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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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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:
  • Specificity: collaboration with
    • EVASION(realtime physics simulation)
    • PERCEPTION (computer vision)
  • - 30 nodes cluster
  • - 15 cameras
  • - 16 projectors
slide17

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-objective Adaptive 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, 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
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