CyberinfrastructureFrom Dreams to Reality

Deborah L. CrawfordDeputy Assistant Director of NSF for

Computer & Information Science & Engineering

Workshop for eInfrastructuresRome, December 9, 2003

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Daniel E. Atkins, Chair, University of Michigan Kelvin K. Droegemeier, University of Oklahoma Stuart I. Feldman, IBM Hector Garcia-Molina, Stanford University Michael L. Klein, University of Pennsylvania David G. Messerschmitt, University of California at Berkeley Paul Messina, California Institute of Technology Jeremiah P. Ostriker, Princeton University Margaret H. Wright,New York University http://www.communitytechnology.org/nsf_ci_report/

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In summary then, the opportunity is here to create cyberinfrastructure that enables more ubiquitous, comprehensive knowledge environments that become functionally complete .. in terms of people, data, information, tools, and instruments and that include unprecedented capacity for computational, storage, and communication.

They can serve individuals, teams and organizations in ways that revolutionize what they do, how they do it, and who can participate.

- The Atkins Report

Setting the Stage

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Desired Characteristics

• Science- and engineering-driven• Enabling discovery, learning and innovation• Promising economies of scale and scope • Supporting data-, instrumentation-,

compute- and collaboration-intensive applications

• High-end to desktop• Heterogeneous• Interoperable-enabled by collection of

reusable, common building blocks

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Hardware

Integrated Cyberinfrastructure meeting the needs of a community of communities

CI Services & Middleware

Applications • Environmental Science• High Energy Physics• Proteomics/Genomics• Learning

Science Gateways

CI Commons

Distributed Resources

Training & Workforce Development

Discovery, Learning & Innovation

Science of CI

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Overarching Principles

• Enrich the portfolio• Demonstrate transformative power of CI across S&E enterprise• Empower range of CI users – current and emerging • System-wide evaluation and CI-enabling research informs progress

• Develop intellectual capital• Catalyze community development and support• Enable training and professional development • Broaden participation in the CI enterprise

• Enable integration and interoperability• Develop shared vision, integrating architectures, common investments• Promote collaboration, coordination and communication across fields• Share promising technologies, practices and lessons learned

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S&E Gateways

CI Commons

Core Activities

- Compute-centric- Information-intensive- Instrumentation-enabling- Interactive-intensive

Integrative CI“system of systems”

CI Planning - A Systems Approach

CI-enabling Research

Domain-specific strategic plans-Technology/human capital roadmaps-Gaps and barrier analyses (policy, funding, ..)

System-wide activities-Education, training-(Inter)national networks-Capacity computing-Science of CI

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Baselining NSF CI Investments

• Core (examples)• Protein Databank• Network for Earthquake Engineering Simulation• International Integrated Microdata Access System• Partnerships, Advanced Computational Infrastructure • Circumarctic Environmental Observatory Network• National Science Digital Library• Pacific Rim GRID Middleware

• Priority Areas (examples)• Geosciences Network • international Virtual Data Grid Laboratory • Grid Research and Applications Development

.. and others too numerous to mention (~$400M in FY’04)

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CI Building Blocks

Partnerships for Advanced Computational Infrastructure (PACI)

– Science Gateways (Alpha projects, Expeditions)

– Middleware Technologies (NPACKage, ATG, Access Grid in a Box, OSCAR )

– Computational Infrastructure

NSF Middleware Initiative (NMI)– Production software releases– GridsCenter Software Suite, etc.

Early Adopters– Grid Physics Network (GriPhyN),

international Virtual Data Grid Laboratory (iVDGL)

– National Virtual Observatory– Network for Earthquake

Engineering Simulation (NEES)– Bio-Informatics Research Network

(BIRN)

Extensible Terascale Facility (TERAGRID)

– Science Gateways (value-added of integrated system approach)

– Common Teragrid Software Stack (CTSS)

– Compute engines, Data, Instruments, Visualization

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Extensible Terascale Facility (TERAGRID) A CI Pathfinder

• Pathfinder Role– integrated with extant CI capabilities – clear value-added

•supporting a new class of S&E applications• Deploy a balanced, distributed system

– not a “distributed computer” but rather– a distributed “system” using Grid technologies

•computing and data management•visualization and scientific application analysis•remote instrumentation access

• Define an open and extensible infrastructure– an “enabling cyberinfrastructure” demonstration– extensible beyond original sites with additional funding

•NCSA, SDSC, ANL, Caltech and PSC•ORNL, TACC, Indiana University, Purdue University and Atlanta hub

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Resource Providers + 4 New Sites

NCSA: Compute IntensiveSDSC: Data Intensive PSC: Compute Intensive

IA64

IA64 Pwr4EV68

IA32

IA32

EV7

IA64

10 TF IA-64128 large memory nodes

230 TB Disk Storage3 PB Tape Storage

GPFS and data mining

4 TF IA-64DB2, Oracle Servers500 TB Disk Storage6 PB Tape Storage1.1 TF Power4

6 TF EV6871 TB Storage

0.3 TF EV7 shared-memory150 TB Storage Server

1.25 TF IA-6496 Viz nodes

20 TB Storage

0.4 TF IA-64IA32 Datawulf80 TB Storage

Extensible Backplane NetworkLA

HubChicago

Hub

IA32

Storage Server

Disk Storage

Cluster

Shared Memory

VisualizationCluster

LEGEND

30 Gb/s

IA64

30 Gb/s

30 Gb/s30 Gb/s

30 Gb/s

Sun

Sun

ANL: VisualizationCaltech: Data collection analysis

40 Gb/s

Backplane Router

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Common Teragrid Software Stack (CTSS)

