Distributing a n-body Problem Algorithm at Large-Scale over a Multi-Sites Grid using JavaSpace

1. Cracow Grid Workshop October, 18th 2006 Distributing a n-body Problem Algorithmat Large-Scale over a Multi-Sites Gridusing JavaSpace Virginie Galtier

2. JavaSpace Overview TupleSpace (à la Linda) + Java OO + Jini services (transactions…) API : • simple • rich to build distributed applications with shared memory paradigm

3. Objectives To investigate performance gains obtained when distributing a real and complete long-range data interaction application, on a large number of processors both on clusters and multi-sites grids. • impact of workers location ? • speed-up ? • evolution of best ExecTime/NbProc ratio when problem size increases ?

4. N-Body Problem long-range data interaction

5. 50 50 50 Worker Worker Worker 1 1 1 Master Distributed Algorithm homogeneous workers take write

6. 50 50 50 Worker Worker Worker 1 1 1 1 1 1 Master Distributed Algorithm read find 1 group among 3 (instead of 2 bodies among 6)

7. 50 50 50 Worker Worker Worker 1 1 1 1 1 1 1 1 Master Distributed Algorithm compute updated positions read computation/communication overlap

8. 50 50 50 Worker Worker Worker 2 2 1 2 2 1 2 2 2 1 Master Distributed Algorithm write take free space from intermediate results

9. 50 50 50 Worker Worker Worker 50 50 49 50 50 49 50 49 50 Master Distributed Algorithm

10. Testbed

11. Speed-up Study Speed-up Number of Workers

12. Size-up Efficiency (%) Number of Workers rule-of-thumb to maintain a 90% efficiency: double P when N doubles

13. Large Scale Extensibility Time / Body / Step (sec.) Number of Bodies O(N2)→O(N)

14. Future Work • different kind of application? • different JavaSpace implementation? • compare with other Java-based middleware (ProActive) • influence of fault-tolerance mechanisms?

15. Thank you