Download What is Cyberinfrastructure? The Computer Science

What is Cyberinfrastructure?
The Computer Science Perspective
Dr. Chaitan Baru
Project Director, The Geosciences Network (GEON)
Director, Science R&D, San Diego Supercomputer Center
SACNAS, Sept 29-Oct 1, 2005, Denver, CO
Cyberinfrastructure: A Definition
“The comprehensive infrastructure needed to
capitalize on dramatic advances in information
technology has been termed cyberinfrastructure.”
From “NSF’S Cyberinfrastructure Vision for 21st Century Discovery,” NSF
Cyberinfrastructure Council, September 26th, 2005, Ver.4.0, pg 4.
 Application of IT to problems in science and
engineering…and in other areas
 “Comprehensive infrastructure”, i.e. hardware,
software, and expertise (people)
SACNAS, Sept 29-Oct 1, 2005, Denver, CO
Cyberinfrastructure: What do we mean?
•
Technologies to bring remote resources together
A broad, systemic, strategic conceptualization
Components of Cyberinfrastructure (Web
Services)-enabled science & engineering
High-performance computing
for modeling, simulation, data
processing/mining
Humans
Individual &
Group Interfaces
& Visualization
Instruments for
observation and
characterization.
Global
Connectivity
Facilities for activation,
manipulation and
construction
Collaboration
Services
http://www.communitytechnology.
org/nsf_ci_report/
Physical World
Knowledge management
institutions for collection building
and curation of data, information,
literature, digital objects
Source: Dan Atkins
Implies global (international) system for collaboration
SACNAS, Sept 29-Oct 1, 2005, Denver, CO
Evolution of the Computational
Infrastructure Investments in the US
Source: Dr. Deborah Crawford
Chair, NSF CyberInfrastructure Working Group (CIWG)
Cyberinfrastructure
Terascale
GRID Term Coined ~ Metacomputing
Telescience: Access to Remote Resources
PACI
Prior
Computing
Investments
Supercomputer Centers
|
1985
|
|
1990
1995
• NPACI: National Partnership for
Advanced Computational Infrastructure
• NCSA: National Computatioal
Science Alliance
NSF Networking
Mosaic - Web Browser
TCS, DTF,
ETF
SDSC (San Diego Supercomputer Center); NCSA
(National Center for Supercomputing Applications);
PSC (Pittsburgh Supercomputer Center), CTC
(Cornell
Theory Center)
|
|
|
2000
2005
2010
A timeline from the Computational Infrastructure Division of the US National Science Foundation
SACNAS, Sept 29-Oct 1, 2005, Denver, CO
Integrated Cyberinfrastructure System:
Meeting the needs of multiple communities
Applications
Education and Training
Discovery & Innovation
Source: Dr. Deborah Crawford, Chair, NSF CyberInfrastructure Working
Group
• Environmental Science
• High Energy Physics
• Biomedical Informatics
• Geoscience
Development
Tools & Libraries
Domainspecific
Cybertools
(software)
Shared
Cybertools
(software)
Grid Services
& Middleware
Hardware
SACNAS, Sept 29-Oct 1, 2005, Denver, CO
Distributed
Resources
(computation,
communication
storage, etc.)
Examples of NSF Cyberinfrastructure
Projects
•
GriPhyN: Grid Physics Network
•
•
NVO: National Virtual Observatory
•
•
•
Sharing of experimental data
Central, persistent repository for data from shake-table and tsunami wave tank
experiments
GEON: Geosciences Network
•
•
•
Sharing human and mouse structural and functional brain imaging data between
independent, remote research groups
NEES: Network for Earthquake Engineering Simulations
•
•
•
Providing online access to digital sky surveys
Integrating heterogeneous sky surveys
BIRN: Biomedical Informatics Research Network (NIH)
•
•
Sharing high-energy physics data from single, large data sources
Integrating existing 4D multi-disciplinary data products
Extreme heterogeneity in data: discipline, scale, resolution, accuracy
SEEK: Science Environment for Ecological Knowledge
•
•
IT infrastructure to support ecological modeling
Access to distributed ecological data collections
All require (on-demand) access to large computers, for modeling, data
analysis, visualization and data assimilation
SACNAS, Sept 29-Oct 1, 2005, Denver, CO
Guiding Principles for CI Projects
•
Use IT state-of-the-art, and develop advanced IT where needed
•
•
Employ open-architecture and standards-based approach, based on community
standards
•
•
IT works in close conjunction with science, to develop best practices, data sharing frameworks,
useful and usable capabilities and tools
Create the “science infrastructure”
•
•
•
•
Use best practices, including commercial tools,
while developing advanced technology in open source, and doing CS research
An equal partnership
•
•
E.g.use of Web services and/or Grid services based approach to accessing distributed resources
 The “two-tier” approach
•
•
•
to support the “day-to-day” conduct of science (e-science)
Integrated online databases with advanced search engines
Online science models
Robust tools and applications, etc.
Leverage other intersecting projects
•
•
•
Much commonality in the technologies, regardless of science disciplines
Constantly work towards eliminating (or, at least, minimizing) the “NIH” syndrome
And, importantly, try not to reinvent what industry already knows how to do…
SACNAS, Sept 29-Oct 1, 2005, Denver, CO