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Dr. Frederica Darema

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Dynamic Data Driven Application Systems (DDDAS) A new paradigm for applications/simulations and measurement methodology (Symbiotic Measurement&Simulation Systems) – PowerPoint PPT presentation

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Title: Dr. Frederica Darema


1
Dynamic Data Driven Application
Systems (DDDAS) A new paradigm for
applications/simulations and measurement
methodology (Symbiotic MeasurementSimulation
Systems)
Dr. Frederica Darema Senior Science and
Technology Advisor Director, Next Generation
Software Program Director, Biological Information
technology Systems NSF
2
What is DDDAS
Simulations (Math.Modeling Phenomenology Observati
on Modeling Design)
Theory (First Principles)
Simulations (Math.Modeling Phenomenology)
Theory (First Principles)
Experiment Measurements Field-Data User
Experiment Measurements Field-Data User
Dynamic Feedback Control Loop
Challenges Application Simulations
Development Algorithms Computing Systems Support
3
Examples of Applications benefiting from the new
paradigm
  • Engineering (Design and Control)
  • aircraft design, oil exploration, semiconductor
    mfg, structural eng
  • computing systems hardware and software design
  • (performance engineering)
  • Crisis Management
  • transportation systems (planning, accident
    response)
  • weather, hurricanes/tornadoes, floods, fire
    propagation
  • Medical
  • customized surgery, radiation treatment, etc
  • BioMechanics /BioEngineering
  • Manufacturing/Business/Finance
  • Supply Chain (Production Planning and Control)
  • Financial Trading (Stock Mkt, Portfolio Analysis)
  • DDDAS has the potential to revolutionize
  • science, engineering, management systems

4
Outline
  • Background and New Directions
  • Examples of Dynamic Data-Driven Application
    Systems (DDDAS)
  • CurrentTechnology Trends
  • Applications, Platforms ( Grids)
  • Why now is the time for DDDAS
  • Enabling DDDAS - Challenges and Approaches
  • Systems Software
  • Performance Engineering/ Systematic methods for
    building sw/hw systems
  • Dynamic application composition and Run-Time
    support
  • DDDAS application efforts
  • Algorithms for DDDAS
  • Agency Efforts
  • Existing programs
  • Future Initiatives
  • Technology transfer to industry

5
NSF March 2000 Workshop on DDDAS(Co-Chairs
Craig Douglas, UKy Abhi Desmukh, UMass)Invited
Presentations
  • New Directions on Model-Based Data Assimilation
    (Chemical Appls)
  • Greg McRae, Professor, MIT
  • Coupled atmosphere-wildfire modeling
  • Janice Coen, Scientist, NCAR
  • Data/Analysis Challenges in the Electronic
    Commerce Environment
  • Howard Frank, Dean, Business School, UMD
  • Steered computing - A powerful new tool for
    molecular biology
  • Klaus Schulten, Professor, UIUC, Beckman
    Institute
  • Interactive Control of Large-Scale Simulations
  • Dick Ewing, Professor, Texas AM University
  • Interactive Simulation and Visualization in
    Medicine Applications to Cardiology,
    Neuroscience and Medical Imaging
  • Chris Johnson, Professor, University of Utah
  • Injecting Simulations into Real Life
  • Anita Jones, Professor, UVA
  • Workshop Report www.cise.nsf.gov/eia/dddas

6
Fire Model
  • Sensible and latent heat fluxes from ground and
    canopy fire -gt heat fluxes in the atmospheric
    model.
  • Fires heat fluxes are absorbed by air over a
    specified extinction depth.
  • 56 fuel mass -gt H20 vapor
  • 3 of sensible heat used to dry ground fuel.
  • Ground heat flux used to dry and ignite the
    canopy.

