1 / 25

moais.imag.fr

M ulti-Pr o gramming and Scheduling Design for A pplications of I nteractive S imulation Jean-Louis Roch & al. http://moais.imag.fr. Louvre, Musée de l’Homme Sculpture (Tête) Artist : Anonyme Origin: Rapa Nui [Easter Island] Date : between the XIst and the XVth century

chloe
Download Presentation

moais.imag.fr

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Multi-Programming and Scheduling Design for Applications of Interactive SimulationJean-Louis Roch & al. http://moais.imag.fr Louvre, Musée de l’Homme Sculpture (Tête) Artist : Anonyme Origin: Rapa Nui [Easter Island] Date : between the XIst and the XVth century Dimensions : 1,70 m high EVALUATION SEMINAR -RESEARCH THEME Num B"Grids and high-performance computing"March 27-28, 2008

  2. Staff and Skills • 1/1/2005: Creation of MOAIS team1/1/2006: Creation of INRIA team-project MOAIS • Vincent Danjean [MdC 9/2005] • Pierre-François Dutot [MdC 9/2006] • Thierry Gautier [CR] • Guillaume Huard [MdC] • Grégory Mounié [MdC] • Bruno Raffin [CR INRIA] • Jean-Louis Roch [MdC, Team leader] • Denis Trystram [Prof] • Frédéric Wagner [MdC 9/2006] • 1 Invited Prof. Alfredo Goldman [USP Sao Paulo] • 19 PhD students, 1 engineer • 14 PhDs defended since 2005 • Parallel algorithms & programming • Scheduling • Interactive applications

  3. Evolution of parallel programming • Parallelism everywhere • Distributed, Heterogeneous MPSoC Grids Cluster SMP multi-core GPU MPI OpenMP Cuda [NVidia] MapReduce [Google] TBB [Intel] …SPIRIT Cilk++ [CilkArts] Fortress[Sun]

  4. input output MOAIS objective • End-to-end parallel programming solutionsfor high-performance interactive computing with provable performances. optimization computational steering, VR embedded • Performance is multi-objective QAP/Nugent on Grid’5000 [PRISM, GSCOP, DOLPHIN] Streaming on MPSoCs [ST] INRIA Grimage platform [MOAIS, PERCEPTION, EVASION]

  5. Approach • To mutually adapt application and scheduling. • Proactive/static to the platform : the devices evolve gradually • Online/dynamic to the execution context : data and resources • Tolerant to data variations, failures, other appli. perturbation,… • From algorithms to applications • Scheduling and parallel programming schemes • Programming interfaces and tools • Target applications : batch scheduling, combinatorial optimization, computational steering, stream encoding

  6. Research Directions 4. Interactivity  3. Adaptive algorithms 2. Interfaces for coordination Performance 1. Scheduling   Overview Interactive application Adaptive control of execution M O A I S model: abstract representation algorithm: scheduling, fault tolerance Architecture

  7. Research directions and achievements for 2005-2007 • Scheduling • Interfaces for coordination • Adaptive algorithms • Interactive applications

  8. 1. Scheduling • Objective A: modeling of scheduling problems for adaptive applications • Adaptable parallelism degree for efficient coarse grain scheduling • Parallel task models: moldable tasks, divisible load • Some results: • Comparisons and coupling models: [IJ FCS 06 ] • Off-line: improvement of performance ratio : • 3/2-approximation [SIAM J.Comp 07] instead of 2 [Turek&al] by strip-packing • (3+5) for moldable tasks on a grid of clusters [Europar’06] • On-line: decrease of control overhead : « work-first principle » [Cilk] • Extension to general distributed data-flow computations [ICTTA’06, ICCS’07] Task == | | … | . . .

  9. 1. Scheduling Objective B: Design of multi-objective scheduling with provable guarantees. • Simultaneous approximation for each objective • Approximated solutions of Pareto optimal solutions: • Makespan/Reliability[SPAA07] - Makespan/Memory [IPDPS08] • Generic -Relaxation scheme[Shmoys&al.]: • Makespan/Minsum [WEA05]; To include a smart algorithm inside a recursive doubling (eg. for Makespan) (eg. for Minsum) For moldable tasks: yields a bi-approximation with arbitrary ratio between Cmax and Minsum[WEA05] t 16 0 2 4 8

  10. 2. Interfaces for coordination Objective: provably efficient control at runtime of the coupling of components with various synchronizations constraints. • Kaapi middleware • Provable performances: • Efficient local serialization “work-first principle”, zero-copy [J. CLSS’07, ICCS07]] • Scheduling: • coarse-grain graph partitioning + ``work-stealing’ • Fault-tolerance protocols, from scheduling properties • coordinated protocol[ICTTA’06] + original TIC protocol [EIT05, TDSC08] • Positioning: • Multi-processors/multi-core architectures: Intel TBB, Cilk++ • Grid / global platforms: Tolerate failure and falsification: Satin (FT) 1 struct sum { 2 void operator()(Shared_r < int > a, 3 Shared_r < int > b, 4 Shared_w < int > r ) 5 { r.write(a.read() + b.read()); } 6 } ; 7 8 struct fib { 9 void operator()(int n, Shared_w<int> r) 10 { if (n <2) r.write( n ); 11 else 12 { int r1, r2; 13 Fork< fib >() ( n-1, r1 ) ; 14 Fork< fib >() ( n-2, r2 ) ; 15 Fork< sum >() ( r1, r2, r ) ; 16 } 17 } 18 } ; Local stack runtime Distributed nestedmacrodataflow graph

  11. 2. Kaapi: Support and transfert • Quadratic assignment [ANR CHOC] • Finite element computations [ANR DISCOGRID] • Cryptographic S-Box selection [ANR SAFESCALE] • Probabilistic inference engine [ProBayes] • Distributed implementation of CAPE-Open standardfor process engineering computations [IFP] Cluster implementation of compliant runtime RSI/Indiss-RT

  12. communication Overheads redundancy synchronization 3. Adaptive algorithms Objective: To design and analyze algorithms that may obliviously adapt their execution under the control of the scheduling … Sequential algorithm Parallel algo 1 P=2 Parallel algo 2 P=100 Parallel algo k P=+∞ Which one to select?

