Computing and Data Grids

1
Computing and Data Grids for Science and
Engineering
William E. Johnston http//www-itg.lbl.gov/wej/
Computational Research Division,DOE Lawrence
Berkeley National Laboratory and NASA Advanced
Supercomputing (NAS) Division, NASA Ames Research
Center
www.ipg.nasa.gov
doesciencegrid.org
6/24/03
2
The Process of Large-Scale Science is Changing

• Large-scale science and engineering problems
  require collaborative use of many compute, data,
  and instrument resources all of which must be
  integrated with application components and data
  sets that are
• developed by independent teams of researchers
• or are obtained from multiple instruments
• at different geographic locations
• The evolution to this circumstance is what has
  driven my interest in high-speed distributed
  computing now Grids for 15 years.
• E.g., see 1, and below.

3
Complex Infrastructure is Needed forSupernova
Cosmology
4
The Complexity of a Complete Approach to
Climate Modeling Terrestrial Biogeoscience
Involves Many Interacting Processes and Data
Chemistry CO2, CH4, N2O ozone, aerosols
Climate Temperature, Precipitation, Radiation,
Humidity, Wind
Heat Moisture Momentum
CO2 CH4 N2O VOCs Dust
Minutes-To-Hours
Biogeophysics
Biogeochemistry
Carbon Assimilation
Aero- dynamics
Decomposition
Water
Energy
Mineralization
Microclimate Canopy Physiology
Phenology
Hydrology
Inter- cepted Water
Bud Break
Soil Water
Days-To-Weeks
Snow
Leaf Senescence
Evaporation Transpiration Snow Melt Infiltration R
unoff
Gross Primary Production Plant
Respiration Microbial Respiration Nutrient
Availability
Species Composition Ecosystem Structure Nutrient
Availability Water
Years-To-Centuries
Ecosystems Species Composition Ecosystem Structure
WatershedsSurface Water Subsurface
Water Geomorphology
Disturbance Fires Hurricanes Ice Storms Windthrows
Vegetation Dynamics
Hydrologic Cycle
(Courtesy Gordon Bonan, NCAR Ecological
Climatology Concepts and Applications. Cambridge
University Press, Cambridge, 2002.)
5
Cyberinfrastructure for Science

• Such complex and data intensive scenarios require
  sophisticated, integrated, and high performance
  infrastructure to provide the
• resource sharing and distributed data management,
• collaboration, and
• application frameworks
• that are needed to successfully manage and carry
  out the many operations needed to accomplish the
  science
• This infrastructure involves
• high-speed networks and services
• very high-speed computers and large-scale storage
• highly capable middleware, including support for
  distributed data management and collaboration

6
The Potential Impact of Grids

• A set of high-impact science applications in the
  areas of
• high energy physics
• climate
• chemical sciences
• magnetic fusion energy
• have been analyzed 2 to characterize their
  visions for the future process of science how
  must science be done in the future in order to
  make significant progress

7
The Potential Impact of Grids

• These case studies indicate that there is a great
  deal of commonality in the infrastructure that is
  required in every case to support those visions
  including a common set of Grid middleware
• Further, Grids are maturing to the point where it
  is providing useful infrastructure for solving
  the computing, collaboration, and data problems
  of science application communities (e.g. as
  illustrated by the case studies, below)

8
Grids Highly Capable Middleware

• Core Grid services / Open Grid Services
  Infrastructure
• Provide the consistent, secure, and uniform
  foundation for managing dynamic and
  administratively heterogeneous pools of compute,
  data, and instrument resources
• Higher level services / Open Grid Services
  Architecture
• Provide value-added, complex, and aggregated
  services to users and application frameworks
• E.g. information management Grid Data services
  that will provide a consistent and versatile view
  of data real and virtual of all descriptions

