The OptIPuter: Enabling Campus, State, National, and Planetary-Scale LambdaGrids - PowerPoint PPT Presentation

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The OptIPuter: Enabling Campus, State, National, and Planetary-Scale LambdaGrids

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Title: The OptIPuter: Enabling Campus, State, National, and Planetary-Scale LambdaGrids


1
"The OptIPuter Enabling Campus, State, National,
and Planetary-Scale LambdaGrids"
  • LONI Forum
  • Baton Rouge, LA
  • September 2, 2004

Dr. Larry Smarr Director, California Institute
for Telecommunications and Information
Technologies Harry E. Gruber Professor, Dept. of
Computer Science and Engineering Jacobs School of
Engineering, UCSD
2
OptIPuter Application Motivation Interactive
Analysis of Remote Multi-Gigabyte Data Objects
  • Hundreds Of Megapixels 2-D Images
  • Satellite Imaging and Remote Sensing
  • Telescopes or Microscopy
  • GigaZone 3-D (1k x 1k x 1k) Objects
  • Supercomputer Simulations
  • Seismic or Medical Imaging
  • Goal Remote Interactive Analysis and
    Visualization
  • Virtually Impossible Today
  • New Technology of Dedicated Lightpaths
  • Bring These Capabilities to Laboratory
    Researchers

3
Cosmic Simulator with a Billion Zone and
Gigaparticle Resolution
Source Mike Norman, UCSD
  • 5123 AMR or 10243 Unigrid
  • 8-64 Times Mass Resolution
  • Can Simulate First Galaxies
  • One Gigazone Run
  • Output 10 TeraByte
  • Snapshot is 100 GB
  • Must Visually Analyze

10243 Unigrid
Compare with Sloan Survey
SDSC Blue Horizon (2004)
4
OptIPuter Driver On-Line Microscopes
CreatingVery Large Biological Montage Images
IBM 9M Pixels
  • 2-Photon Laser Confocal Microscope
  • High Speed On-line Capability
  • Montage Image Sizes Exceed 16x Highest Resolution
    Monitors
  • 150 Million Pixels!
  • Use Graphics Cluster with Multiple GigEs to Drive
    Tiled Displays

Source David Lee, NCMIR, UCSD
5
Earth System Enterprise-Data Lives in
Distributed Active Archive Centers (DAAC)
EOS Aura Satellite Has Been Launched Challenge is
How to Evolve to New Technologies
6
However, Average Throughput of NASA Data Products
to End User is Only 50-100 Megabits/s
Tested from GSFC-ICESAT August 2004
http//ensight.eos.nasa.gov/Missions/icesat/index.
shtml
7
High Resolution Aerial Photography Generates
Images With 10,000 Times More Data than Landsat7
Shane DeGross, Telesis
USGS
8
States are Acquiring Their Own Dark Fiber
Networks -- Illinoiss I-WIRE and Indianas
I-LIGHT
Today Nearly Two Dozen States are Creating Dark
Fiber NetworksIncluding the Most Advanced LONI!
Source Charlie Catlett, ANL
9
Regional Network Evolution
  • The Fundamental Nature of Regional Networking is
    Rapidly Changing
  • The GigaPoP Model Based on Provisioned,
    High-Capacity Services Steadily Is Being Replaced
  • On The Metro and Regional Scales
  • A Model of Facility-Based Networking Built with
    Owned Assets has Emerged
  • Regional Optical Networks (RONs)
  • Notably, this Change Increases the Importance of
    Regional Networks in the Traditional Three-Level
    Hierarchy of U.S. RE Advanced Networking

Source Steve Corbató, Internet2
10
Leading Emerging Regional Optical Networks
  • New York New England Region (NEREN)
  • North Carolina (NC LambdaRail)
  • Ohio (Third Frontier Network)
  • Oklahoma (OneNet)
  • Oregon
  • Pacific Northwest (Lariat Supported by NIH)
  • Rhode Island (OSHEAN)
  • SURA Crossroads Southeastern U.S.)
  • Texas (LEARN)
  • Utah
  • Virginia (MATP)
  • Wisconsin (WEROC)
  • California (CALREN)
  • Colorado (FRGP/BRAN)
  • Connecticut (Conn. Education Network)
  • Florida (Florida LambdaRail)
  • Georgia (Southern Light Rail)
  • Indiana (I-LIGHT)
  • Illinois (I-WIRE)
  • Louisiana (LONI)
  • Maryland, D.C. northern Virginia (MAX)
  • Michigan (MiLR)
  • Minnesota

Source Steve Corbató, Internet2
11
NLR Will Provide an Experimental Network
Infrastructure for U.S. Scientists Researchers
National LambdaRail Partnership Serves Very
High-End Experimental and Research Applications
4 x 10Gb Wavelengths Initially Capable of 40 x
10Gb wavelengths at Buildout
Starting Fall 2004
12
Dark Fiber Gauging Community-Wide Progress
  • Aggregate Dark Fiber Assets Acquired by/for U.S.
    RE Optical Initiatives
  • CENIC (for CalREN NLR)
    6,200 (Route-miles)
  • FiberCo (via Level 3 for NLR RONs)
    5,650
  • SURA (via ATT)
    6,000
  • Plus 2,000 Route-Miles for Research
  • NLR Phase 2 (WilTel Qwest)
    4,000
  • OARnet
    1,600
  • ORNL (via Qwest)
    900
  • Other Projects (IN,IL,MI,OR, )
    1,500
  • Total (conservative estimate)
    25,850
  • Over 55 of these Assets are in Regionals
  • Remainder held by NLR (11,250 Route-Miles)

