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Conference on Applied Parallel Computing. Integration of Multidiscipline Applications in Grid-computing Environments. NGUYEN G.T., J. BLACHON, C. PLUMEJEAUD. « OPALE » PROJECT. PARA’02 , Espoo, June 16th, 2002. OPALE. • New INRIA project since January 1st, 2002. • Follow up SINUS project.

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Integration of multidiscipline applications in grid computing environments

Conference on Applied Parallel Computing

Integration of Multidiscipline Applications in Grid-computing Environments

NGUYENG.T., J. BLACHON, C. PLUMEJEAUD

« OPALE » PROJECT

PARA’02, Espoo, June 16th, 2002


Integration of multidiscipline applications in grid computing environments

OPALE

• New INRIA project since January 1st, 2002

• Follow up SINUS project

• Located Sophia-Antipolis & Grenoble

• Topics

NUMERICOPTIMISATION (genetic, hybrid, …)

MODELREDUCTION (hierarchic, multi-grids, …)

INTEGRATION PLATFORMS

Coupling, distribution, parallelism, grids, clusters, ...

APPLICATIONS : aerospace, electromagnetics, …


Integration of multidiscipline applications in grid computing environments

OVERVIEW

• USER POINT OF VIEW

• SOFTWARE ENGINEERING POINT OF VIEW

• APPLICATION DEVELOPER ’S POINT OF VIEW

• THEORETICAL POINT OF VIEW

• CONCLUSIONS


Integration of multidiscipline applications in grid computing environments

OVERVIEW

• USER POINT OF VIEW

• SOFTWARE ENGINEERING POINT OF VIEW

• DEVELOPER ’S POINT OF VIEW

• THEORETICAL POINT OF VIEW

• CONCLUSIONS


Integration of multidiscipline applications in grid computing environments

WHERE WE ARE TODAY

Bits and pieces….

• 1980 : one year CPU time

• 1992 : one month «  »

• 1997 : four days «  »

• 2002 : one hour «  »

• ASCI White (LLNL) : 8192 IBM SP procs

• ASCI Red (Sandia) : 9632 Intel procs

• ASCI Blue Mountain (LANL) : 6144 SGI procs


Integration of multidiscipline applications in grid computing environments

TEST CASE

WING PROFILE OPTIMISATION


Integration of multidiscipline applications in grid computing environments

TEST CASE

• SHOCK-WAVE INDUCED DRAG REDUCTION

• WING PROFILE OPTIMISATION (RAE2822)

• Euler eqns (0,84 Mach, i = 2°) + BCGA (100 gen.)

• 2D MESH : 14747 nodes, 29054 triangles

• 4.5 hours CPU time (SUN Micro SPARC 5, Solaris 2.5)

• 2.5 minutes CPU time (PC cluster 40 bi-procs PIII, Linux)


Integration of multidiscipline applications in grid computing environments

“CAST” INTEGRATION PLATFORM

COLLABORATIVE APPLICATIONS SPECIFICATION TOOL

GOALS

• “DECISION” CORBA INTEGRATION PLATFORM

COLLABORATIVE MULTI-DISCIPLINE OPTIMISATION

• DESIGN FUTURE HPCN OPTIMISATION PLATFORMS

• TEST CASES IMPLEMENTATION

GENETIC & PARALLEL OPTIMISATION ALGORITHMS

CODE COUPLING FOR CFD, CSM SOLVERS & OPTIMISERS


Integration of multidiscipline applications in grid computing environments

The front stage….


Integration of multidiscipline applications in grid computing environments

CAST DISTRIBUTED INTEGRATION PLATFORM

User interface


Integration of multidiscipline applications in grid computing environments

OVERVIEW

• USER POINT OF VIEW

• SOFTWARE ENGINEERING POINT OF VIEW

• DEVELOPER ’S POINT OF VIEW

• THEORETICAL POINT OF VIEW

• CONCLUSIONS


Integration of multidiscipline applications in grid computing environments

INTEGRATIONPLATFORMS

What they are...

• COMMON DEFINITION, CONFIGURATION, DEPLOYMENT,

EXECUTION & MONITORING ENVIRONMENTS

• COLLABORATIVE APPLICATIONS

Distributed tasks interacting dynamically in controlled and formally provable way

• CODE-COUPLING FOR HETEROGENEOUS SOFTWARE

• DISTRIBUTED : LAN, WAN, HSN...

• TARGET HARDWARE : NOW, COW, PC-clusters, grids, ...

• TARGET APPLICATIONS : multidiscipline engineering, ...


Distributed simulation

DISTRIBUTED SIMULATION

What is required...

  • • MULTI-DISCIPLINE PROBLEM SOLVING ENVIRONMENTS

  • • HIGH-PERFORMANCE & TRANSPARENT DISTRIBUTION

  • • USING CURRENT COMMUNICATION STANDARDS

  • • USING CURRENT PROGRAMMING STANDARDS

  • • WEB LEVEL USER INTERFACES

  • • OPTIMIZED LOAD BALANCING & COMMUNICATION FLOW


Integration of multidiscipline applications in grid computing environments

DESIGN ALTERNATIVES

• PROBLEM REQUIREMENTS

Optimize specific pbs & solutions : ReMAP

• HARWARE & SOFTWARE ENVIRONMENTS

System evolution & development : PARIS

• EXISTING PLATFORMS

Globus, Condor, NetSOLVE, Legion, ….