9
User Interfaces
Application Frameworks (e.g. XCAT, SciRun) and
Portal Toolkits (e.g. XPortlets)
Applications (Simulations, Data Analysis, etc.)
Higher-level Services / OGSA (Data Grid Services,
Workflow management, Visualization, Data
Publication/Subscription, Brokering, Job Mgmt,
Fault Mgmt, Grid System Admin., etc.)
Core Grid Services / OGSI Uniform access to
distributed resources
Systems management and access
Global EventServices, Auditing, Monitoring
Security Services
Communication Services
Grid Information Service
UniformComputingAccess
Authentication Authorization
Unix and OGSI hosting
Uniform Data Access
Co-Scheduling
Grid Managed Resources
Asia-Pacific
Europe
PNNL
ESNet
X.509 CA
scientific instruments
ESnet
LBNL
ANL
DOEScience Grid
Supernova Observatory
ORNL
Synchrotron Light Source
NERSCSupercomputing Large-Scale Storage
Funded by the U.S. Dept. of Energy, Office of
Science,Office of Advanced Scientific Computing
Research,Mathematical, Information, and
Computational Sciences Division
10
Grids Highly Capable Middleware

• Also .
• Knowledge management
• Services for unifying, classifying and reasoning
  about services, data, and information in the
  context of a human centric problem solving
  environment the Semantic Grid
• Critical for building problem solving
  environments that
• let users ask what if questions
• ease the construction of multidisciplinary
  systems by providing capabilities so that the
  user does not have to be an expert in all of the
  disciplines to build a multidisciplinary system

11
Grid Middleware

• Grids are also
• A worldwide collection of researchers and
  developers
• Several hundred people from the US, European, and
  SE Asian countries working on best practice and
  standards at the Global Grid Forum
  (www.gridforum.org)
• A major industry effort to combine Grid Services
  and Web Services (IBM, HP, Microsoft) (E.g. see
  3)
• Vendor support from dozens of IT companies

12
Web Services and Grids

• Web services provide for
• Describing services (programs) with sufficient
  information that they can be discovered and
  combined to make new applications (reusable
  components)
• Assembling groups of discovered services into
  useful problem solving systems
• Easy integration with scientific databases that
  use XML based metadata

13
Web Services and Grids

• So
• Web Services provide for defining, accessing, and
  managing services
• while
• Grids provide for accessing and managing
  dynamically constructed, distributed compute and
  data systems, and provide support for
  collaborations / Virtual Organizations

14
Combining Web Services and Grids

• Combining Grid and Web services will provide a
  dynamic and powerful computing and data system
  that is rich in descriptions, services, data, and
  computing capabilities
• This infrastructure will give us the basic tools
  to deal with complex, multi-disciplinary, data
  rich science models by providing
• for defining the interfaces and data in a
  standard way
• the infrastructure to interconnect those
  interfaces in a distributed computing environment

15
Combining Web Services and Grids

• This ability to utilize distributed services is
  important in science because highly specialized
  code and data is maintained by specialized
  research groups in their own environments, and it
  is neither practical nor desirable to bring all
  of these together on a single system
• The Terrestrial Biogeoscience climate system is
  an example where all of the components will
  probably never run on the same system there
  will be manysub-models and associated data that
  are built and maintained in specialized
  environments

16
Terrestrial Biogeoscience A Complete Approach
to Climate Modeling Involves Many Complex,
Interacting Processes and Data
Chemistry CO2, CH4, N2O ozone, aerosols
Climate Temperature, Precipitation, Radiation,
Humidity, Wind
Heat Moisture Momentum
CO2 CH4 N2O VOCs Dust
Minutes-To-Hours
Biogeophysics
Biogeochemistry
Carbon Assimilation
Aero- dynamics
Decomposition
Water
Energy
Mineralization
Microclimate Canopy Physiology
Phenology
Hydrology
Inter- cepted Water
Bud Break
Soil Water
Days-To-Weeks
Snow
Leaf Senescence
Evaporation Transpiration Snow Melt Infiltration R
unoff
Gross Primary Production Plant
Respiration Microbial Respiration Nutrient
Availability
Species Composition Ecosystem Structure Nutrient
Availability Water
Years-To-Centuries
Ecosystems Species Composition Ecosystem Structure
WatershedsSurface Water Subsurface
Water Geomorphology
Disturbance Fires Hurricanes Ice Storms Windthrows
Vegetation Dynamics
Hydrologic Cycle
(Courtesy Gordon Bonan, NCAR Ecological
Climatology Concepts and Applications. Cambridge
University Press, Cambridge, 2002.)
17
Combining Web Services and Grids