Kirk Complex Fire. U.S.F.S. photo
Slide Courtesy of Cohen/NCAR
7
Coupled atmospheric and wildfire models
Slide Courtesy of Cohen/NCAR
8
AMAT Centura Chemical Vapor Deposition Reactor
Operating Conditions Reactor Pressure 1 atm Inlet
Gas Temperature 698 K Surface Temperature 1173
K Inlet Gas-Phase Velocity 46.6 cm/sec
Slide Courtesy of McRae/MIT
9
Some Technology Challenges in Enabling DDDAS
  • Application development
  • interfaces of applications with measurement
    systems
  • dynamically select appropriate application
    components
  • ability to switch to different algorithms/componen
    ts depending on streamed data
  • Algorithms
  • tolerant to perturbations of dynamic input data
  • handling data uncertainties
  • Systems supporting such dynamic environments
  • dynamic execution support on heterogeneous
    environments
  • GRID Computing, and Beyond!!!

10
Why Now is the Time for DDDAS
  • Technological progress prompted advances in some
    of the challenges
  • Computing speeds advances (uniprocessor and
    multiprocessor systems), Grid Computing
  • Systems Software
  • Applications Advances (parallel grid computing)
  • Algorithms advances (parallel grid computing,
    numeric and non-numeric techniques)
  • Examples of efforts in
  • Systems Software
  • Applications
  • Algorithms

11
What is Grid Computing?
coordinated problem solving on dynamic and
heterogeneous resource assemblies
DATA ACQUISITION
ADVANCEDVISUALIZATION
,ANALYSIS
COMPUTATIONALRESOURCES
IMAGING INSTRUMENTS
LARGE-SCALE DATABASES
Example Telescience Grid, Courtesy of Ellisman
Berman /UCSDNPACI
12
Application Directions
Past
  • Monolithic
  • One programming language
  • Computation Intensive
  • Batch
  • Hours/Days
  • Computation Intensive
  • Data Intensive
  • Few Minutes/hours
  • Real Time Turn-around
  • Visualization (real time)
  • Interactive Steering by user
  • and ... DDDAS

Present / Future
  • Multi-Modular
  • Multi-Source Data
  • Multi-Language
  • Multiple Developers

Such characteristics require new capabilities in
systems software
13
Some Examples of Todays Applications
14
The e-Business / (CIM, CIE)
Distributor Channel
Order Processing Customer Service Sales
Management
Manufacturing Product DBs Inventory Shipping
Application Integration Interoperability
Process Coordination Management
Monitoring
Business to Business
Enterprise Messaging
Data Integration Interoperability
Mobile Workers Knowledge Workers Business
Communications
Business to Customer
Web e-commerce
15
Compare with Classical (Old) Supply Chain
Parts Supplier
Parts Supplier
Transportation Supplier
16
MSTAR (DARPA)(Moving and Stationary Target
Acquisition and Recognition)
Focus of Attention
Index Database (created off-line)
...
Search Tree
Regions of Interest (ROI)
Segmented Terrain Map
SAR Image Collateral Data - DTED, DFAD - Site
Models - EOSAT imagery
...
Indexing
Target Scene Model Database (created off line)
Task Predict
Task Extract
Extract
Search
Statistical Model
Predict
Clutter Database
CAD
Match Results
Tree Clutter
Semantic Tree
Form Associations
Refine Pose Score
Analyze Mismatch
Shadow Obscuration ?
x2,y2, ??
x1,y1, ??
Score 0.75
Ground Clutter
Feature-to-Model Traceback
Match
17
Platform Directions
Past
  • Vector Processors, SIMD MPPs
  • Distributed Memory MPs
  • Shared Memory MPs
  • Distributed Platforms,
    Heterogeneous Computers and Networks
  • Heterogeneity
  • architecture (compute network)
  • node power (supernodes, PCs)

Present/Future
  • Latencies
  • variable (internode, intranode)
  • Bandwidths
  • different for different links
  • different based on traffic

