# Presentation: 281270

## The Grid and its Implicationsfor Science (and Industry)

- Ian Foster
- Argonne National Laboratory
- University of Chicago
- Globus Alliance

- Royal Society of New Zealand, Wellington, March 16, 2004

## My Affiliations: (1) Physical Organizations

- My Affiliations:(2) VirtualOrganizations

## Multidisciplinary Teams:Problem Solving in the 21st Century

- Teams organized around common goals
- Communities: “Virtual organizations”
- With diverse membership & capabilities
- Heterogeneity is a strength not a weakness
- And geographic and political distribution
- No location/organization possesses all required skills and resources
- Must adapt as a function of the situation
- Adjust membership, reallocate responsibilities, renegotiate resources

## Overview

- Part I: Technology, science, & industry
- Technology trends
- Why distributed teams are important
- The need for infrastructure
- Implications beyond science
- Part II: New modes of working

## Living in an Exponential World

**Scientific American, January 2001**

**Number of Years**

**0**

**1**

**2**

**3**

**4**

**5**

**Performance per Dollar Spent**

## Moore’s Law  Tiny Sensors

**www.givenimaging.com**

- Project Neptune

- Habitat sensors

- Israeli Video Pill

## Living in an Exponential World:(2) Storage

- Storage density doubles every 12 months
- Dramatic growth in online data (1 petabyte = 1000 terabyte = 1,000,000 gigabyte)
- 2000	~0.5 petabyte
- 2005	~10 petabytes
- 2010	~100 petabytes
- 2015	~1000 petabytes?
- Transforming entire disciplines in physical and, increasingly, biological sciences; humanities next?

## Living in an Exponential World:(3) Networking (Coefficients Matter)

- Living in an Exponential World:(3) Networking (Coefficients Matter)

- Network vs. computer performance
- Computer speed doubles every 18 months
- Network speed doubles every 9 months
- Difference = order of magnitude per 5 years
- 1986 to 2000 @ NCSA
- Computers: x 500
- Networks: x 340,000
- 2001 to 2010: ??
- Computers: x 60
- Networks: x 4000

**Scientific American, January 2001**

## Parallelism Has Come to Optical Networking

- “Parallel lambdas will drive this decade
- the way parallel processors drove the 1990s” (Larry Smarr)

## The Opportunity (or Challenge):Computational Cornucopia

- The Opportunity (or Challenge):Computational Cornucopia

- Abundant computation, data, bandwidth
- In many fields, too much data—not too little
- Simulations of unprecedented accuracy
- Ubiquitous internet  distance not a barrier
- But as a consequence
- Rate of change accelerates
- Complex problems  multidisciplinary teams & sharing of resources & expertise
- Without infrastructure, you can’t compete

## Overview

- Part I: Technology, science, & industry
- Technology trends
**Why distributed teams are important**
- The need for infrastructure
- Implications beyond science
- Part II: New modes of working

## Why Distributed Teams Are Important

- Increasingly challenging & complex problems
- Global change, cosmology, biology, ...
- Life sciences, manufacturing, mineral exploration
- Film production, game development, ...
- Required expertise & resources distributed
- People
- Computational capability
- Data
- Sensors

## High Energy Physics Experiments

## Network for Earthquake Eng. Simulation

**Field Equipment**

**Laboratory Equipment**

**Remote Users**

**Remote Users: ****(****K-12 Faculty and Students)**

**Instrumented Structures and Sites**

**Leading Edge Computation **

**Curated Data Repository**

**                ****Laboratory Equipment**

**(****Faculty, Students, Practitioners)**

**Simulation Tools Repository**

- www.neesgrid.org

## The Power of Data Integration:Virtual Observatories

- No. & sizes of data sets as of mid-2002,               grouped by wavelength
-   12 waveband coverage of large     areas of the sky
-   Total about 200 TB data
-   Doubling every 12 months
-   Largest catalogues near 1B objects

-                         Data and images courtesy Alex Szalay, John Hopkins

## Overview

- Part I: Technology, science, & industry
- Technology trends
- Why distributed teams are important
**The need for infrastructure**
- Implications beyond science
- Part II: New modes of working