Source Steve Corbató, Internet2
13
Global Lambda Integrated Facility (GLIF)Creates
Metacomputers on the Scale of Planet Earth
Stockholm NorthernLight
New York MANLAN
10 Gbit/s
IEEAF 10 Gbit/s
10 Gbit/s
2.5 Gbit/s
2.5 Gbit/s
10 Gbit/s
SURFnet 10 Gbit/s
CAnet4
Amsterdam NetherLight
Dwingeloo ASTRON/JIVE
Chicago StarLight
Tokyo WIDE
10 Gbit/s
IEEAF 10 Gbit/s
DWDM SURFnet
NSF 10 Gbit/s
10 Gbit/s
10 Gbit/s
2.5 Gbit/s
Tokyo APAN
SURFnet 10 Gbit/s
2.5 Gbit/s
10 Gbit/s
Prague CzechLight
Geneva CERN
London UKLight
www.glif.is Created in Reykjavik, Iceland 2003
Source Kees Neggers, SURFnet
14
The OptIPuter Project Removing Bandwidth as an
Obstacle In Data Intensive Sciences
  • NSF Large Information Technology Research
    Proposal
  • Cal-(IT)2 and UIC Lead CampusesLarry Smarr PI
  • USC, SDSU, NW, Texas AM, Univ. Amsterdam
    Partnering Campuses
  • Industrial Partners
  • IBM, Sun, Telcordia/SAIC, Chiaro Networks,
    Calient, Glimmerglass
  • 13.5 Million Over Five Years
  • Optical IP Streams From Lab Clusters to Large
    Data Objects

NIH Biomedical Informatics
NSF EarthScope and ORION
Research Network
http//ncmir.ucsd.edu/gallery.html
siovizcenter.ucsd.edu/library/gallery/shoot1/index
.shtml
15
OptIPuter Middleware Architecture for
Distributed Virtual Computers
OptIPuter Applications
Visualization
DVC 1
DVC 2
DVC 3
Layer 5 SABUL, RBUDP, Fast, GTP
Real-Time Objects
Security Models
Data Services DWTP
Higher Level Grid Services
Grid and Web Middleware (Globus/OGSA/WebServices
/J2EE)
Layer 4 XCP
Node Operating Systems
l-configuration, Net Management
Physical Resources
From Grids to LambdaGrids
Source Andrew Chien, UCSD OptIPuter Software
Systems Architect
16
Creating a Model for a Campus LambdaGrid
17
OMNInetThe Metro Area OOO Tesbed
NTON
10 Gb Lambdas
18
10GE OptIPuter CAVEWAVEWill Help Launch the
National LambdaRail
EVL
Source Tom DeFanti, OptIPuter co-PI
19
OptIPuter Scalable Visualization SystemsEnd
User Clusters Connected With Gigabit Flows
Earth Sciences are an OptIPuter Driver Major NASA
Goddard Visit This Week
20 Megapixels
3 Megapixels
Linux Cluster with Nvidia Graphics Cards
20
High Bandwidth Optical Fibers Will Enable High
Definition Global Virtual Teaming
UC Irvine
UC San Diego
In 2005 Cal-(IT)2 will Link Its Two Buildings
Creating a Collaboration Laboratory
21
Ultra-Resolution OptIPuter Displays Utilizing
Photonic Multicasting --Scaling to 100 Million
Pixels
UIC-EVL
GlimmerglassSwitch Used to Multicast and Direct
TeraVision Stream from One Tile to Another on the
Geowall-2
Driven by Linux Graphics Clusters
Glimmerglass Switch
30 Megapixel High-Resolution Visualizations 1
Gigapixel at 30fps 30Gb/s Bandwidth
22
Currently Developing OptIPuter Software to
Coherently Drive 100 MegaPixel Displays
  • 55-Panel Display
  • 100 Megapixel
  • 11x5 21 LCDs
  • Developed at EVL
  • Driven by 30 Dual-Opterons (64-bit)
  • 60 TB Disk
  • 30 10GE interfaces
  • Linked to OptIPuter
  • Complementary to NASA ARC HyperWall

23
Special issue of Communications of the ACM
(CACM)Blueprint for the Future of
High-Performance Networking
  • Introduction
  • Maxine Brown (guest editor)
  • TransLight A Global-scale LambdaGrid for
    e-Science
  • Tom DeFanti, Cees de Laat, Joe Mambretti, Kees
    Neggers, Bill St. Arnaud
  • Transport Protocols for High Performance
  • Aaron Falk, Ted Faber, Joseph Bannister, Andrew
    Chien, Bob Grossman, Jason Leigh
  • Data Integration in a Bandwidth-Rich World
  • Ian Foster, Robert Grossman
  • The OptIPuter
  • Larry Smarr, Andrew Chien, Tom DeFanti, Jason
    Leigh, Philip Papadopoulos
  • Data-Intensive e-Science Frontier Research
  • Harvey Newman, Mark Ellisman, John Orcutt

www.acm.org/cacm
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