GiBs
Grids
Petaflops Platform (Grid-in-a-Box)
Distributed Platform
.
MPP
NOW
SP
18
TeraGrid 13.6 TF, 6.8 TB memory, 79 TB internal
disk, 576 network disk
ANL 1 TF .25 TB Memory 25 TB disk
Extreme Blk Diamond
Caltech 0.5 TF .4 TB Memory 86 TB disk
574p IA-32 Chiba City
256p HP X-Class
32
32
32
32
24
128p Origin
128p HP V2500
32
24
32
24
HR Display VR Facilities
92p IA-32
5
4
5
8
8
HPSS
HPSS
OC-48
NTON
OC-12
Calren
ESnet HSCC MREN/Abilene Starlight
Chicago LA DTF Core Switch/Routers Cisco 65xx
Catalyst Switch (256 Gb/s Crossbar)
Juniper M160
OC-48
OC-12 ATM
OC-12
GbE
NCSA 62 TF 4 TB Memory 240 TB disk
SDSC 4.1 TF 2 TB Memory 225 TB SAN
vBNS Abilene Calren ESnet
OC-12
OC-12
OC-12
OC-3
Myrinet
4
8
HPSS 300 TB
UniTree
2
Myrinet
4
10
1024p IA-32 320p IA-64
1176p IBM SP 1.7 TFLOPs Blue Horizon
14
Sun Server
15xxp Origin
4
16
2 x Sun E10K
Slide Courtesy of Berman/NPACI
19
Grids Form the Basis of a National Information
Infrastructure
August 9, 2001 NSF Awarded 53,000,000 to
SDSC/NPACI and NCSA/Alliance for TeraGrid
  • TeraGrid will provide in aggregate
  • 13.6 trillion calculations per second
  • Over 600 trillion bytes of immediately accessible
    data
  • 40 gigabit per second network speed
  • Provide a new paradigm for data-oriented
    computing
  • Critical for disaster response, genomics,
    environmental modeling, etc.

Slide Courtesy of Berman/NPACI
20
Examples Other Agencies Grid Efforts
  • DARPA
  • SF Express (Synthetic Forces Express)
  • LargeScale distributed, interactive, battle
    simulation
  • Simulation decomposed terrain contiguously among
    supercomputers
  • Simulation of 50,000 entities in 8/97, 100,000
    entries in 3/98
  • NSF and DoE
  • CACTUS/GriPhyN (ITR, NGS, SciDAC)
  • Toolkit for Large-Scale Relativity Simulations
  • Largest Simulations for Colliding Black Holes
  • International Team/Grid

NASAs Information Power Grid
21
Why Now is the Time for DDDAS ?
  • Technological progress prompted advances in some
    of the challenges
  • Computing speeds advances (uniprocessor and
    multiprocessor systems), Grid Computing
  • Applications Advances (parallel grid computing)
  • Algorithms advances (parallel grid computing,
    numeric and non-numeric techniques)
  • Examples of efforts in
  • Systems Software
  • Applications
  • Algorithms

22
Agency Efforts
  • NSF
  • NGS The Next Generation Software Program
  • develops systems software supporting dynamic
    resource execution
  • ITR Information Technology Research (NSF-wide)
  • has been used as an opportunity to support DDDAS
    related efforts
  • 46 DDDAS pre-proposals many meritorious
  • about 24 proposals 8 were awarded
  • more on this, next slide.
  • Gearing towards a DDDAS initiative
  • expect participation from all NSF Directorates
  • CISE, MPS, ENG, BIO, GEO, SBE, HER
  • DARPA, NASA, DoE
  • have related programs (NASA/IPG, DoE/SciDAC)
  • and interested in DDDAS

23
DDDAS proposals awarded in FY01 ITR Competition
  • Biegler Real-Time Optimization for Data
    Assimilation and Control of Large Scale Dynamic
    Simulations
  • Car Novel Scalable Simulation Techniques for
    Chemistry, Materials Science and Biology
  • Knight Data Driven design Optimization in
    Engineering Using Concurrent Integrated
    Experiment and Simulation
  • Lonsdale The Low Frequency Array (LOFAR) A
    Digital Radio Telescope
  • McLaughlin An Ensemble Approach for Data
    Assimilation in the Earth Sciences
  • Patrikalakis Poseidon Rapid Real-Time
    Interdisciplinary Ocean Forecasting Adaptive
    Sampling and Adaptive Modeling in a Distributed
    Environment
  • Pierrehumbert- Flexible Environments for
    Grand-Challenge Climate Simulation
  • Wheeler- Data Intense Challenge The Instrumented
    Oil Field of the Future