## “Cyberinfrastructure”

-  “A new age has dawned in scientific & engineering research, pushed by continuing progress in computing, information, and communication technology, & pulled by the expanding complexity, scope, and scale of today’s challenges. The capacity of this technology has crossed thresholds that now make possible a comprehensive “cyberinfrastructure” on which to build new types of scientific & engineering knowledge environments & organizations and to pursue research in new ways & with increased efficacy.” [NSF Blue Ribbon Panel report, 2003]

## Cyberinfrastructure-enabled Knowledge Communities

- Computation, Storage, Communication and Interface Technologies

**Cyber-infrastructure:**
- Equipment, Software, People, Institutions

- Virtual teams, communities, organizations, knowledge communities, environments/ecologies

- Slide courtesy Dan Atkins, U.Michigan

**Notes:**
Cyberinfrastructure is the middle level.

## Infrastructure (1):80% of Success is Showing Up*

- The world is shrinking rapidly—but not uniformly. E.g., from Chicago, I can send:
- 1 TB (1012 bytes) to Geneva in 20 minutes
- 1 MB (106 bytes) to Wellington in 4 hours
- The dirty underside of exponentials
- 9 month doubling  10 years lag in network deployment = **10,000x** slower
- Without an always-on, hi-speed network you might as well be on a different planet!
-  **Broadband national & international nets**

- *Woody Allen

## Collaborating at Speed(www.internetnz.net.nz/public/ngi)

- “An innovative & globally connected economy, with state of the art national internet infrastructure delivering bandwidth at capacities and prices that encourage collaboration, and stimulate researchers & entrepreneurs to seek new challenges and business opportunities.” (www.ngi-nz.co.nz/about/strategy.html)
- “In the knowledge economy, the new roads & flight paths are the internet ... **Don’t waste time with another report; just get on and build it**.” (Ian Taylor, p38)

## U.S. National Lambda Rail

**Source: John Silvester, Dave Reese, Tom West-CENIC**

**2000 Miles **
**10 ms**
**=1000x Campus Latency**

**Very High-End Experimental and Research Applications**
**  ****4 x 10GB Wavelengths Initially**
**  ****Capable of 40 x 10Gb wavelengths at Buildout**

## StarLight International Interconnects

**Source: **
**Tom DeFanti, **
**UIC**

- Trans-Pacific Optical Research Testbed

## Infrastructure (2):Moving Beyond PC Science

- “PC science”: science scoped to the data and computing that fits on a PC
- Limits questions asked & answers obtained
- Networks allow us to do much better
- Build inexpensive clusters
- Harness idle desktops: Condor etc.
- Access remote supercomputers
- Integrate multiple datasets
- Create community data resources
- Need not be inordinately expensive
- But does require investment

## Overview

- Part I: Technology, science, & industry
- Technology trends
- Why distributed teams are important
- The need for infrastructure
**Implications beyond science**
- Part II: New modes of working

## Cyberinfrastructure & VOs Have Relevance Far Beyond Science 

- 1) Virtualization of information technology
- From vertical silos to on-demand access
- Improve efficiency of delivery, increase flexibility of use
- E.g., financial services, e-commerce
- 2) New applications, products, & services enabled by much computation & data
- Media, life sciences, manufacturing, seismic exploration, online gaming, etc., etc., etc.

## The Value of Grid Computing:IBM Perspective

- Reduced Complexity
- & Cost

- Higher Quality of Service

- Increased Productivity
- & ROI

- Increased
- Efficiency

- The Value of Grid Computing:IBM Perspective

- Improved Resiliency

## Grids: HP Perspective

- shared, traded resources

- value

- clusters

- grid-enabled systems

- programmable data center

- virtual data center

- Open VMS clusters, TruCluster, MC ServiceGuard

- Tru64, HP-UX, Linux

- UDC

- computing utility or GRID

- today

**Notes:**
HP Labs, in close cooperation with the business units, is researching advanced technologies to simplify these complex GRID solutions. This research program is called Planetary Scale Computing. Its goal is to create a flexible, global secure IT infrastructure. This program builds upon the successes of clusters and GRID enabled platforms to create programmable data centers that support the dynamic allocation of servers and storage from a shared pool. These programmable data centers can be aggregated to create a virtual data center that supports the automatic and secure provisioning and assurance of services across geographical and corporate boundaries.

## Platform Symphony:Real-Time Online Processing

**Servers:**
**Execution**

**Application**
**Services:**
**Distribution**

**Applications:**
**Delivery**

- Source: *The Grid: Blueprint for a New Computing Infrastructure* (2nd Edition), 2004.

