Key Objectives

1. Key Objectives • Parallel Programming Model and Tools • desesperatly needed • for the masses (New Scientist, New SME) • for new architectures (Multi-cores) • As Effective as possible: • Efficient • However Programmer/User Productivity is first Key • For both Multi-cores and Distributed • Actually the way around • Some Handling of ``Large-scale’’ (Grid, Clouds)

2. Intech, Jeudi 2 juillet 2009

4. Overview of Cloud, Parallel Computingand ProActive PACAGrid D.Caromel, et al. Speed: Application + Development: Productivity

6. Agenda 1. Background: OASIS team 2. Cloud Computing 3. ProActive Parallel Suite: Programming, Optimizing Scheduling 4. CPER ProActive PACA GRID 5. Use Cases & Demos

7. 1. Background Parallel & Distributed

8. OASIS Team, INRIA-UNSA-I3S/CNRS • A joint team, about 35 persons • Parallelism and Distribution, Proof, Verification • ProActive Parallel Suite From Multi-cores to Enterprise GRIDs

9. OASIS Team Composition (35) • PostDoc (1): • Regis Gascon (INRIA) • Engineers (10): • Elaine Isnard (AGOS) • Fabien Viale (ANR OMD2, Renault ) • Franca Perrina (AGOS) • Germain Sigety (INRIA) • Yu Feng (ETSI, FP6 EchoGrid) • Bastien Sauvan (ADT Galaxy) • Florin-Alexandru.Bratu (INRIA CPER) • Igor Smirnov (Microsoft) • Fabrice Fontenoy (AGOS) • Open position (Thales) • Trainee (2): • Etienne Vallette d’Osia (Master 2 ISI) • Laurent Vanni (Master 2 ISI) • Assistants (2): • Sylvie Lelaidier (INRIA) • Sandra Devauchelle (I3S) An international team with about 10 nationalities • Researchers (5): • D. Caromel (UNSA, Det. INRIA) • E. Madelaine (INRIA) • F. Baude (UNSA) • F. Huet (UNSA) • L. Henrio (CNRS) • PhDs (11): • Antonio Cansado (INRIA, Conicyt) • Brian Amedro (SCS-Agos) • Cristian Ruz (INRIA, Conicyt) • Elton Mathias (INRIA-Cordi) • Imen Filali (SCS-Agos / FP7 SOA4All) • Marcela Rivera (INRIA, Conicyt) • Muhammad Khan (STIC-Asia) • Paul Naoumenko (INRIA/Région PACA) • Viet Dung Doan (FP6 Bionets) • Virginie Contes (SOA4ALL) • Guilherme Pezzi (AGOS, CIFRE SCP) • + Visitors + Interns

10. 2. Cloud Computing 10

11. Clouds: Basic Definition • Dynamically scalable, often virtualized resources • Provided as a service over the Internet • Users need not have knowledge of, expertise in, or control over the technology infrastructure • Software as a service (SaaS), CRM, ERP • Platform as a service (PaaS), Google App Engine • Infrastructure as a service (IaaS), Amazon EC2

12. Clouds in Picture From Joseph Kent Langley

13. From Grids to Clouds • Grid Computing • Several administrative Domains • Virtual Organizations • Trading not based on Currency (Too) Hard • Still a strong need for Sharing: • Under used machines, Green IT pressure • TCO, Electric Bill • Services: Accessing a Hosted Software Distributed, //, & Grid Technologies for Clouds

14. Multi-Core Push

15. Symetrical Multi-Core: 8-ways Niagara II • 8 cores • 4 Native threads per core • Linux see 32 cores!

16. Multi-Cores: A Few Key Points • Moore’s Law rephrased: Nb. of Cores double every 18 to 24 months • Key expected Milestones: Cores per Chips (OTS) • 2010: 32 to 64 • 2012: 64 to 128 • 2014: 128 to 256 • 1 Million Cores Parallel Machines in 2012 • 100 M cores coming in 2020 • Multi-Cores are NUMA, and turning Heterogeneous (GPU) • They are turning into SoC with NoC: NOT SMP!

17. ProActive PACA Grid in Cloud Context

18. 3. ProActive Parallel Suite 18

19. Parallel Acceleration Toolkit in Java:Parallelism: Multi-Core+Distributed

22. ProActive : Active objects JVM A A WBN! A ag =newActive (“A”, […], VirtualNode) V v1 = ag.foo (param); V v2 = ag.bar (param); ... v1.bar(); //Wait-By-Necessity JVM ag v2 v1 V Wait-By-Necessity is a Dataflow Synchronization Java Object Active Object Req. Queue Future Object Proxy Thread Request 22

23. Standard system at Runtime: No Sharing NoC: Network On Chip Proofs of Determinism 23

24. TYPED ASYNCHRONOUS GROUPS 24

25. Broadcast and Scatter ag JVM c3 c3 c3 c3 c3 c3 c3 c1 c1 c1 c1 c1 c1 c1 c2 c2 c2 c2 c2 c2 c2 JVM JVM s s JVM • Broadcast is the default behavior • Use a group as parameter, Scattered depends on rankings cg ag.bar(cg); // broadcast cg ProActive.setScatterGroup(cg); ag.bar(cg); // scatter cg 25

26. Optimizing 26

28. IC2D

29. IC2D

30. ChartIt

31. Pies for Analysis and Optimization

32. Video 1: IC2D OptimizingMonitoring, Debugging, Optimizing

33. Scheduling 33

35. Scheduler: User Interface

36. Video 2:Scheduler, Resource Manager

37. 4. ProActive PACA GRID 37

38. The ProActive PACA Grid Platform (1) • Dell Blades • 160 cores • DELL PowerEdge LAME BLADE 19552 Intel Xeon E5335 2.0 Ghz quad core 2×4 Mo16GB 667MHZ FBD2 hdd 73Go SAS 15Krpm • Linux fedora Core 7Kernel 2.6.23.17-88 • Storage server: Dell PowerEdge P29502 Intel Xeon E5345 2.33 Ghz quad core 2×4 Mo6×500Go SATA 7.2Krpm RAID0

39. The ProActive PACA Grid Platform (2) • HP • HPCS Windows • 64 cores • 8 nodes :HP ProLiant BL460c2 Intel Xeon E5320 quad core 1.86 GHZ 8 Mo8GB 667MHZ FBD2 hdd 72Go hot plug 10Krpm RAID 0 • Windows HPC 2008 64 bits

40. The ProActive PACA Grid Platform (3) • Dell Blades • 384 cores • Total: • 608 Cores available Today • Potential Extension: • Grid 5000

42. Video 3:CPER ProActive PACA Grid

43. Use Cases & Demos Downstairs • AGOS, SOA, BPEL processes in parallel on the Grid, Franca Perrina • Life technologies: Genomic, Transcriptome Parallel Analysis, IPMC, Emil Salageanu • Price-It Excel, Finance, Vladimir Bodnartchouk • IC2D : An Eclipse GUI to Debug and Optimize your ProActive Application, Brian Amedro • CPER ProActive Paca Grid, Germain Sigety • Web Start for accessing ProActive Paca Grid, Florin Alexandru-Bratu 3 Demos Applicatives Visu, Debug ProActive PACA GRID

44. Summary

45. Conclusion: Available in PACA Grid Future Developments:Multi-Core + Distributed

46. The Future ProActive PACA Grid + Coeur Interactive + Mesocentre (OCA) + Clouds For: Science Labs and Local Industries (Large and SME)

47. Intech, Jeudi 2 juillet 2009

49. ProActive PACA Grid in Context