24
Enabling DDDAS
Dynamic Data-Driven Application
Systems -- Symbiotic MeasurementSimulation Syste
ms
New Systems Software Technology NGS Program
Dynamic Compilers Application Composition
Performance Engineering
25
Enabling DDDAS
Dynamic Data-Driven Application
Systems -- Symbiotic MeasurementSimulation Syste
ms
Dynamic Compilers Application Composition
Performance Engineering
26
The NGS Program develops Performance Engineering
Technology Performance Models Measurements
Distributed Applications
Collaboration
Visualization
Environments
Authenication
/
Scalable I/O
Data Management
Authorization
Archiving/Retrieval
Dependability
Performance Engineered Design
Technology
Services
Services
. . .
Other Services
Distributed Systems Management
Distributed, Heterogeneous, Dynamic, Adaptive
Computing Platforms and Networks
Components Technology
Device
CPU
Memory
. . .
Technology
Technology
Technology
27
Enables Analysis in Multiple views of the
system (The applications view)
Distributed Applications
. . .
Collaboration
Visualization
Environments
Authenication
/
Scalable I/O
Data Management
Authorization
IO / File
Archiving/Retrieval
Dependability
Models
Services
Services
. . .
Other Services
OS
Scheduler
Distributed Systems Management
Models
Architecture /
Distributed, Heterogeneous, Dynamic, Adaptive
Network
Computing Platforms and Networks
Models
Memory
Device
CPU
Memory
. . .
Models
Technology
Technology
Technology
28
Enabling DDDAS
Dynamic Data-Driven Application
Systems -- Symbiotic MeasurementSimulation Syste
ms
Dynamic Compilers Application Composition
Performance Engineering
29
Technology Gap Example case Distributed
Application
Dynamic Analysis Situation
  • Network of
  • Workstations
  • (NOW)
  • Symmetric
  • Multiprocessor
  • (SMP)

Launch Application(s)
  • Application cannot
  • be repartitioned dynamically
  • when problem size or
  • number of SMPs changes
  • Cluster of
  • SMPs

Distributed Computing Resources
Adaptable Systems Infrastructure
30
The NGS Program developsTechnology for integrated
feedback control Runtime Compiling System (RCS)
and Dynamic Application Composition
Application Model

Dynamic Analysis Situation
Distributed Programming Model
Application Program
Compiler Front-End
Application Intermediate Representation
Compiler Back-End
Launch Application (s)
Performance Measuremetns Models
Dynamically Link Execute
Application Components Frameworks
Distributed Computing Resources
Distributed Platform
Adaptable computing Systems Infrastructure
31
Example of NGS supported effort
  • The GrADS Project (Grid Application Development
    Software)
  • Design and development of a Grid program
    development and execution environment
  • Tight coupling between program preparation and
    program execution environment
  • Contract-based performance economy

Slide Courtesy of GRADS group
32
NGS fosters Employing Performance Engineering
Technology for Application Composition and
Run-Time Support on Dynamic, Heterogeneous
Computing Platforms so that the users Shouldnt
Have to be Heroes to Achieve Grid Program
Performance and... because heroism is not enough
33
Enabling DDDAS
Dynamic Data-Driven Application
Systems -- Symbiotic MeasurementSimulation Syste
ms
Dynamic Compilers Application Composition
Performance Engineering
34
Challenges
  • Application development
  • develop interfaces of applications with
    measurement systems
  • dynamically select appropriate application
    components
  • need to switch to different algorithms/components
    depending on streamed data
  • Algorithms
  • tolerant to perturbations of dynamic input data
  • handling data uncertainties
  • Systems supporting such dynamic environments
  • need Performance Engineering technology
  • Application Composition Frameworks
  • Dynamic Run-Time Support

