ICT infrastructure for Science: e-Science developments

1. ICT infrastructure for Science: e-Science developments Henri Balbal@cs.vu.nlVrije Universiteit Amsterdam

2. Outline • What is e-Science? • Virtual Laboratory for e-Science (VL-e) • Research infrastructure of VL-e • Some VL-e results • Future developments in the Netherlands

3. Science is changing • System level science • the integration of diverse sources of knowledge about the constituent parts of a complex system with the goal of obtaining an understanding of the system's properties as a whole [Ian Foster] • Multidisciplinary research • Each discipline can solve only part of a problem • Collaborations betweens distributed research groups • Research driven by (distributed) data • Data explosion, both volume and complexity

4. Examples • Functioning of the cell for system biology • Cognition • Cancer research • Cohort studies in medicine (biobanking) • Discovery of biomarkers for drug design • Ecosystems/biodiversity • Studies of water/air pollution • Study black matter

5. e-Science • Goal: allow scientists to collaborate in experiments and integration of research • Enable system level science • Design methods to optimally exploit underlying infrastructure • Hardware (network, computing, datastorage) • Software (web, grid middleware)

6. e-Science in context Sytem level experiments e-Science Web/grid software Infrastructure

7. Virtual Laboratory fore-Science (VL-e) • 40 M€ BSIK project (2004-2009) • Generic application support • Application cases are drivers for computer & computational science and engineering research • Re-use of components via generic solutions • Rationalization of experimental process • Reproducible & comparable

8. User Interfaces & Virtual reality based visualization VL-e Bio-diversity Telescience Food Informatics Bio-Informatics Data Intensive Science Medical diagnosis & imaging Interactive PSE Adaptive information disclosure Virtual lab. & System integration Collaborative information Management High-performancedistributed computing Security & Generic AAA Optical Networking

9. The VL-e infrastructure Application specific service Application Potential Generic service & Virtual Lab. services Virtual Lab. rapid prototyping (interactive simulation) Virtual Laboratory Additional Grid Services (OGSA services) Grid Middleware Grid & Network Services Network Service (lambda networking) Gigaport VL-E Proof of concept Environment VL-E Experimental Environment Proof-of-Concept Rapid Prototyping (DAS-3)

10. DAS-3 272 nodes(AMD Opterons) 792 cores 1TB memory LAN: Myrinet 10G Gigabit Ethernet WAN: 20-40 Gb/s OPN

11. Applications can dynamically allocate light paths and change the topology of the wide-area network • Applications: model checking, game tree search, processing CineGrid data (4K video) • Kees Verstoep’s talk (yesterday)

12. BiG Grid Rapid prototyping (interactive simulation) Virtual Laboratory Virtual Laboratory Additional Grid Services (OGSA services) Grid Middleware Grid Middleware Network Service (lambda networking) Surfnet Surfnet VL-E Proof of concept Environment VL-E Experimental Environment Big Grid

13. Outline • What is e-Science? • Virtual Laboratory for e-Science (VL-e) • Research infrastructure of VL-e • Some VL-e results • Applications • Generic application support (middleware) • Future developments in the Netherlands

14. Group Activation Map fMRI scan MR scanner Stimulus System for Cognitive research Intro fMRI Functional MRI: Analysis Brain activation maps • Large datasets, many instances • Computation demanding analysis • Distributed resources (scanning, analysis) • Collaboration (data, methodology)

15. MedicalApplications … … Virtual Laboratory Grid Middleware Surfnet VL-e Environment Medical Diagnosis and ImagingProblem Solving Environment Application specific services: • Access to PACS, DICOM • Interfaces to medical scanners (MRI) • In-house developed algorithms: • Eddy Current Reduction • Matched Masked Bone Elimination • Authentication & authorization Stimulus System 3 Tesla MRI VL-e generic services: • Provides: • Scientific visualization techniques • SRB • Resource browsing • Workflow management • Job submission • Data querying • Uses: • V Browser on SRB • Parallel processing techniques • VLAM Grid services: • Storage facilities • High Performance Computing platforms • High Performance Visualization

16. Bird behaviour in relation to weather and landscape RADAR Calibration and Data assimilation Predictions and on-line warnings Bird distributions Ensembles Dynamic bird behaviour MODELS

17. Ibis – Grid programming • Goal: • drastically simplify grid programming/deployment • applications running on many co-allocated resources (``grids as promised’’)

18. Ibis system

19. Ibis applications • e-Science (VL-e) • Brain MEG-imaging • Mass spectroscopy • Grammar learning • Multimedia content analysis • Other programming systems • Workflow engine for astronomy (D-grid), grid file system, ProActive, Jylab, …

20. Multimedia content analysis • Analyzes video streams to recognize objects • Extract feature vectors from images • Describe properties (color, shape) • Data-parallel task implemented with C++/MPI • Compute on consecutive images • Task-parallelism on a grid

21. ‘Most Visionary Research’ award at AAAI 2007, (Frank Seinstra et al.) MMCA

22. Discussion about infrastructure • Need well-balanced infrastructure supporting compute/data/network-intensive applications • Generic software is part of the infrastructure • Key to obtain flexibility • Organization is important, different roles • Application experiments • Computer Science experiments • Production • Building infrastructure is research in itself

23. Next: national e-Science centre? • Coordinate e-Science research • Software services needed for e-Science • Organize support • Help in developing policies for infrastructure