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Grid Computing in Multidisciplinary CFD
optimization problems
The challenge of Multi-physics Industrial
Applications
Parallel CFD Conference, Moscow (RU)
Toan NGUYEN
Project OPALE
May 13-15th, 2003
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OUTLINE
INRIA
STATE OF THE ART
PARALLEL CFD OPTIMIZATION
MULTIDISCIPLINARY APPLICATIONS
CURRENT ISSUES
FUTURE TRENDS CONCLUSION
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PART 1
http//www.inria.fr
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INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE
ET AUTOMATIQUE

National Research Institute for Computer Science
and Automatic Control

• Created 1967
• French Scientific and Technological Public
  Institute
• Ministry of Research and Ministry of Industry

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INRIA MISSIONS

• Fundamental and applied research
• Design experimental systems
• Technology transfer to industry
• Knowledge transfer to academia
• International scientific collaborations
• Contribute to international programs
• Technological assessment
• Contribute to standards organizations

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2.500 in six Research Centers
PERSONNEL

• 900 permanent staff
• 400 researchers
• 500 engineers, technicians and administrative
  pers.
• 500 researchers from other organizations
• 600 trainees, PhD and post-doctoral students
• 100 external collaborators
• 400 visiting researchers from abroad

Rocquencourt
Lorraine
Rennes
Rhône-Alpes
Futurs
Sophia Antipolis
Budget 120 MEuros (tax not incl.) 25
self-funding through 600 contracts
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CHALLENGES

• Expertise to program, compute and communicate
  using the Internet and heterogeneous networks
• Design new applications using the Web and
  multimedia databases
• Expertise to develop robust software
• Design and master automatic control for complex
  systems
• Combine simulation and virtual reality

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APPLICATIONS

• Telecommunications and multimedia
• Healthcare and biology
• Engineering
• Transportation
• Environment

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RESEARCH PROJECTS

• Teams of approx. 20 researchers
• Medium-term objectives and work program (4 years)
• Scientific and financial independence
• Links and partnerships with scientific and
  industrial partners on national and international
  basis
• Regular assessment of results during given
  time-scale

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PROJECTS

• 99 Projects in four themes
• 1 . Networks and Systems
• 2 . Software Engineering and Symbolic Computing
• 3 . Human-Computer Interaction, Image
  Processing, Data Management, Knowledge
  Systems
• 4 . Simulation and Optimization of Complex
  Systems

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INTERNATIONAL COOPERATION

• Develop collaborations with European research
  centres and industries strengthen the European
  scientific community in Information
  Communication Technologies
• Increase international collaborations and enhance
  exchanges
• Cooperations with the United States, Japan,
  Russia
• Relations with China, India, Brazil, etc.
• Partnerships with developing countries
• World Wide Web Consortium (W3C)
• Work with the best industrial partners worldwide