  13. 3. Adaptive algorithms • Heterogeneous resources, variable speeds:work-stealing to obliviouslyself-tune granularity • But: workWp increases when depthDpdecreases : • multi-objective problem • Adaptive recursive coupling of algorithms [Europar’06, PASCO’07, PDP’08] • Relaxation: sequential / parallel work-stealing • Minimize both the work Wpand the depthDp

  14. Bridge = AWS scheduling Film-grain application (streaming) Ex: HD-TV Noise reduction 3. Adaptive algorithms • Cache&processor oblivious stream computations [ PDP’07] • AWS: adaptive work-stealing for MPSoCs • Use case: HDTV on MPSoCs [ST Microelectronics film grain tech.] MPSoC Application description potential parallelism [AWS api] Architecture description [SPIRIT / IP-XACT Simulator] • Near optimal experimental results [PDP08]

  15. Time (ms) 3. Adaptive algorithms Adaptive 3D-vision [VR’07] Realtime constraint : 30 frames per sec Adaptive heterogeneous coupling with Kaapi: CPU+GPU [EGPV’07] Maximumprecision Level of details 1 .. 16 CPUs

  16. 4. Interactivity • Motivation: parallelism for interactive applications • Challenging application: multi-cameras, multi-cpus, multi-GPUs, multi-display • Grimage platform [2004…] • Positioning: other platforms: • [Blue-C, ETH Zurich, 2005 …],[Tele-Immersion@UCBerkeley 2005] • Specificity: collaboration with • EVASION(realtime physics simulation) • PERCEPTION (computer vision) • - 30 nodes cluster • - 15 cameras • - 16 projectors

  17. 4. Interactivity Middleware dedicated to interactive applications • Distributed components, moldable • Parallel code coupling • Static coarse grain mapping HDTV player on 12 Mpixels display wall (16 projectors) [CPUs + GPUs]

  18. MPSoC Grids Cluster SMP multi-core GPU Summary of 2005-2007 Multi-objective Adaptive Performance • Applications are time-consuming but essential to validate scientific approach AWS

  19. Some facts • Publications • Contracts • Softwares: • Kaapi, FlowVR, Taktuk, AWS • 127 in 3 years , 19 rank #1 - 17 Int. Journal (SIAM J.Comp, IEEE TC, TPDS, TDSC, EJOR, FCS, …) • - 59 Int. Conf (SPAA, IPDPS, CCGrid VR, VIS, Europar, ICCS, Siggraph…) • Industry partners: STM, IFP, CEA, Bull, C-S, DCN, • 2 ARC, 5 ANRs • 1 pole MINALOGIC • 2 Europe, 1 Ass. team K€

  20. Highlights • 1st prize Plugtest Nov. 2007 Nqueens challenge • SIGGRAPH Aug. 2007 Emerging Technologies Demo • Valorization: start-up (Sep. 2007) • co-founded by former PhD C. Menier [joined MOAIS / PERCEPTION] • transfer: parallel 3D modeling • Dec. 2006: special Jury prize • Nov. 2007: 1st prize • Nqueens(23) in 2107s with 3654 cores ~4000 visitors

  21. Research directions 2008-2012[1/3] To push the interactions to large scale • Heterogeneous computing • Complex memory hierarchy • Provable performances vs adversary • Game theory

  22. Research directions 2008-2012[2/3] • Scheduling : multi-objective • Large systems, many users, various objectives : equity / fairness • Extra global objective to non-cooperative strategies • Coordination interface => Runtime for HIPC on demand • Work-stealing based runtime extended to complex memory hierarchy • Dependable computing on global computing platforms

  23. Research directions [3/3] • Adaptive algorithms • Large data sets, out-of-core issues • Framework / high level library • High performance interactive computing • Interactive resolution of complex problem (scheduling) • Grimage: explore new 3D interactions [PERCEPTION] • Parallelism for adaptive interactive performance • [EVASION, ALCOVE] Kaapi partitioning+work-stealing to balance load between heterogeneous resources (CPUs / GPUs )

  24. Summary “To provide parallel programming schemes, interfaces and tools for high performance interactive computing that enable to achieve provable performances on distributed parallel architectures, from multi-processors system-on-chip to lightweight grids and global computing platforms.” SIGGRAPH’07 [MOAIS - PERCEPTION - EVASION]

  25. Former members • 13 PhDs defended in 2005-2007 • Now: 2 at INRIA : Alcove, Cepage 7 in university : Reims, IKI Iran, Luxembourg, Vannes, Damascus, Warsaw, Colima 1 in Postdoc : Iowa SU 1 Start-up co-founder : 4DViews 2 in industry : IFP, Amadeus • 2 postdocs • Now: Univ. Paris 6, Petrobraz • 1 long term visit • Axel Krings, Idaho State Univ • 3 engineers • Now: INRIA/PARIS, industry

More Related