• The complexity of the modeling done in
  Terrestrial Biogeoscience is a touchstone for
  this stage of evolution of Grids and Web Services
  this is one of the problems to solve in order
  to provide a significant increase in capabilities
  for science
• Integrating Grids and Web Services is a major
  thrust at GGF e.g. in the OGSI and Open Grid
  Services Architecture Working Groups.Also see
  http//www.globus.org/ogsa/

18
The State of Grids

• Persistent infrastructure is being built - this
  is happening, e.g., in
• DOE Science Grid
• NASAs IPG
• International Earth Observing Satellite Committee
  (CEOS)
• EU Data Grid
• UK eScience Grid
• NSF TeraGrid
• NEESGrid (National Earthquake Engineering
  Simulation Grid)
• all of which are focused on large-scale science
  and engineering

19
The State of Grids Some Case Studies

• Further, Grids are becoming a critical element of
  many projects e.g.
• The High Energy Physics problem of managing and
  analyzing petabytes of data per year has driven
  the development of Grid Data Services
• The National Earthquake Engineering Simulation
  Grid has developed a highly application oriented
  approach to using Grids
• The Astronomy data federation problem has
  promoted work in Web Services based interfaces

20
High Energy Physics Data Management

• Petabytes of data per year must be distributed to
  hundreds of sites around the world for analysis
• This involves
• Reliable, wide-area, high-volume data management
• Global naming, replication, and caching of
  datasets
• Easily accessible pools of computing resources
• Grids have been adopted as the infrastructure for
  this HEP data problem

21
High Energy Physics Data Management CERN / LHC
Data One of Sciences most challenging data
management problems
100 MBytes/sec
event simulation
Online System
PByte/sec
Tier 0 1
eventreconstruction
human2m
HPSS
CERN LHC CMS detector 15m X 15m X 22m, 12,500
tons, 700M.
2.5 Gbits/sec
Tier 1
German Regional Center
French Regional Center
FermiLab, USA Regional Center
Italian Center
0.6-2.5 Gbps
analysis
Tier 2
0.6-2.5 Gbps
Tier 3
CERN/CMS data goes to 6-8 Tier 1 regional
centers, and from each of these to 6-10 Tier 2
centers. Physicists work on analysis channels
at 135 institutes. Each institute has 10
physicists working on one or more channels. 2000
physicists in 31 countries are involved in this
20-year experiment in which DOE is a major player.
Institute 0.25TIPS
Institute
Institute
Institute
100 - 1000 Mbits/sec
Physics data cache
Tier 4
Courtesy Harvey Newman, CalTech
Workstations
22
High Energy Physics Data Management

• Virtual data catalogues and on-demand data
  generation have turned out to be an essential
  aspect
• Some types of analysis are pre-defined and
  catalogued prior to generation - and then the
  data products are generated on demand when the
  virtual data catalogue is accessed
• Sometimes regenerating derived data is faster and
  easier than trying to store and/or retrieve that
  data from remote repositories
• For similar reasons this is also of great
  interest to the EOS (Earth Observing Satellite)
  community

23
US-CMS/LHC Grid Data Services TestbedInternation
al Virtual Data Grid Laboratory
metadatadescriptionof analyzeddata
Interactive User Tools
Data GenerationRequestExecution Management
Tools
Data Generation RequestPlanning Scheduling
Tools
Virtual Data Tools

• Metadata catalogues
• Virtual data catalogues

Security andPolicy
Other GridServices
ResourceManagement
Core Grid Services
Transforms
Distributed resources(code, storage,
CPUs,networks)
Raw datasource
24
CMS Event Simulation Productionusing GriPhyN
Data Grid Services

• Production Run on the Integration Testbed (400
  CPUs at 5 sites)
• Simulate 1.5 million full CMS events for physics
  studies
• 2 months continuous running across 5 testbed
  sites
• Managed by a single person at the US-CMS Tier
  1site
• Nearly 30 CPU years delivered 1.5 Million Events
  to CMS Physicists