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OPALE
INRIA project (January 2002)
Follow-up SINUS project
Located Sophia-Antipolis Grenoble
Areas
NUMERIC OPTIMISATION (genetic, hybrid, )
MODEL REDUCTION (hierarchic, multi-grids, )
INTEGRATION PLATFORMS
Coupling, distribution, parallelism, grids,
clusters, ...
APPLICATIONS aerospace, electromagnetics,
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PART 2
STATE OF THE ART
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GRID COMPUTING
THE GRIDBUS PROJECT
(Univ. Melbourne, Australia)
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GRID COMPUTING
RESOURCE MANAGEMENT
INFORMATION SERVICES
DATA MANAGEMENT
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APPLICATIONS
National Partnership for Advanced Computational
Infrastructure
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GRID COMPUTING
HIGH PERFORMANCE COMPUTING
HIGH THROUGHPUT COMPUTING
PETA-DATA MANAGEMENT
LONG DURATION APPLICATIONS
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GRID COMPUTING
HIGH-PERFORMANCE PROBLEM SOLVING ENVIRONMENTS
BUSINESS TO BUSINESS E-COMMERCE
LARGE SCALE SCIENTIFIC APPLICATIONS
ENGINEERING, BIO-SCIENCES, EARTH CLIMATE
MODEL.
AFFORDABLE HIGH-PERFORMANCE COMPUTING
IRREGULAR AND DYNAMIC BEHAVIOR APPLICATIONS
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GRID COMPUTING
OPTIMALGRID PROJECT
(IBM Almaden Resarch Center)
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GRID COMPUTING
PERFORMANCE DIRECTED MANAGEMENT
DISCOVERY, SHARING, COORDINATED USE, MONITORING
DISTRIBUTED HETERO. DYNAMIC RESOURCES SERVICES
PERFORMANCE, SECURITY, SCALABILITY, ROBUSTNESS
DYNAMIC MONITORING
ADAPTIVE RESOURCE CONTROL
ERROR AMPLIFIER SYNDROM
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GRID COMPUTING
PLANNING ADAPTING DISTRIBUTED APPLICATIONS
LOCATION TRANSPARENCY, MULTIPLE PROTOCOL BINDINGS
CREATE COMPOSE DISTRIBUTED SYSTEMS
NEED ENQUIRY, REGISTRATION PROTOCOLS
GRID SERVICES (OGSA)
BROKERING, FAULT DETECTION TROUBLESHOOTING
COMPATIBLE UNDERLYING PLATFORMS
CACHING, MIGRATING, REPLICATING DATA
APPLICATIONS HIGH ENERGY PHYSICS (DATAGRID,
PPDG, GriPhyN)
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GRID COMPUTING
GRID Research, Integration, Deployment Support
center
NSF Middleware Initiative Globus, Condor-G,
NWS,
KX509, GSI-SSH, GPT
ISI, Univ. Chicago, NCSA, SDSC, Univ. Wisconsin
Madison
NSF, Dept Energy, DARPA, NASA
GOAL national middleware infrastructure to
permit seamless resource sharing across virtual
organizations
PHILOSOPHY the whole is greater than the sum
of its parts
APPLICATIONS NEES, GriPhyN, Intl Virtual Data
Grid Lab (ATLAS)
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GRID COMPUTING
Incentives
Incentives
SOFTWARE DEV. FREE OPEN SOURCE (Linux,
FreeBSD)
PARALLEL DISTRIBUTED PROGRAMMING
BEOWULF CLUSTERS
HIGH-SPEED GIGABITS/SEC NETWORKS
COMPONENT PROGRAMMING
DEVELOPMENT LARGE DISTRIBUTED DATA FILE SYTEMS
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BEOWULF CLUSTER
PC-cluster at INRIA Rhône-Alpes (216 Pentium III
procs.)
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GRIDS vs. CLUSTERS
Grid is a type of parallel and distributed
system that enables the sharing, selection, and
aggregation of resources distributed across
multiple administrative domains, based on their
(resources) availability, capability,
performance, cost and users' quality-of-service
requirements. If distributed resources happen
to be managed by a single, global centralised
scheduling system, then it is a cluster. In
cluster, all nodes work cooperatively with common
goal and objective as the resource allocation is
performed by a centralised, global resource
manager. In Grids, each node has its own
resource manager and allocation policy.
Rajkumar Buyya (Grid Infoware)
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DISTRIBUTION vs. PARALLELISM
PARALLELISM IS NOT DISTRIBUTION
YOU CAN RUN SEQUENTIALLY PARALLEL CODES
DISTRIBUTION SUPPORTS A LIMITED FORM PARALLELISM
YOU CAN DISTRIBUTE SEQUENTIAL CODES
YOU CAN RUN SEQUENTIAL CODES IN PARALLEL
PARALLELISM ALLOWS DISTRIBUTION
YOU CAN DISTRIBUTE PARALLEL CODES
GLOBUS WILL NOT PARALLELIZE YOUR CODE
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WHERE WE ARE TODAY
Bits and pieces
1980 one year CPU time
1992 one month   
1997 four days   
2002 one hour   
Moores law results
Earth Sim (Japan) 5.120 NEC procs
ASCI Q (LANL) 11.968 HP Alpha procs
ASCI White (LLNL) 8.192 IBM SP Power 3 procs
MCR Linux (LLNL) 2.304 Intel 2.4 GHz Xeon
procs
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DISTRIBUTED SIMULATION PLATFORM
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