35
Some more Challenges on Applications
Development Issues
  • Handling Data Streams in addition to Data Sets
  • Handling different data structures semantic
    information
  • Interfaces to Measurement Systems -
    Interactive Visualization and Steering
  • Standards for data exchange
  • Combining Local and Global Knowledge
  • Model Interactions
  • Application control of measurement systems
  • Dynamic Application Composition and Runtime
    support
  • Examples from ITR supported efforts

36
NSF ITR Project A Data Intense Challenge The
Instrumented Oilfield of the Future PI Prof.
Mary Wheeler, UT Austin Multi-Institutional/Multi-
Researcher Collaboration
Slide Courtesy of Wheeler/UTAustin
37
Highlights of Instrumented Oilfield Project
  • Motivation
  • Field instrumentation for information technology
    and computational science essential for
    monitoring and optimizing oil and gas
    production.
  • Integration yields

THE INSTRUMENTED OILFIELD
  • Field Technologies
  • Time-lapse surface and borehole seismic,
    permanent downhole sensors, intelligent well
    completions, fiber optics, and remote control
    operations

Slides Courtesy of Wheeler/UTAustin
38
Highlights of Instrumented Oilfield Proposal
  • IT Technologies
  • Data management, data visualization, parallel
    computing, and decision-making tools such as
    new wave propagation and multiphase,
    multi- component flow and transport computational
    portals, reservoir production

THE INSTRUMENTED OILFIELD
  • Major Outcome of Research
  • Computing portals which will enable reservoir
    simulation and geophysical calculations to
    interact dynamically with the data and with each
    other and which will provide a variety of visual
    and quantitative tools. Test data provided by
    oil and service companies

39
Economic Modeling and Well Management
Production Forecasting Well Management
Reservoir Performance
Simulation Models
Visualization
Data Analysis
Multiple Realizations
Field Measurements
Data Management and Manipulation
Reservoir Monitoring Field Implementation
Data Collections from Simulations and Field
Measurements
40
Highlights of Instrumented Oilfield Proposal
Simulation Framework
41
ITR Project
  • A Data Intense Challenge
  • The Instrumented Oilfield of the Future
  • Industrial Support (Data)
  • British Petroleum (BP)
  • Chevron
  • International Business Machines (IBM)
  • Landmark
  • Shell
  • Schlumberger

42
Poseidon
  • Rapid Real-Time
  • Interdisciplinary Ocean Forecasting
  • Adaptive Sampling and Adaptive Modeling
  • in a Distributed Environment
  • Nicholas M. Patrikalakis, Henrik Schmidt, MIT
  • Allan R. Robinson, James J. McCarthy, Harvard
  • http//czms.mit.edu/poseidon

43
Ocean Science Issues
  • Data driven simulations via data assimilation
  • Simulation driven adaptive sampling of the ocean
  • Interdisciplinary ocean science interactions of
    physical, biological, acoustical phenomena
  • Extend state-of-the-art via feedback from
    acoustics to physicalbiological oceanography
  • Application in fisheries management, but also in
    oil-slick containment

44
Interdisciplinary Ocean Science
45
System Architecture
46
Data Driven Design Optimization in
EngineeringUsing Concurrent Integrated
Experiment and SimulationDoyle Knight,
Rutgers-The State University of New JerseyKhaled
Rasheed, University of Georgia
  • Channel or enclosure with isolated heat sources
    (i.e., electronic components)
  • The maximum surface temperature must be
    maintained below a specified level by the flow of
    air or dielectric liquid
  • Control flow of air or liquid for optimal heat
    dissipation

