrate the relevance of the grid concept for biomedical applications (see demo)  We have established a fruitful dialog with  Biology and medical imaging (25 laboratories involved)  The other applications and middleware work packages of DataGrid  The group has found cohesion and coherence despite the various backgrounds of the actors (biologists, computer scientists, physicists)  Key challenges for the lifetime of the DataGrid project  Deploy a true biomedical testbed involving genomics, bio-informatics and medical imaging laboratories (5 sites on testbed1)  Run the applications described in D10.2 to demonstrate the use of a grid in genomics and medical imaging

WP10 Dublin Hinxton(EBI) Uppsala Stockholm (Karolinska) Madrid(CNB) Evry(CNG) Clermont Montpellier Lyon CERN Lausanne(EPFL,SIB) Padova Moscow Kosice The growing interest of the biomedical community …