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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

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Moais imag fr

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


Staff and skills

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


Evolution of parallel programming

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]


Moais objective

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]


Approach

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


Overview

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


Research directions and achievements for 2005 2007

Research directions and achievements for 2005-2007

  • Scheduling

  • Interfaces for coordination

  • Adaptive algorithms

  • Interactive applications


1 scheduling

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 == | | … |

.

.

.


1 scheduling1

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


2 interfaces for coordination

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


    2 kaapi support and transfert

    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


    3 adaptive algorithms

    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?


    3 adaptive algorithms1

    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


    3 adaptive algorithms2

    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]


    3 adaptive algorithms3

    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


    4 interactivity

    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 …],[[email protected] 2005]

  • Specificity: collaboration with

    • EVASION(realtime physics simulation)

    • PERCEPTION (computer vision)

    • - 30 nodes cluster

    • - 15 cameras

    • - 16 projectors


    Moais imag fr

    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]


    Summary of 2005 2007

    MPSoC

    Grids

    Cluster

    SMP

    multi-core

    GPU

    Summary of 2005-2007

    Multi-objectiveAdaptive Performance

    • Applications are time-consuming but essential to validate scientific approach

    AWS


    Some facts

    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€


    Highlights

    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


    Research directions 2008 2012 1 3

    Research directions 2008-2012[1/3]

    To push the interactions to large scale

    • Heterogeneous computing

      • Complex memory hierarchy

    • Provable performances vs adversary

      • Game theory


    Research directions 2008 2012 2 3

    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


    Research directions 3 3

    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 )


    Summary

    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]


    Former members

    Former members

    • 13 PhDs defended in 2005-2007

      • Now: 2 at INRIA : Alcove, Cepage 7 in university : Reims, IKI Iran, Luxembourg, Vannes, Damascus, Warsaw, Colima1 in Postdoc : Iowa SU 1 Start-up co-founder : 4DViews2 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


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