25
Partnerships with the Japanese Science Community

• Comments of Paul Avery avery_at_phys.ufl.edu,
  director iVDGL
• iVDGL is specifically interested in partnering
  with the Japanese HEP community and hopefully the
  National Research Grid Initiative will opens
  doors for collaboration
• Science drivers are critical existing
  international HEP collaborations in Japan provide
  natural drivers
• Different Japanese groups could participate in
  existing or developing Grid applications oriented
  testbeds, such as the ones developed in iVDGL for
  the different HEP experiments
• These testbeds have been very important for
  debugging Grid software while serving as training
  grounds for existing participants and new groups,
  both at universities and national labs.
• Participation in and development of ultra-speed
  networking projects provides collaborative
  opportunities in a crucial related area. There
  are a number of new initiatives that are relevant
• Contact Harvey B Newman ltnewman_at_hep.caltech.edugt
  for a fuller description and resource materials.

26
National Earthquake Engineering Simulation Grid

• NEESgrid will link earthquake researchers across
  the U.S. with leading-edge computing resources
  and research equipment, allowing collaborative
  teams (including remote participants) to plan,
  perform, and publish their experiments
• Through the NEESgrid, researchers will
• perform tele-observation and tele-operation of
  experiments shake tables, reaction walls, etc.
  
• publish to, and make use of, a curated data
  repository using standardized markup
• access computational resources and open-source
  analytical tools
• access collaborative tools for experiment
  planning, execution, analysis, and publication

27
NEES Sites

• Large-Scale Laboratory Experimentation Systems
• University at Buffalo, State University of New
  York
• University of California at Berkeley
• University of Colorado, Boulder
• University of Minnesota-Twin Cities
• Lehigh University
• University of Illinois, Urbana-Champaign
• Field Experimentation and Monitoring
  Installations
• University of California, Los Angeles
• University of Texas at Austin
• Brigham Young University

• Shake Table Research Equipment
• University at Buffalo, State University of New
  York
• University of Nevada, Reno
• University of California, San Diego
• Centrifuge Research Equipment
• University of California, Davis
• Rensselaer Polytechnic Institute
• Tsunami Wave Basin
• Oregon State University, Corvallis, Oregon
• Large-Scale Lifeline Testing
• Cornell University

28
NEESgrid Earthquake Engineering Collaboratory
Instrumented Structures and Sites
Remote Users
Simulation Tools Repository
High-Performance Network(s)
Laboratory Equipment
Field Equipment
Curated Data Repository
Large-scale Computation
Global Connections
Remote Users (K-12 Faculty and Students)
Laboratory Equipment
29
NEESgrid Approach

• Package a set of application level services and
  the supporting Grid software in a singlepoint
  of presence (POP)
• Deploy the POP to a select set of earthquake
  engineering sites to provide the applications,
  data archiving, and Grid services
• Assist in developing common metadata so that the
  various instruments and simulations can work
  together
• Provide the required computing and data storage
  infrastructure

30
NEESgrid Multi-Site Online Simulation (MOST)

• A partnership between the NEESgrid team, UIUC and
  Colorado Equipment Sites to showcase NEESgrid
  capabilities
• A large-scale experiment conducted in multiple
  geographical locations which combines physical
  experiments with numerical simulation in an
  interchangeable manner
• The first integration of NEESgrid services with
  application software developed by Earthquake
  Engineers (UIUC, Colorado and USC) to support a
  real EE experiment
• See http//www.neesgrid.org/most/

31
NEESgrid Multi-Site Online Simulation (MOST)
UIUC Experimental Setup
U. Colorado Experimental Setup
32
Multi-Site, On-Line Simulation Test (MOST)
Colorado Experimental Model
UIUC Experimental Model
SIMULATION COORDINATOR

• UIUC MOST-SIM
• Dan Abrams
• Amr Elnashai
• Dan Kuchma
• Bill Spencer
• and others
• Colorado FHT
• Benson Shing
• and others

