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Grid Adventures on DAS, GridLab and Grid'5000

Explore the benefits of Distributed ASCI Supercomputer (DAS), a homogeneous system ideal for clean laboratory-like research experiments, offering controlled grid environments for Java-centric programming and distributed supercomputing. Discover how DAS fits into the larger landscape of grid experimentation alongside GridLab and Grid’5000.

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Grid Adventures on DAS, GridLab and Grid'5000

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  1. Grid Adventures on DAS,GridLab and Grid'5000 Vrije Universiteit Amsterdam Faculty of Sciences Henri Bal

  2. Background (1) • Distributed ASCI Supercomputer (DAS) • Joint distributed infrastructure of ASCI research school • Long history and continuity • DAS-1 (1997), DAS-2 (2002), DAS-3 (Summer 2006) • Simple Computer Science grid that works • Homogeneous system • Stimulated new lines of CS research

  3. Background (2) • DAS is ideally suited for clear, laboratory-like (distributed) experiments • Projects: Albatross, MagPIe, Manta, Awari, …. • Over 200 users, 25 Ph.D. theses • Less suited for large-scale, heterogeneous grid experiments • Use GridLab testbed and Grid’5000

  4. VU (72) Amsterdam (32) SURFnet1 Gb/s Leiden (32) Delft (32) Utrecht (32) DAS-2 (2002-now) Configuration two 1 GHz Pentium-3s >= 1 GB memory 20-80 GB disk Myrinet interconnect Redhat Enterprise Linux Globus 3.2 PBS => Sun Grid Engine

  5. Outline • Example research projects that need grids • Ibis: Java-centric grid middleware • Satin: Divide & conquer parallelism on a grid • Zorilla: P2P supercomputing • Grid experiments on DAS-2, GridLab, Grid’5000 • Our colorful future: DAS-3

  6. The Ibis system • Programming support for distributed supercomputing on heterogeneous grids • Run non-trivially parallel applications on a grid • Fast RMI, group communication, object replication, d&c • Use Java-centric approach + JVM technology • Inherently more portable than native compilation • Requires entire system to be written in pure Java • Optimized special-case solutions with native code • Optimizations using bytecode rewriting • Several external users: • ProActive, VU medical center, University of Amsterdam, AMOLF, TU Darmstadt

  7. Ibis Overview Application ProActive RepMI RMI Satin GMI MPJ IPL TCP UDP P2P GM Panda Native Legend: Java

  8. Satin: a parallel divide-and-conquer system on top of Ibis • Divide-and-conquer isinherently hierarchical • More general thanmaster/worker • Satin: Cilk-like primitives (spawn/sync) in Java • Supports replicated shared objects with user-defined coherence semantics • Supports malleability (nodes joining/leaving) and fault-tolerance (nodes crashing)

  9. Zorilla: P2P supercomputing • Fully distributed Java-based system for running parallel applications • Uses P2P techniques • Supports malleable (Satin) applications • Uses locality-awareflood-scheduling algorithm

  10. Grid experiments • DAS is ``ideal’’, laboratory-like environment for doing clean performance experiments • GridLab testbed (incl. VU-DAS) is used for doing heterogeneous experiments • Grid’5000 is used for large-scale experiments

  11. Performance Ibis on wide-area DAS-2 (64 nodes) • Cellular Automaton uses Ibis/IPL, the others use Satin.

  12. GridLab testbed

  13. Testbed sites

  14. Experiences • Grid testbeds are difficult to obtain • Poor support for co-allocation • Firewall problems everywhere • Java indeed runs anywhere • Divide-and-conquer parallelism can obtain high efficiencies (66-81%) on a grid • See [van Reeuwijk, Euro-Par 2005]

  15. GridLab results • Efficiency normalized to single CPU type (1GHz P3)

  16. French Grid’5000 system • ``5000 CPUs distributed in 9 sites for research in Grid Computing, eScience and Cyber-infrastructures ‘’ • (Actually 2000) • See Franck’s talk

  17. Grid’5000 experiments • Used Grid’5000 for • Nqueens challenge (2nd Grid Plugtest) • Testing Satin’s shared objects • Large-scale Zorilla experiments • Experiences • No DNS-resolution for compute nodes • Using local IP addresses (192.168.x.y) for routing • Setting up connections to nodes with multiple IP addresses • Unable to run on Grid’5000 and DAS-2 simultaneously

  18. Grid’5000 results • Satin programs (using shared objects) • Running concurrently on clusters at Sophia-Antipolis, Bordeaux, Rennes, and Orsay

  19. Zorilla on Grid’5000 • N-Queens divide-and-conquer Java application • Six Grid’5000 clusters • 338 machines • 676 processors • 22-queens in 35 minutes

  20. Processor allocation results

  21. Comparison • DAS • Ideal for speedup measurements (homogeneous) • Bad for heterogeneous or long-latency experiments • GridLab testbed • Useful for heterogeneous experiments (e.g. Ibis) • Small-scale, unstable • Grid’5000 • Excellent for large-scale experiments • Bad connectivity to outside world (DAS)

  22. CPU’s R CPU’s R CPU’s R NOC CPU’s R CPU’s R DAS-3 • DWDM backplane • Dedicated optical group of lambdas • Can allocate multiple 10 Gbit/s lambdas between sites

  23. Summary • DAS is a shared infrastructure for experimental computer science research • It allows controlled (laboratory-like) grid experiments • We want to use DAS as part of larger international grid experiments (e.g. with Grid’5000)

  24. Acknowledgements • Rob van Nieuwpoort • Jason Maassen • Kees Verstoep • Gosia Wrzesinska • Niels Drost • Thilo Kielmann • Ceriel Jacobs • Kees van Reeuwijk • Many others • Grid’5000 • Andy Tanenbaum • Bob Hertzberger • Henk Sips • Lex Wolters • Dick Epema • Cees de Laat • Aad van der Steen More info: http://www.cs.vu.nl/das2/

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