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INTEGRATION PLATFORMS
What they are...
COMMON DEFINITION, CONFIGURATION, DEPLOYMENT,
EXECUTION MONITORING
ENVIRONMENT
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, ...
TARGET APPLICATIONS multidiscipline
engineering, ...
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DISTRIBUTED OBJECTS ARCHITECTURE
SOFTWARE COMPONENTS
COMPONENTS ENCAPSULATE CODES
COMPONENTS ARE DISTRIBUTED OBJECTS
WRAPPERS AUTOMATICALLY (?) GENERATED
DISTRIBUTED PLUG PLAY
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CAST INTEGRATION PLATFORM
CAST
OPTIMIZERS
SOLVERS
Modules
Modules
Server
Wrapper
Wrapper
CORBA
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SOFTWARE COMPONENTS
BUSINESS COMPONENTS
LEGACY SOFTWARE
OBJECT-ORIENTED COMPONENTS
C, PACKAGES, ...
DISTRIBUTED OBJECTS COMPONENTS
Java RMI, EJB, CCM, ...
CASUAL METACOMPUTING COMPONENTS ?
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DISTRIBUTED OBJECTS ARCHITECTURE
SOFTWARE CONNECTORS
COMPONENTS COMMUNICATE THROUGH SOFTWARE
CONNECTORS
CONNECTORS ARE SYNCHRONISATION CHANNELS
SEVERAL PROTOCOLS
- SYNCHRONOUS METHOD INVOCATION
- ASYNCHRONOUS EVENT BROADCAST
CONNECTORS DATA COMMUNICATION CHANNELS
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PARALLEL APPLICATIONS
The good news.
PARALLEL and/or DISTRIBUTED HARDWARE
// SOFTWARE LIBRARIES MPI, PVM, SciLab //, ...
NEW APPLICATION METHODOLOGIES
DOMAIN DECOMPOSITION
GENETIC ALGORITHMS
GAME THEORY
HIERARCHIC MULTI-GRIDS
NESTING SEVERAL DEGREES PARALLELISM
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NESTING PARALLELISM
LEVERAGE OPTIMISATION STRATEGIES
COMBINE SEVERAL APPROACHES
DOMAIN DECOMPOSITION
GENETIC ALGORITHMS