NCSA Computational Model
33
1994 Northridge Earthquake SimulationRequires a
Complex Mix of Data and Models
Pier 7
Pier 5
Pier 8
Pier 6
NEESgrid provides the common data formats,
uniform dataarchive interfaces, and
computational services needed to supportthis
multidisciplinary simulation
Amr Elnashai, UIUC
34
NEESgrid Architecture
Java Applet
Web Browser
User Interfaces
MultidisciplinarySimulations
Collaborations
Experiments
Curated Data Repository
Simulation Tools Repository
SIMULATION COORDINATOR
Data AcquisitionSystem
NEESpop
NEES Operations
E-Notebook Services
Metadata Services
CompreHensive collaborativE Framework (CHEF)
NEESgrid Monitoring
Video Services
GridFTP
NEESGrid StreamingData System
Accounts MyProxy
Grid Services
NEES distributed resources
Instrumented Structures and Sites
Large-scale Storage
Large-scale Computation
Laboratory Equipment
35
Partnerships with the Japanese Science Community

• Comments of Daniel Abrams ltd-abrams_at_uiuc.edugt,
  Professor of Civil Engineering, University of
  Illinois and NEESGrid project manager
• The Japanese earthquake research community has
  expressed interest in NEESgrid
• I am aware of some developmental efforts between
  one professor and another to explore feasibility
  of on-line pseudodynamic testing - Professor M.
  Watanabe at the University of Kyoto is running a
  test in his lab which is linked with another test
  running at KAIST (in Korea) with Professor Choi.
  They are relying on the internet for transmission
  of signals between their labs.
• International collaboration with the new shaking
  table at Miki is being encouraged and thus they
  are interested in plugging in to an international
  network.  There is interest in NEESgrid in
  installing a NEESpop there so that the utility
  could be evaluated, and connections made with the
  NEESGrid sites.
• We already have some connection to the Japanese
  earthquake center known as the Earthquake
  Disaster Mitigation Center.  We have an MOU with
  EDM and the Mid-America Earthquake Center in
  place.  I am working with their director, Hiro
  Kameda,  and looking into establishing a NEESGrid
  relationship.

36
The Changing Face of Observational Astronomy

• Large digital sky surveys are becoming the
  dominant source of data in astronomy gt 100 TB,
  growing rapidly
• Current examples SDSS, 2MASS, DPOSS, GSC,
  FIRST, NVSS, RASS, IRAS CMBR experiments
  Microlensing experiments NEAT, LONEOS, and other
  searches for Solar system objects
• Digital libraries ADS, astro-ph, NED, CDS, NSSDC
• Observatory archives HST, CXO, space and
  ground-based
• Future QUEST2, LSST, and other synoptic surveys
  GALEX, SIRTF, astrometric missions, GW detectors
• Data sets orders of magnitude larger, more
  complex, and more homogeneous than in the past

37
The Changing Face of Observational Astronomy

• Virtual Observatory Federation of N archives
• Possibilities for new discoveries grow as O(N2)
• Current sky surveys have proven this
• Very early discoveries from Sloan (SDSS),2
  micron (2MASS), Digital Palomar (DPOSS)
• see http//www.us-vo.org

38
Sky Survey Federation
39
Mining Data from Dozens of Instruments / Surveys
is Frequently a Critical Aspect of Doing Science

• The ability to federate survey data is enormously
  important
• Studying the Cosmic Microwave Background a key
  tool in studying the cosmology of the universe
  requires combined observations from many
  instruments in order to isolate the extremely
  weak signals of the CMB
• The datasets that represent the material
  between us and the CMB are collected from
  different instruments and are stored and curated
  at many different institutions
• This is immensely difficult without approaches
  like National Virtual Observatory in order to
  provide a uniform interface for all of the
  different data formats and locations

(Julian Borrill, NERSC, LBNL)
40
NVO Approach

• Focus is on adapting emerging information
  technologies to meet the astronomy research
  challenges
• Metadata, standards, protocols (XML, http)
• Interoperability
• Database federation
• Web Services (SOAP, WSDL, UDDI)
• Grid-based computing (OGSA)
• Federating data bases is difficult, but very
  valuable
• An XML-based mark-up for astronomical tables and
  catalogs - VOTable
• Developed metadata management framework
• Formed international registry, dm (data
  models), semantics, and dal (data access
  layer) discussion groups
• As with NEESgrid, Grids are helping to unify the
  community