## Cost Savings from Grids

- The size of cost savings from grids will come in two waves:
- First from the adoption of clusters
- Then from the adoption of Enterprise Grids
- Firms using Clusters estimate that cost savings will be small at first, but will grow to 15% to 30% savings in IT Costs in 2005-2008.
- Firms planning to use Enterprise Grids estimate that they will experience a second wave of benefits. Savings will grow to 15% to 30% by 2007-2010.

**Source: Robert Cohen, “Grid Computing: Projected Impact on North Carolina’s Economy & Broadband Use through 2010,” Rural Internet Access Authority,  September 2003. http://www.e-nc.org**

## Source: Robert Cohen

- Because of significant cost savings, in addition to productivity gains and changes in supply chain relationships (especially in autos), many early adopter industries show dramatic gains in productivity over the levels expected in the 2010 economic forecast. This is one of the more striking findings of the research. There is a real possibility that once Web services & grids converge, firms will begin to transform their business processes & achieve even higher productivity benefits than the ones that we describe.

## The use of Grids and Web Services results in a substantial increase in bandwidth demand:  The examples are derived from two separate studies

- The economic model provides us with a way to see how much spending for communications services will shift from its assumed pattern through 2010. In many of the early adopter industries we studied, spending on communications services more than doubled by 2010. We believe that this is likely to reflect new spending on broadband access. We plan to revisit these estimates. We are also developing other approaches to examine the broadband story.

- Source: Robert Cohen

## Faster Computers + SensorsNew Modes of Working

**Images Provided by Ron Kikinis & Steve Pieper of the Surgical Planning Laboratory, Brigham and Woman’s Hospital, Harvard**

- Faster Computers + SensorsNew Modes of Working

- Open MRI and surgical theater
- Overlay of Graphics from
- Computed Data & Simulation
- Feedback to surgeon
- Change in location of landmarks
- and Target Tumor
- Feedback To MRI controls
- and radiologist to modulate
- Instrument and Improve Image

- Slide: Larry Smarr, UCSD

## New Modes of Working:Shell’s Visualization Center

**www.shellus.com/sepco/technological_excellence.pdf**

- “By being able to view the data together and talk on the spot, we were able to prepare for a pressure change not anticipated by people working on their own computers...and save significant amounts of money”
**Shell Exploration & Production Company**** **** **

- Over a hundred installed
- for local collaborative
- decision support in
- the oil and gas industry

## Part I: Summary

- New modes of working
- Integration of theory, experiment, computing
- Enabled & driven by technology trends
- Requires new infrastructure
- Broadband networks
- Beyond PC science
- Implications for industry as well as science
- Virtualization
- New modes of working

## Overview

- Part I: Technology, science, & industry
**Part II: New modes of working**
- Grid technologies
- Upper Atmosphere Research Collaboratory
- Network for Earthquake Eng. Simulation
- Data-intensive science & virtual data
- Grid2003 Data Grid
- Earth System Grid
- New modes of research & funding

## The Grid

-   “Resource sharing & coordinated problem solving in dynamic, multi-institutional virtual organizations”

- Enable integration of distributed resources
- Using general-purpose protocols & infrastructure
- To achieve better-than-best-effort service

## Example Grid Capabilities

## The (Dubious) Power Grid Analogy

-    Do we ship work to the power source?

- On-demand access to and integration of
- resources & services, regardless of location

## The Emergence ofOpen Grid Standards

- Increased functionality,
- standardization

- Custom
- solutions

- 1990

- 1995

- 2000

- 2005

- The Emergence ofOpen Grid Standards

- 2010

## Grid Technologies: Globus Toolkit & Open Grid Services Architecture

- Web Services Messaging, Security, Etc.

- WS-Resource Framework

- Domain-Specific Services

- Core Services

- Program
- Execution

- Data Services

## Partners are CreatingStrong GT-Based Grid Solutions

- Note that WSRF has little or no effect on solution users!