// SOFTWARE LIBRARIES MPI, ...
GRIDS PC-CLUSTERS
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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 GRIDS and METACOMPUTING
GLOBUS, LEGION
CONDOR, NETSOLVE
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CLUSTER COMPUTING
PC-cluster at INRIA Rhône-Alpes
(216 Pentium III 200 Itanium procs. Linux)
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PART 3
PARALLEL CFD OPTIMIZATION
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CAST INTEGRATION PLATFORM
COLLABORATIVE APPLICATIONS SPECIFICATION TOOL
GOALS
DECISION CORBA INTEGRATION PLATFORM
COLLABORATIVE MULTI-DISCIPLINE OPTIMISATION
DESIGN FUTURE HPCN OPTIMISATION PLATFORMS
TESTBED
GENETIC PARALLEL OPTIMISATION ALGORITHMS
CODE COUPLING FOR CFD, CSM SOLVERS OPTIMISERS
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The front stage.
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PROCESS ALGEBRA
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TEST CASE
SHOCK-WAVE INDUCED DRAG REDUCTION
WING PROFILE OPTIMISATION (RAE2822)
Euler eqns (Mach 0.84, aoa 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,
Linux)
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TEST CASE
WING PROFILE OPTIMISATION
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CAST DISTRIBUTED INTEGRATION PLATFORM
RENNES
n CFD solvers
PC cluster
GA optimiser
VTHD Gbits/s network
CAST
PC cluster
PC cluster
software
NICE
GRENOBLE
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APPLICATION EXAMPLE
MULTI-ELEMENT WING PROFILE OPTIMISATION
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APPLICATION EXAMPLE
WING GEOMETRY
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APPLICATION EXAMPLE
OPTIMISATION STRATEGY
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APPLICATION EXAMPLE
PERFORMANCE DATA
Cas de test Nproc CPU (seconde) Accélération (T1/Ti)
1 1 5722 1
2 2 2583 2.01
3 5 1189 4.81
4 10 662 8.64
5 20 420 13.62
6 50 348 16.44
7 90 345 16.59
8 150 364 15.72
1h 35 mn
6 mn
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APPLICATION EXAMPLE
PERFORMANCE DATA
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APPLICATION EXAMPLE
PERFORMANCE DATA
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The results...
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CAST INTEGRATION PLATFORM
Behind the stage, again...
GRID 3 PC-CLUSTERS
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EMBEDDED PARALLELISM
Parallelized with MPI on 4 processors
CORBA server implemented in C
CORBA client implemented in C
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APPLICATION EXAMPLE
PERFORMANCE DATA
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APPLICATION DEPLOYMENT
Curves quasi-parallels gt same speed up,
whatever the place.
Join an horizontal asymptote time 200 s
The game load balancing,...
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PART 4
MULTIDISCIPLINARY APPLICATIONS
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MULTIDISCIPLINARY APPLICATIONS
Electronics facilities
Multi-Physics , Numerical Analysis, Applied
mathematics, grid computing
Industrial multi physics test cases requirements
Communication System Integration Platform
Aerodynamics
Modeling Deterministic/Stochastic
Optimizers Validation methods
Aeroacoustics
Data Bases
Aeroelasticity
Database Graphic analysis tools Validation
guidelines
Thermal flows
Fluid atmospheric environment
Safety Medical application
Pollution reduction
Aeronautics
Propulsion
Noise reduction
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APPLICATIONS REQUIREMENTS
HIGH PERFORMANCE COMPUTING
BIOSCIENCES, ENGINEERING, ENVIRONMENTAL APPS,
HIGH THROUGHPUT COMPUTING
HIGH ENERGY PHYSICS
SATELLITE IMAGING
MULTI-LAYERED ARCHITECTURE
CERN LHC FACILITY
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APPLICATIONS REQUIREMENTS
SHOULD OR COULD A GRID EMULATE A MAINFRAME ?
HOW CAN COMPUTE MODELS BE ADAPTED TO MAKE BEST
USE OF GRIDS ?
WHERE DO GRIDS NOT MAKE SENSE ?
WHAT IS THE REAL COST OF OWNING A GRID ?
CAN UNUSED POWER OF DESKTOP BE HARNESSED ?
HOW TO USE GRIDS FOR HIGH I/O APPLICATIONS ?
HOW TO DESIGN GRIDS FOR HIGH AVAILABILITY ?
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DESIGN ALTERNATIVES
EXISTING PLATFORMS
Globus, Condor, NetSOLVE, Legion, .
EXISTING TOOLS
NWS, SUN GRID ENG.
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DESIGN ALTERNATIVES
PROBLEM ORIENTED ENVIRONMENTS
Optimize specific pbs solution ReMAP
(Madeleine, DIET, FAST)
APACHE (Athapascan, )
HARWARE SOFTWARE ORIENTED ENV.
System devlpt optimisation PARIS (PADICO,
PACO, DO)
OASIS (ProActive, )
APPLICATION ORIENTED
Ease of use OPALE (CAST),
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INTEGRATING MULTIDISCIPLINARY APPLICATIONS
INTEGRATION OF PARTNERS EXPERTISE TO
DEPLOY COLLABORATIVE APPLICATIONS
NETWORKED PC-CLUSTERS, COMPUTERS DATABASES
TO SUPPORT MULTIDISCIPLINARY CHALLENGES
HIGH-LEVEL PROCEDURES FOR CONCURRENT
ENGINEERING (CSCW, VIRTUAL ORGANIZATIONS
ENTERPRISES )
INCLUDE CAD/CAM, MULTI-PHYSICS SOLVERS
OPTIMIZERS
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SCALABILITY
Optimized
Initial profile
AIRFOIL OPTIMIZATION
ONERA M6 SUPERSONIC WING
AOA 3, MACH 1.8
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PLATFORM REQUIREMENTS