41
NVO Image Mosaicking

• Specify box by position and size
• SIAP server returns relevant images
• Footprint
• Logical Name
• URL

Can choose standard URL http//....... SRB
URL srb//nvo.npaci.edu/..
42
Atlasmaker Virtual Data System
Metadata repositories Federated by OAI
Higher LevelGrid Services
Data repositories Federated by SRB
2d Store result return result
Core Grid Services
2c Compute on TG/IPG
Compute resources Federated by TG/IPG
43
Background Correction
Uncorrected
Corrected
44
NVO Components
Visualization
Resource/Service Registries
Web Services
Simple Image Access Services
Cone Search Services
VOTable
VOTable
Cross-Correlation Engine
UCDs
UCDs
Streaming
Grid Services
Data archives
Computing resources
45
International Virtual Observatory Collaborations

• German AVO
• Russian VO
• e-Astronomy Australia
• IVOA(International Virtual Observatory
  Alliance)

• Astrophysical Virtual Observatory (European
  Commission)
• AstroGrid, UK e-scienceprogram
• Canada
• VO India
• VO Japan
• (leading the work on VO query language)
• VO China

US contacts Alex Szalay szalay_at_jhu.edu, Roy
Williams roy_at_cacr.caltech.edu,Bob Hanisch
lthanisch_at_stsci.edugt
46
Where to in the Future?The potential of a
Semantic Grid / Knowledge Grid Combining
Semantic Web Services and Grid Services

• Even when we have well integrated WebGrid
  services we still do not provide enough
  structured information and tools to let us ask
  what if questions, and then have the underlying
  system assemble the required components in a
  consistent way to answer such a question.

47
Beyond Web Services and Grids

• A commercial example what if question
• What does my itinerary look like if I wish to go
  SFO to Paris, CDG, and then to Bucharest.
• In Bucharest I want a 3 or 4 star hotel that is
  within 3 km of the Palace of the Parliament, and
  the hotel cost may not exceed the U. S. Dept. of
  State, Foreign Per Diem Rates.

48
Beyond Web Services and Grids

• To answer such a question a relatively easy
  task, but tedious, for a human the system must
  understand the relationships between maps and
  locations, between per diem charts and published
  hotel rates, and it must be able to apply
  constraints (lt 3 km, 3 or 4 star,cost lt per
  diem rates, etc.)
• This is the realm of Semantic Grids

49
Semantic Grids / Knowledge Grids

• Work is being adapted from the Artificial
  Intelligence community to provide 4
• Ontology languages to extend metadata to
  represent relationships
• Language constructs to express rule based /
  constraint relationships among, and
  generalizations of, the extended terms

50
Future Cyberinfrastructure
technology impact
Resource Description Framework (RDF) 7 Expresses relationships among resources (URI(L)s) in the form of object-attribute-value (property). Values of can be other resources, thus we can describe arbitrary relationships between multiple resources. RFD uses XML for its syntax. Can ask questions like What are a particular propertys permitted values, which types of resources can it describe, and what is its relationship to other properties.
Resource Description Framework Schema (RDFS) 7 An extensible, object-oriented type system that effectively represents and defines classes. Object-oriented structure Class definitions can be derived from multiple superclasses, and property definitions can specify domain and range constraints. Can now represent tree structured information (e.g. Taxonomies)
51
Future Cyberinfrastructure
technology impact
Ontology Inference Layer (OIL) 8 OIL inherits all of RDFS, and adds expressing class relationships using combinations of intersection (AND), union (OR), and compliment (NOT). Supports concrete data types (integers, strings, etc.) OIL can state conditions for a class that are both sufficient and necessary. This makes it possible to perform automatic classification Given a specific object, OIL can automatically decide to which classes the object belongs. This is functionality that should make it possible to ask the sort of constraint and relationship based questions illustrated above.
OWL (DAMLOIL) 9 Knowledge representation and manipulation that have well defined semantics and representation of constraints and rules for reasoning
52
Semantic Grid Capabilities