- Globus Toolkit

## Examples of Production Grid Deployments

-   “Persistent deployment of Grid services in support of a diverse user community”

- Grid3/iVDGL (US)
- 22 sites, O(3000) CPUs, 2 countries
- LHC Computing Grid
- 25 sites, international
- High energy physics
- NorduGrid
- 24 clusters, 724 CPUs, 6 countries; physics

- NASA IPG
- 4 sites, O(3000) CPUs
- Aeronautics
- NEESgrid (prod. 2004)
- Instruments, data, compute, collaborative
- Earthquake eng.
- TeraGrid	(prod. Jan 04)
- 5 sites, expanding

- W

- P

- P

- P

- P

- P

- P re-WS

-                  Web Services

## From Tony Blair’s Speech to the Royal Society (23 May 2002) 

-    What is particularly impressive is the way that scientists are now undaunted by important complex phenomena. **Pulling together the massive power available from modern computers, the engineering capability to design and build enormously complex automated instruments to collect new data, with the weight of scientific understanding developed over the centuries, the frontiers of science have moved into a detailed understanding of complex phenomena ranging from the genome to our global climate**. Predictive climate modelling covers the period to the end of this century and beyond, with our own Hadley Centre playing the leading role internationally.

## From Tony Blair’s Speech to the Royal Society (23 May 2002)

**   ****The emerging field of e-science should transform this kind of work. It's significant that the UK is the first country to develop a national e-science Grid, which intends to make access to computing power, scientific data repositories and experimental facilities as easy as the Web makes access to information**.
-    One of the pilot e-science projects is to develop a digital mammographic archive, together with an intelligent medical decision support system for breast cancer diagnosis and treatment. An individual hospital will not have supercomputing facilties, but through the Grid it could buy the time it needs. So the surgeon in the operating room will be able to pull up a high-resolution mammogram to identify exactly where the tumour can be found.

## Overview

- Part I: Technology, science, & industry
- Part II: New modes of working
- Grid technologies
**Upper Atmosphere Research Collaboratory**
- Network for Earthquake Eng. Simulation
- Data-intensive science & virtual data
- Grid2003 Data Grid
- Earth System Grid
- New modes of research & funding

## Upper Atmosphere Research Collaboratory

- UARC slides: Dan Atkins

## Team chat

- Work rooms

- Real-time instruments

- Archival data

- Journals

- Computational models

- Annotation

- Session replay

## Evolved into a Network of Instruments (One Global Instrument)

## Slide 51

## Overview

- Part I: Technology, science, & industry
- Part II: New modes of working
- Grid technologies
- Upper Atmosphere Research Collaboratory
**Network for Earthquake Eng. Simulation**
- Data-intensive science & virtual data
- Grid2003 Data Grid
- Earth System Grid
- New modes of research & funding

## Network for Earthquake Eng. Simulation

**Field Equipment**

**Laboratory Equipment**

**Remote Users**

**Remote Users: ****(****K-12 Faculty and Students)**

**Instrumented Structures and Sites**

**Leading Edge Computation **

**Curated Data Repository**

**                ****Laboratory Equipment**

**(****Faculty, Students, Practitioners)**

**Simulation Tools Repository**

- www.neesgrid.org

## NEESgrid High-level Structure

## Architecture of NEESgrid Equipment Site. 

## Substructure Pseudo-dynamic Testing

*Structural Test*

*Geotechnical Test*
*at Another Lab*

*Structural FE*
*Simulation**** 1***

*Geotechnical FE*
*Simulation 2*

***Total System***

**Physical Tests**

**Numerical Simulation**

## Test Structure for Multi-Site Online Simulation Test (MOST) Experiment

- Note: for ease of programming, all computational models were written in Matlab.

## MOST: A Grid Perspective

**U. Colorado**
***Experimental Model***

**UIUC***** ***
***Experimental Model***

**NCSA**** **
***Computational Model***

- SIMULATION
- COORDINATOR

## Tele-Control Services

## Data Viewer

## Number of Remote Participantsin MOST Experiment

## Overview

- Part I: Technology, science, & industry
- Part II: New modes of working
- Grid technologies
- Upper Atmosphere Research Collaboratory
- Network for Earthquake Eng. Simulation
**Data-intensive science & virtual data**
- Grid2003 Data Grid
- Earth System Grid
- New modes of research & funding

## Data-Intensive Analysis

- Users & resources in many institutions ...
- 1000s of users, 100s of institutions, petascale resources
- ... engage in collaborative data analysis
- Both structured/scheduled & interactive
- Many overlappingvirtual orgs must
- Define activities
- Pool resources
- Prioritize tasks
- Manage data
- ...