47
AMAT Centura Chemical Vapor Deposition Reactor
Operating Conditions Reactor Pressure 1 atm Inlet
Gas Temperature 698 K Surface Temperature 1173
K Inlet Gas-Phase Velocity 46.6 cm/sec
Slide Courtesy of McRae/MIT
48
(No Transcript)
49
(No Transcript)
50
(No Transcript)
51
Some more onneed for New Algorithms
  • Data Driven Application/Algorithmic Components
  • e.g. need for adaptive re-meshing
  • Multiple Scales and Model Reduction
  • Uncertainties in Streamed Data
  • new algorithms for uncertainty propagation
  • decision-making metrics in presence of
    uncertainty
  • database design for representation of
    uncertainties
  • valuation of cost of safety factors - bounds of
    uncertainty
  • Optimization and Inverse Problem

52
Real Time Optimization for Data Assimilation and
Control In Large Scale Dynamic Simulations
(Bielak, Ghattas, et al
Parameter Estimation Seismic Inversion
Forward Problem
Forward Problem


Given
Given




soil material and earthquake
soil material and earthquake
source parameters, find
source parameters, find
earthquake ground motion.
earthquake ground motion.




Inverse Problem
Inverse Problem


Given
Given
earthquake observations,
earthquake observations,
estimate material and
estimate material and
source parameters.
source parameters.

Slides Courtesy of Ghattas/CMU
53
Parameter Estimation Source Localization for
Parameter Estimation Source Localization for
Atmospheric Release of Hazardous Materials
Atmospheric Release of Hazardous Materials




Atmospheric Release Advisory Capability (ARAC)
project at
Atmospheric Release Advisory Capability (ARAC)
project at
Lawrence Livermore National Lab (Gayle
Lawrence Livermore National Lab (Gayle
Sukiyama
Sukiyama
et al.)
et al.)
o
o
Atmospheric release of hazardous
nuclear/chemical/biological
Atmospheric release of hazardous
nuclear/chemical/biological
material
material
o
o
Collection of local meteorological data
Collection of local meteorological data
o
o
Transport/diffusion model predicts extent of
spread
Transport/diffusion model predicts extent of
spread
o
o
Real-time inversion problem source localization
from sensor data
Real-time inversion problem source localization
from sensor data
o
o
See http//www.
See http//www.
llnl
llnl
.
.
gov
gov
/
/
ees
ees
/NARAC/
/NARAC/
arac
arac
.html
.html
54
Forward vs. Inverse Problem
Forward vs. Inverse Problem


PDE model
PDE model


Forward problem
Forward problem
o
o
Given data
Given data
(e.g. material coefficients, domain and
(e.g. material coefficients, domain and
x
x
boundary sources, boundary and initial conditions,
boundary sources, boundary and initial conditions,
geometry), find state
geometry), find state
u
u


Inverse problem
Inverse problem
o
o
Given desired goal involving
Given desired goal involving
, find
, find
u
u
x
x
o
o
Arise in many DDDAS scenarios
Arise in many DDDAS scenarios


parameter estimation
parameter estimation


data assimilation
data assimilation


optimal control
optimal control
55
Summary Remarks


PDE-constrained optimization key enabling
technology for
PDE-constrained optimization key enabling
technology for
DDDAS problems
DDDAS problems
o
o
Data assimilation parameter estimation
Data assimilation parameter estimation
o
o
Optimal control
Optimal control


Fast algorithms can be designed for many problems
classes
Fast algorithms can be designed for many problems
classes
o
o
Turnaround time small constant multiple of PDE
solve
Turnaround time small constant multiple of PDE
solve


