Virtual Laboratory for e-Science (VL-e)

1. vrije Universiteit Virtual Laboratory fore-Science (VL-e) Henri Bal Department of Computer ScienceVrije Universiteit Amsterdam bal@cs.vu.nl

2. Outline • e-Science and virtual laboratories • The VL-e project • VL-e and networking • Case studies: • Visualization • Interactive problem solving environments • Distributed supercomputing • Computing/networking infrastructure

3. e-Science • Web is about exchanging information • Grid is about sharing resources • Computers, data bases, instruments, services • e-Science supports experimental science by providing a virtual laboratory on top of Grids

4. Grid Harness multi-domain distributed resources Virtual Laboratories Distributed computing Application Specific Part Application Specific Part Application Specific Part Visualization & collaboration Potential Generic part Potential Generic part Potential Generic part Management of comm. & computing Virtual Laboratory Application oriented services Management of comm. & computing Management of comm. & computing Knowledge Data & information

5. User Interfaces & Virtual reality based visualization Virtual Laboratory for e-Science 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

6. vrije Universiteit The VL-e project • 20 partners • Academic - Industrial • 40 M€ (20 M€ BSIK funding) • 2004 - 2008

7. VL-e and networking • e-Science applications generate much (distributed) data • High-resolution imaging • Bio-informatics queries • Particle physics: • Currently: 1 PByte per year • LHC (2007): 10-30 PByte per year • Virtual laboratories need high-speed networks for • Remote visualization • Interactive problem solving environments • Distributed supercomputing

8. Telescience Food Bio-Inf Bio-div Medical imaging Data Intensie Visualization i PSE A.I.D. Virtual lab CIM High-performancedistributed computing Security Optical Networking VL-e and networking

9. Visualization on the Grid

10. Visualization on the Grid

11. Visualization on the Grid

12. Visualization on the Grid

13. Visualization on the Grid

14. ui (VRE) MRI, PET Monolith, Cluster Cave, Wall, PC, PDA From Medical Image Acquisition to Interactive Virtual Visualization… Simulated blood flow MR image Patient at MRI scanner MR image Segmentation Shear stress, velocities GVK LB Solver Medical Data MD login and Grid Proxy creation Bypass creation LB mesh generation Job submission ce (e.g., Bratislava) ce (e.g., Valencia) se (e.g., Leiden) • P.M.A. Sloot, A.G. Hoekstra, R.G. Belleman, A. Tirado-Ramos, E.V. Zudilova, D.P. Shamonin, R.M. Shulakov, A.M. Artoli , L. Abrahamyan Interactive Problem Solving Environments Virtual Node navigation Job monitoring Simulated Blood Flow VRE

15. Distributed supercomputing (parallel computing on grids) DAS-2 VU (72 nodes) UvA (32) GigaPort Leiden (32) Delft (32) Utrecht (32) Distributed ASCI Supercomputer 2

16. Distributed supercomputing (parallel computing on grids)

17. HPC on a grid? • Can grids be used for High-Performance Computing applications that are not trivially parallel? • Key: grids usually are hierarchical • Collections of clusters, supercomputers • Fast local links, slow wide-area links • Can optimize algorithms to exploit this hierarchy • Message combining + latency hiding on wide-area links • Optimized collective communication operations (broadcast etc.) • Often gives latency-insensitive, throughput-bound algorithms

18. Ibis: a Java-centric grid programming environment • Written in pure Java, runs on heterogeneous grids • “Write once, run everywhere ” • Many applications: • Electromagnetic simulation (Jem3D) • Automated protein identification (VL-e application from AMOLF) • N-body simulations • SAT-solver • Raytracer Jem3D (see SC’04) Available from www.cs.vu.nl/ibis

19. Networking demands • Low latency is needed for • Interactive visualization • Interactive Problem Solving Environments • Synchronous, latency-sensitive parallel algorithms • High throughput is needed for • Data-intensive e-Science applications • Visualization of large data sets • Asynchronous, throughput-bound parallel algorithms • Efficient collective (group) communication for • Collaborative visualization between multiple sites • Collective operations in parallel algorithms

20. Outline • e-Science and virtual laboratories • The VL-e project • VL-e and networking • Examples: • Visualization • Interactive Problem Solving Environments • Distributed supercomputing • Computing/networking infrastructure

21. VL-e environments Application specific service Medical Application Telescience Bio ASP 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

22. DAS-3 • Proposed next generation grid in the Netherlands • Partners: • ASCI research school (VU, UvA, TU Delft, Leiden) • Gigaport-NG/SURFnet: DWDM computer backplane (dedicated optical group of 8 lambdas) • VL-e and MultimediaN BSIK projects • Topology controlled by applications through the Network Operations Center

23. CPU’s R CPU’s R CPU’s R NOC CPU’s R CPU’s R DAS-3

24. Summary • VL-e (Virtual Laboratory for e-Science) studies entire e-Science chain, including applications, middleware and grids • High networking demands from applications and generic methods • New state-of-the-art Grid infrastructure planned for 2006 using optical networking