• LEGACY APPLICATION SOFTWARE

How to integrate them into new PSE ?


Integration of multidiscipline applications in grid computing environments

INRIA PROJECTS ALTERNATIVES

• PROBLEM REQUIREMENTS

Optimize specific pbs : ReMAP, ATHAPASCAN

• HARWARE & SOFTWARE ENVIRONMENTS

System development : PARIS, OASIS

• OTHER EXISTING PLATFORMS

Globus, Condor, NetSOLVE, Legion, ….

• LEGACY & NEW APPLICATION SOFTWARE

How to integrate them into new PSE ?


Integration of multidiscipline applications in grid computing environments

ADVANCES IN SOFTWARE

• OBJECT-ORIENTED TECHNOLOGY

PROGRAMMING : C++, JAVA, C#, ...

APPLICATION MODELING : UML

• VISUAL PROGRAMMING

• COMPONENTS PROGRAMMING

REUSABILITY

MODULARITY

INTEROPERABILITY


Integration of multidiscipline applications in grid computing environments

DISTRIBUTED OBJECT ARCHITECTURE

• TRANSPARENT DISTRIBUTED OBJECT COMPUTING

• CORBA COMPLIANT

• SIMPLE SOFTWARE MODEL

- COMPONENTS

- CONNECTORS

• COMPONENTS PLUG-IN (e.g., optimizers, solvers)


Integration of multidiscipline applications in grid computing environments

DISTRIBUTED OBJECTS ARCHITECTURE

SOFTWARE COMPONENTS

• COMPONENTS ENCAPSULATE CODES

• COMPONENTS ARE DISTRIBUTED OBJECTS

• WRAPPERS AUTOMATICALLY (?) GENERATED

• DISTRIBUTED PLUG & PLAY


Integration of multidiscipline applications in grid computing environments

CAST PROTOTYPE

CAST

OPTIMIZERS

SOLVERS

Modules

Modules

Server

Wrapper

Wrapper

CORBA


Integration of multidiscipline applications in grid computing environments

SOFTWARE COMPONENTS

• BUSINESS COMPONENTS

LEGACY SOFTWARE

• OBJECT-ORIENTED COMPONENTS

C++, PACKAGES, ...

• DISTRIBUTED OBJECTS COMPONENTS

Java RMI, EJB, CCM, ...

• METACOMPUTING COMPONENTS ?


Integration of multidiscipline applications in grid computing environments

DISTRIBUTED OBJECT ARCHITECTURE

SOFTWARE CONNECTORS

• COMPONENTS COMMUNICATE THROUGH SOFTWARE

CONNECTORS

• CONNECTORS ARE SYNCHRONISATION CHANNELS

• SEVERAL PROTOCOLS

- SYNCHRONOUS METHOD INVOCATION

- ASYNCHRONOUS EVENT BROADCAST

• CONNECTORS = DATA COMMUNICATION CHANNELS


Integration of multidiscipline applications in grid computing environments

OVERVIEW

• USER POINT OF VIEW

• SOFTWARE ENGINEERING POINT OF VIEW

• APPLICATION DEVELOPER POINT OF VIEW

• THEORETICAL POINT OF VIEW

• CONCLUSIONS


Integration of multidiscipline applications in grid computing environments

PARALLEL APPLICATIONS

The good news….

• PARALLEL and/or DISTRIBUTED HARDWARE

• // SOFTWARE LIBRARIES : MPI, PVM, SciLab //, ...

• SUPPORT FOR NEW APPROACHES

DOMAIN DECOMPOSITION

GENETIC ALGORITHMS

GAME THEORY

HIERARCHIC MULTI-GRIDS

• SUPPORT SEVERAL DEGREES PARALLELISM


Integration of multidiscipline applications in grid computing environments

ADVANCES IN HARDWARE

The best news….

• HIGH-SPEED NETWORKS : ATM, FIBER OPTICS...

Gigabits/sec networks available (2.5, 10, …)

• PC & Multiprocs CLUSTERS : thousands GHz procs...

• Lays the ground for GRID and METACOMPUTING

GLOBUS, LEGION

CONDOR, NETSOLVE


Integration of multidiscipline applications in grid computing environments

CLUSTER COMPUTING

PC-cluster at INRIA Rhône-Alpes (216 Linux Pentium III procs.)


Integration of multidiscipline applications in grid computing environments

CLUSTER COMPUTING

PC-cluster at INRIA Rhône-Alpes (216 Pentium III procs.)


Integration of multidiscipline applications in grid computing environments

AIRFOIL OPTIMISATION


Integration of multidiscipline applications in grid computing environments

AIRFOIL OPTIMISATION


Integration of multidiscipline applications in grid computing environments

The results...