• Based on these technologies, the emerging
  Semantic Grid 6 / Knowledge Grid 5 services
  will provide several important capabilities
• 1) The ability to answer what if questions by
  providing constraint languages that operate on
  ontologies that describe content and
  relationships of scientific data and operations,
  thus automatically structuring data and
  simulation / analysis components into Grid
  workflows whose composite actions produce the
  desired information

53
Semantic Grid Capabilities

• 2) Tools, content description, and structural
  relationships so that when trying to assemble
  multi-disciplinary simulations, an expert in one
  area can correctly organize the other components
  of the simulation without having to involve
  experts in all of the ancillary sub-models
  (components)

54
Future Cyberinfrastructure

• Much work remains to make this vision a reality
• The Grid Forum has recently established a
  Semantic Grid Research Group 10 to investigate
  and report on the path forward for combining
  Grids and Semantic Web technology.

55
Thanks to Colleagues who Contributed Material to
this Talk

• Dan Reed, Principal Investigator, NSF NEESgrid
  Director, NCSA and the Alliance Chief Architect,
  NSF ETF TeraGrid Professor, University of
  Illinois - reed_at_ncsa.uiuc.edu
• Ian Foster, Argonne National Laboratory and
  University of Chicago, http//www.mcs.anl.gov/fo
  ster
• Dr. Robert Hanisch, Space Telescope Science
  Institute, Baltimore, Maryland
• Roy Williams, Cal Tech Dan Abrams, UIUC Paul
  Avery, Univ. of Florida Alex Szalay, Johns
  Hopkins U. Tom Prudhomme, NCSA

56
Integrated Cyberinfrastructure for Science
Science Portals collaboration and problem solving
Web Services
Grid Services secure and uniform access and
management for distributed resources
Supercomputing andLarge-Scale Storage
Supernova Observatory
Advanced Chemistry
High Speed Networks
Computing and Storageof Scientific Groups
High Energy Physics
Advanced Engine Design
Spallation Neutron Source
Macromolecular Crystallography
Advanced Photon Source
57
Notes

• 1 The Computing and Data Grid Approach
  Infrastructure for Distributed Science
  Applications, William E. Johnston.
  http//www.itg.lbl.gov/johnston/Grids/homepage.ht
  mlCI2002
• 2 DOE Office of Science, High Performance
  Network Planning Workshop.August 13-15, 2002
  Reston, Virginia, USA.http//doecollaboratory.pnl
  .gov/meetings/hpnpw
• 3 Developing Grid Computing Applications in
  IBM developerWorks Web services Web services
  articleshttp//www-106.ibm.com/developerworks/lib
  rary/ws-grid2/?n-ws-1252
• 4 See The Semantic Web and its Languages, an
  edited collection of articles in IEEE
  Intelligent Systems, Nov. Dec. 2000. D. Fensel,
  editor.
• 5 For an introduction to the ideas of Knowledge
  Grids I am indebted to Mario Cannataro, Domenico
  Talia, and Paolo Trunfio (CNR, Italy). See
  www.isi.cs.cnr.it/kgrid/
• 6 For an introduction to the ideas of Semantic
  Grids I am indebted to Dave DeRoure (U.
  Southampton), Carol Gobel (U. Manchester), and
  Geoff Fox (U. Indiana). See www.semanticgrid.org
• 7 The Resource Description Framework, O.
  Lassila. ibid.
• 8 FAQs on OIL Ontology Inference Layer, van
  Harmelen and Horrocks. ibid. and OIL An
  Ontology Infrastructure for the Semantic Web.
  Ibid.
• 9 Semantic Web Services, McIlraith, Son,
  Zeng. Ibid. and Agents and the Semantic
  Web, Hendler. Ibid.
• 10 See http//www.semanticgrid.org/GGF This GGF
  Research Group is co-chaired by David De Roure
  ltdder_at_ecs.soton.ac.ukgt, Carole Goble
  ltcgoble_at_cs.man.ac.ukgt, and Geoffrey Fox
  ltgcf_at_grids.ucs.indiana.edugt