ICT infrastructure for Science: e-Science developments Henri Bal Vrije Universiteit Amsterdam

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

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

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

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)

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

Virtual Laboratory for e-Science (VL-e) ● 40 M€ BSIK project ( ) ● 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

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

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

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

● 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)

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

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

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

Medical Diagnosis and Imaging Problem Solving Environment 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 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 Grid Middleware Surfnet Virtual Laboratory VL-e Environment … Medical Applications … Grid services: Storage facilities High Performance Computing platforms High Performance Visualization Stimulus System 3 Tesla MRI

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

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

Ibis system

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, …

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

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

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

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