Integration of multidiscipline applications in grid computing environments

CLUSTER COMPUTING

PC-cluster at INRIA Rhône-Alpes

Multi-airfoil optimization : game theory + multi-grids hierarchic algo.


Integration of multidiscipline applications in grid computing environments

CAST DISTRIBUTED INTEGRATION PLATFORM

GRID computing...

RENNES

n CFD solvers

PC cluster

VTHD Gbits/s network

GA optimiser

CAST

PC cluster

PC cluster

software

GRENOBLE

NICE

July 2001...


Integration of multidiscipline applications in grid computing environments

NSD

CORBA

CfdSolver

cfd1

IRD

Algogen.idl

Check for syntaxe of request

CAST

AlgoGen

i1,i2, i3, …, in

Event channell,

i1, i2, i3, ….

CfdSolver

cfd2

CAST DISTRIBUTED INTEGRATION PLATFORM

Behind the stage, again...


Integration of multidiscipline applications in grid computing environments

CfdSolver

Cfd1

i1

Genetic Algorithm

i1, i2 ,i3, …, in

Event channel,

i1, i2, i3, …, in

CfdSolver

Cfd2

i2

CfdSolver

Cfd3

i3

Processor

P0

Processor

P0

Processor

P0

Processor

P1

Processor

P1

Processor

P1

Processor

P2

Processor

P2

Processor

P2

Processor

P3

Processor

P3

Processor

P3

THREE LEVELS of PARALLELISM

Parallelized with MPI on p processors

CORBA server implemented in C++

CORBA client implemented in C++

Genetic algorithm based on selection, mutation, crossover


Integration of multidiscipline applications in grid computing environments

CAST DISTRIBUTED INTEGRATION PLATFORM

* Curves quasi-parallels

=> same speed up, whatever the place.

* Join an horizontal asymptote:

time = 200 s

The game : load balancing,...


Integration of multidiscipline applications in grid computing environments

OVERVIEW

• USER POINT OF VIEW

• SOFTWARE ENGINEERING POINT OF VIEW

• DEVELOPER ’S POINT OF VIEW

• THEORETICAL POINT OF VIEW

• CONCLUSIONS


Integration of multidiscipline applications in grid computing environments

PROCESS FORMULAE

MILNER ’S SCCS PROCESS ALGEBRA

END

InitB

InitH

BCGA

FUN

HYBRID


Integration of multidiscipline applications in grid computing environments

OPERATORS

• CHOICE

• SYNCHRONIZATION

• PARALLEL EXECUTION

• SERIAL EXECUTION

• ITERATIONS

• COMPLEX EXPRESSIONS : process formulae

IC simulation : several coupled models


Integration of multidiscipline applications in grid computing environments

STRONG POINTS

• FORMAL SPECIFICATION SYSTEM

Milner ’s SCCS algebra

• STRONG THEORETICAL FOUNDATIONS

Process algebra for asynchronous systems

• SPECIFICATION & VERIFICATION OF COMPLEX APPS

• EASY TO USE

Intuitive interface : simple component model

No theoretical background knowledge required

Transparent distribution using CORBA


Integration of multidiscipline applications in grid computing environments

OVERVIEW

• USER POINT OF VIEW

• SOFTWARE ENGINEERING POINT OF VIEW

• DEVELOPER ’S POINT OF VIEW

• THEORETICAL POINT OF VIEW

• CONCLUSIONS


Integration of multidiscipline applications in grid computing environments

TODAY ’S FUTURE

• MULTIDISCIPLINE SIMULATION

e.g., DIGITAL DYNAMIC AIRCRAFT

• DISTRIBUTED INTEGRATION PLATFORMS

CAST, JACO3, CCAT, ProACTIVE ...

• GRID COMPUTING

GLOBUS, LEGION, CONDOR, ...


Integration of multidiscipline applications in grid computing environments

TOMORROW’S FUTURE

Behind the stage, again...

• METACOMPUTING

POWER SUPPLY PARADIGM APPLIED TO

COMPUTING RESOURCES WORLDWIDE

• « COTS » PROGRAMMING

COMPONENTS OFF THE SHELF

• DYNAMIC LOAD BALANCING & RESSOURCE ALLOC

OBSERVE, START, SUSPEND, RESUME, STOP, MIGRATE

REMOTE PROCESSES DYNAMICALLY


Integration of multidiscipline applications in grid computing environments

CONCLUSION

• INTEGRATION PLATFORMS PROVIDE

DEFINE, CONFIGURE, DEPLOY, EXECUTE & MONITOR

COLLABORATIVE APPLICATIONS

• GRID COMPUTING

• FULLY CORBA COMPLIANT

• ALSO ALLOWS CORBA & non-CORBA COMPONENTS

• SMOOTH TRANSITION FROM EXISTING CODE-COUPLING

ENVIRONMENTS

• ALLOWS SEQUENTIAL & PARALLEL COMPONENTS


Integration of multidiscipline applications in grid computing environments

DOCUMENTATION

• http://www.inrialpes.fr/opale

• http://cast.sourceforge.net/manuel

[email protected]


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