• Linux Operating Environment• Basic and Core Globus Services

– GSI (Grid Security Infrastructure)

– GSI-enabled SSH and GSIFTP

– GRAM (Grid Resource Allocation & Management)

– GridFTP– Information Service– Distributed accounting– MPICH-G2– Science Portals

• Advanced and Data Services– Replica

Management Tools

– GRAM-2 (GRAM extensions)

– CAS (Community Authorization Service)

– Condor-G (as brokering “super scheduler”)

– SDSC SRB (Storage Resource Broker)

– APST user middleware, etc.

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TERAGRID as a Pathfinder

• Science Drivers - Gateways-On-demand computing-Remote visual steering-Data-intensive computing

• Systems Integrator/Manager-Common TERAGRID Software Stack-User training & services-TERAGRID Operations Center

• Resource Providers-Data resources, compute engines, viz, user services

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Focus on Policy and Social Dynamics

• Policy issues must be considered up front

• Social engineering will be at least as important as software engineering

• Well-defined interfaces will be critical for successful software development

• Application communities will need to participate from the beginning

Fran Berman, SDSC

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CI Building Blocks

Partnerships for Advanced Computational Infrastructure (PACI)

– Science Gateways (Alpha projects, Expeditions)

– Middleware Technologies (NPACKage, ATG, Access Grid in a Box, OSCAR )

– Computational Infrastructure

NSF Middleware Initiative (NMI)– Production software releases– GridsCenter Software Suite, etc.

Early Adopters– Grid Physics Network (GriPhyN),

international Virtual Data Grid Laboratory (iVDGL)

– National Virtual Observatory– Network for Earthquake

Engineering Simulation (NEES)– Bio-Informatics Research Network

(BIRN)

Extensible Terascale Facility (TERAGRID)

– Science Gateways (value-added of integrated system approach)

– Common Teragrid Software Stack (CTSS)

– Compute engines, Data, Instruments, Visualization

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CI Commons

Goals• Commercial-grade software – stable, well-supported and well-

documented• User surveys and focus groups inform priority-setting• Development of “Commons roadmap”

Unanswered questions• What role does industry play in development and support of

products• In what timeframe will software and services be available• How will customer satisfaction be assessed and by whom• What role do standards play – and does an effective standards

process exist today

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CI CommonsCommunity Development Approach

• End-user communities willing and able to modify code• Adds features, repairs defects, improves code• Customizes common building blocks for domain applications• Leads to higher quality code, enhances diversity• Natural way to set prioritiesRequires• Education, training in community development methodologies• Effective Commons governance plan• Strong, sustained interaction between Commons developers

and community code enhancers

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Challenging Context

• Cyberinfrastructure Ecology– Technological change more rapid than institutional change– Disruptive technology promises unforeseen opportunity

• Seamless Integration of New and Old– Balancing upgrades of existing and creation of new

resources– Legacy instruments, models, data, methodologies

• Broadening Participation• Community-Building• Requires Effective Migration Strategy

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Kelvin Droegemeier, Center for Analysis and Prediction of Storms (CAPS) University of Oklahoma

On-Demand: Severe Weather Forecasting

Several times a week, need multiple hours dedicated access to amulti-Teraflops system.

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On Demand: Brain Data Grid

DukeUCLA

Cal Tech

Stanford U. Of MN

HarvardNCRR Imaging

and Computing Resources UCSD

Cal-(IT)2SDSC

Deep Web

Surface Web

Cyberinfrastructure Linking Tele-instrumentation, Data Intensive Computing, and Multi-scale Brain Databases. Wireless “Pad”

Web Interface

Objective: Form a National Scale Testbed for Federating Large Databases Using NIH High Field NMR Centers

Mark Ellisman, Larry Smarr, UCSD

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• membrane potential (s)

• bath concentration (s) – Inside / Outside

• bath diffusion constant

• channel diffusion constant

• time step-size

• number of time steps

• channel diameter

• channel length

• force profile

• ion trajectory

• ion type

•temperature

• ionic strength

• protein dielectric

• water dielectric

• protein 3-d structure coordinates

• technical specifications *

• partial charges of titratable residues • pH of bath

• interaction potentials between titratable groups in protein

• temperature

• ionic strength

• protein dielectric

• water dielectric

• protein 3-d struct coord

• technical specifications *

Related by sampling method used for calculation of diffusion constant

in MD simulations

Hole Profile analysis

• protein 3-d structure coordinates

• one position in channel

• approximate channel direction

• technical specifications***

• protein/lipid 3-d struct coord and topology

• force field sets

• ion-water ratio

• ion type/initial positions

• simulation time step-size

• simulation methodology specifications **

Hole AnalysisHole Analysis

Electrostatics - IElectrostatics - I

Molecular DynamicsMolecular Dynamics

Ele

ctro

stat

ics

- II

Ele

ctro

stat

ics

- II

Bro

wni

an D

ynam

ics

Bro

wni

an D

ynam

ics

User

Web Portal

TeraGrid Resources

DataWorkflowManager

Globus Client

Molecular Biology Simulation

Eric Jakobsson, UIUC