Numerous algorithmic challenges remain
Numerous algorithmic challenges remain
o
o
Memory vs. work tradeoff for time-dependent
Memory vs. work tradeoff for time-dependent
adjoint PDEs
adjoint PDEs
o
o
Non-smooth problems
Non-smooth problems
o
o
Inexact
Inexact
Jacobians
Jacobians
o
o
Approximation of 2
Approximation of 2
derivatives
derivatives
nd
nd
o
o
Robustness to noise
Robustness to noise
o
o
Ill-conditioning and ill-
Ill-conditioning and ill-
posedness
posedness
,
,
regularizaton
regularizaton
o
o
Real time aspects (bootstrapping, early
termination)
Real time aspects (bootstrapping, early
termination)
o
o
Pointwise
Pointwise
inequality constraints
inequality constraints
o
o
Physics-based globalizations
Physics-based globalizations
56
Modeling Uncertainty
Irreducible versus epistemic uncertainty
  • Stochastically-excited structures
  • Boundary conditions, geometry, properties
  • Sensitivity/failure analysis
  • Gaussian and non-Gaussian processes
  • Polynomial Chaos vs. Monte Carlo
  • Stochastic spectral/hp element methods

Because I had worked in the closest possible
ways with physicists and engineers, I knew that
our data can never be precise Norbert Wiener
Slides Courtesy of Karniadakis/Brown
57
Partially Correlated non-Uniform Random Inflow
  • Deterministic
  • Stochastic
  • Pressure

58
Non-uniform Gaussian Random BC
Umean along centerline
Vmean along centerline
59
Non-uniform Exponential Random BC
Umean along centerline
Vmean along centerline
60
Research Opportunities in Uncertainty
  • UUncertainty analysis is a fertile and much
    needed area for inter-disciplinary research
  • EEstimates of uncertainties in model inputs are
    desperately needed

Uncertainty ? Ignorance
61
Additional Considerations/Requirements on
Hardware and Software Systems
  • Extended Spectrum of platforms
  • Assemblies of Sensor Networks and Computational
    Grid platforms
  • Systems Architectures including Measurement
    Systems
  • Programming Environments
  • Application, System, and Resource Management
  • Models of the Computational Infrastructure
  • Security and Fault Tolerance
  • DDDAS will accentuate and create the need for
    advances in such areas

62
Programming Environments
  • Procedural - gt Model Based
  • Programming -gt Composition
  • Custom Structures -gt Customizable Structures
  • (patterns, templates)
  • Libraries -gt Frameworks -gt
  • Compositional Systems
  • (Knowledge Based Systems)
  • Application Composition Frameworks and
    Interoperability extended to include measurements
  • Data Models and Data Management
  • Extend the notion of Data Exchange Standards
    (Applications and Measurements)

63
Research and Technology Roadmap (emphasis on
multidisciplinary research)
Application Composition System


Distributed programming models
.

Application performance Interfaces
.
.
i

Compilers optimizing mappings on complex
systems
n
t
D
Providing

Application RunTime System
E
E
enhanced
g

Automatic selection of solution methods
.
.

Interfaces, data representation exchange
M
capabilities
.

Debugging tools
O
r
for
S
Applications
a
t
Measurement System

i
o
.

Application/system multi-resolution models
.

Modeling languages
.

Measurement and instrumentation
n
Y1
Y2
Y3
Y4 Y5
Exploratory
Development
Integration Demos
64
Agency Efforts
  • NSF
  • NGS, SES, ITR
  • ITR broad, NSF-wide
  • Gearing for DDDAS initiative
  • will provide a focus for new exciting work in
    applications, algorithms and systems areas
  • NSF report www.cise.nsf.gov/eia/dddas
  • Also DARPA, NASA, DoE interested

65
What about Industry DDDAS
  • Industry has history of
  • forging new research and technology directions
    and
  • adapting and productizing technology which has
    demonstrated promise
  • Need to strengthen the joint academe/industry
    research collaborations joint projects / early
    stages
  • Technology transfer
  • establish path for tech transfer from academic
    research to industry
  • joint projects, students, sabbaticals (academe
    lt----gt industry)
  • Initiatives from the Federal Agencies / PITAC
  • Cross-agency co-ordination
  • Effort analogous to VLSI, Networking, and
    Parallel and Scalable computing
  • Industry is interested in DDDAS

66
DDDAS has potential for significant impact to
science, engineering, and commercial world, akin
to the transformation effected since the 50s by
the advent of computers
http//www.cise.nsf.gov/eia/dddas
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