• NEED FOR VIRTUAL REALITY ENVIRONMENT ?
• NEED FOR CSCW PROCEDURES SUPPORT ?
• NEED FOR GRID COMPUTING ?
• NEED FOR DISTRIBUTED DATABASE TECHNOLOGY ?

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PERFORMANCE
AIRFOIL OPTIMIZATION
ONERA M6 SUPERSONIC WING
AOA 3, MACH 1.8
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MULTIPHYSICS APPLICATIONS

• New methods and tools ( validation and
  optimization ) for solving Multidisciplinary
  Industrial Challenges
• Multi Physics Validation expertise spread in
  Research and Industry
• Cross fertilize Modeling, Experimentation and
  Scientific disciplines
• Single expertize revisited in a
  multi-disciplinary context
  Complexity at interfaces
  validation of interfaces in multi physics,
  multi-scale and multi-modeling to provide a
  unified view of experiments and numerics

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ROBUSTNESS
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MULTIPHYSICS APPLICATIONS
Multidisciplinary/Multicriteria Optimization
expertise spread in Research and Industry
Complexity of search spaces robustness and
efficiency of hybridized deterministic/adaptive
optimization methods - deterministic and
global optimizers - evolutionary
optimizers - hierarchy, game strategies and
decision methods Complexity at
interfacesCAD/CAM and Parameterization/Optimizati
on
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NEW CHALLENGES
MULTIDISCIPLINARY DESIGN
HIGH-LIFT DEVICES 1 CRITERION / 1 DISCIPLINE
(3D Navier-Stokes) MAXIMIZE LIFT
DRAG-BUFFETING 2 CRITERIA / 1 DISCIPLINE (3D
Navier-Stokes) MINIMIZE CRUISE DRAG MAXIMIZE
Cz BUFFET
AERO-ACOUSTICS HIGH-LIFT DEVICES 2
CRITERIA/ 2 DISCIPLINES (3D Navier-Stokes)
NOISE REDUCTION OF MULTI-ELEMENTS AIRFOILS DURING
TAKE-OFF
SUPERSONIC REGIME BANG 2 CRITERIA/ 2
DISCIPLINES (3D Navier-Stokes)
SUPERSONIC REGIME NOISE REDUCTION 2
CRITERIA/ 2 DISCIPLINES (3D Navier-Stokes)
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THE PLATFORM
Communication System Web-based system
Local Solvers Deterministic/Stochastic
Optimizers Validation codes
Distributed Data Bases
GOVERMENTAL INSTITUTIONS Generic multiphysics
test cases PC clusters
Local Databases Graphic analysis tools Validation
guidelines
Computing System Grid computing
environment Concurrent engineering platform
INDUSTRIES
RESEARCH CENTRES AND UNIVERSITIES
Industrial multi physics test cases High
performance computers
Multi-Physics optimisation PC clusters
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THE PLATFORM

• COMMUNICATION SYSTEM
• Supports interactions among partners and
  collaborative applications
• A DISTRIBUTED DATA MGT SYSTEM
• Supports remote partners data and test-cases
• A COMPUTING SYSTEM
• Supports partners grid-computing resources
  (PC-clusters, files, )

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PART 5
CURRENT ISSUES
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ONGOING EFFORTS
MULTIDISCIPLINE MODELLING
AERO-STRUCTURE, AERO-ACOUSTICS tight coupling
MULTIDISCIPLINE OPTIMIZATION
COMBUSTION, POLLUTION, NOISE REDUCTION
DISTRIBUTED APPLICATIONS SCHEDULING
loose coupling
APPLICATIONS CHARACTERIZATION
I/O PATTERNS, REAL-TIME ADAPTIVE RESOURCE CONTROL
DYNAMIC MONITORING
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ONGOING EFFORTS
COLLABORATIVE PROJECTS

• Performance monitoring dynamic load balancing

• Virtual organisations

• Dynamic resource co-allocation, process data
  migration

• Integrating applications with grid computing
  technology

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VIRTUAL ORGANISATIONS
DYNAMIC COLLECTIONS USERS RESOURCES
MAY OVERLAP SPECIFIC VIEWS FEDERATED RESOURCES
MEMBERSHIP ACCESS PROTOCOLS
SCALABLE ROBUST ARCHITECTURE PROTOCOLS
AGGREGATIONS OF DISTRIBUTED RESOURCES (VIRTUE)
DISTRIBUTED ALLOCATION MANAGEMENT SCHEDULING
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VIRTUAL ORGANISATIONS
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VIRTUAL ORGANISATIONS
HIERARCHICAL, GLOBALLY UNIQUE NAMES
RESOURCE NAME PROVIDER SCOPE NAME
INFORMATION PROVIDER AGGREGATE DIRECTORIES
VO
GRIS GRID RESOURCE INFORMATION SERVICE
(GLOBUS)
UNRELIABLE FAILURE DETECTORS
DISK SPLITTING (PABLO, AUTOPILOT)
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INTEGRATION WITH GRIDS
VIRTUAL ORGANISATIONS VIRTUE (Dan Reed, UIUC)
GENERIC INFO. SERVICES FOR RESOURCE DISCOVERY
MONITOR EXISTENCE CHARACTERISTICS RESOURCES
SERVICES COMPUTATIONS MANAGEMENT
DISTRIBUTED APPLICATIONS STEERING (AUTOPILOT)
INTERACTIVE REAL-TIME (I/O ?) PERFORMANCE TUNING
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PERFORMANCE MONITORING
Sensor design
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LEGACY NEW APPS
Last but not least
How to integrate them in new PSE (Fortran, MPI
vs. C, Java, C) ?
Interface with PSE (Sockets, CORBA, RMI, EJB,
CCM, ) ?
Coupling with existing apps maths libraries
(user transparency) ?
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PART 6
FUTURE TRENDS
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TOMORROWS PSE
Behind the stage, again...
METACOMPUTING
POWER SUPPLY PARADIGM APPLIED TO
COMPUTING RESOURCES WORLDWIDE
 COTS  PROGRAMMING
COMPONENTS OFF THE SHELF
DYNAMIC LOAD BALANCING RESSOURCE ALLOC
MONITOR, START, SUSPEND, RESUME, STOP, MIGRATE
REMOTE PROCESSES DYNAMICALLY
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CONCLUSION
METACOMPUTING
LARGE SCALE MULTIDISCIPLINARY APPLICATIONS
COLLABORATIVE ENVIRONMENTS
 COTS  PROGRAMMING
FLEXIBLE INTEROPERABLE APPS DEVELOPMENT
VIRTUAL ORGANIZATIONS
REAL CSCW ON FULL SCALE PRODUCTION PROJECTS
FULL USER CONTROL
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CONCLUSION
LARGE DYNAMIC COLLABORATIVE ENVIRONMENTS
THE DIGITAL DYNAMIC AIRCRAFT
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REFERENCES
http//www.inrialpes.fr/opale
Toan.Nguyen_at_inrialpes.fr