## GriPhyN Overview

## (Early) Virtual Data Language

**pythia_input**

**pythia.exe**

**cmsim_input**

**cmsim.exe**

**writeHits**

**writeDigis**

- begin v /usr/local/demo/scripts/cmkin_input.csh  file i ntpl_file_path  file i template_file  file i num_events  stdout cmkin_param_fileendbegin v /usr/local/demo/binaries/kine_make_ntpl_pyt_cms121.exe  pre    cms_env_var  stdin  cmkin_param_file  stdout cmkin_log  file o ntpl_fileendbegin v /usr/local/demo/scripts/cmsim_input.csh  file i ntpl_file  file i fz_file_path  file i hbook_file_path  file i num_trigs  stdout cmsim_param_fileendbegin v /usr/local/demo/binaries/cms121.exe  condor copy_to_spool=false  condor getenv=true  stdin  cmsim_param_file  stdout cmsim_log  file o fz_file  file o hbook_fileendbegin v /usr/local/demo/binaries/writeHits.sh   condor getenv=true   pre orca_hits   file i fz_file   file i detinput   file i condor_writeHits_log   file i oo_fd_boot   file i datasetname   stdout writeHits_log   file o hits_dbendbegin v /usr/local/demo/binaries/writeDigis.sh   pre orca_digis   file i hits_db   file i oo_fd_boot   file i carf_input_dataset_name   file i carf_output_dataset_name   file i carf_input_owner   file i carf_output_owner   file i condor_writeDigis_log   stdout writeDigis_log   file o digis_dbend

- CMS “Pipeline”

## Virtual Data Example:

**mass = 200**
**decay = WW**
**stability = 1**
**LowPt = 20**
**HighPt = 10000**

**mass = 200**
**decay = WW**
**stability = 1**
**event = 8**

**mass = 200**
**decay = WW**
**stability = 1**
**plot = 1**

**mass = 200**
**decay = WW**
**plot = 1**

**mass = 200**
**decay = WW**
**event = 8**

**mass = 200**
**decay = WW**
**stability = 1**

**mass = 200**
**decay = WW**
**stability = 3**

**mass = 200**

**mass = 200**
**decay = WW**

**mass = 200**
**decay = ZZ**

**mass = 200**
**decay = bb**

**mass = 200**
**plot = 1**

**mass = 200**
**event = 8**

- ...The scientist
- adds a new
- derived data
- branch...

- ...and continues to
- investigate...

- Search for
- WW decays of
- the Higgs Boson
- and where only
- stable, final state
- particles are
- recorded:**  stability = 1**

- Scientist discovers
- an interesting result – wants to know how
- it was derived.

- High Energy Physics

- Work and slide by
- Rick Cavanaugh and
- Dimitri Bourilkov,
- University of Florida

## Virtual Data Example:Sloan Galaxy Cluster Analysis

- Galaxy cluster
- size distribution

- Task
- Graph

- Virtual Data Example:Sloan Galaxy Cluster Analysis

- Sloan Data

- Jim Annis, Steve Kent, Vijay Sehkri, Neha Sharma, Fermilab, Michael Milligan, Yong Zhao, Chicago

## Virtual Data Example: Education(Work in Progress)

- Virtual Data Example: Education(Work in Progress)

-   “We uploaded the data to the Grid & used the grid analysis tools to find the shower”

## Overview

- Part I: Technology, science, & industry
- Part II: New modes of working
- Grid technologies
- Upper Atmosphere Research Collaboratory
- Network for Earthquake Eng. Simulation
- Data-intensive science & virtual data
**Grid2003 Data Grid**
- Earth System Grid
- New modes of research & funding

## Grid2003: An Operational Grid

**Grid2003: An Operational Grid**
**28 sites (2100-2800 CPUs) & growing**
**400-1300 concurrent jobs**
**7 substantial applications + CS experiments**
**Running since October 2003**

**Korea**

**http://www.ivdgl.org/grid2003**

## Grid2003 Components

**Computers & storage** at 28 sites (to date)
- 2800+ CPUs
**Uniform service environment** at each site
- Globus Toolkit provides basic authentication, execution management, data movement
- Pacman installation system enables installation of numerous other VDT and application services
**Global & virtual organization services**
- Certification & registration authorities, VO membership services, monitoring services
**Client-side tools** for data access & analysis
- Virtual data, execution planning, DAG management, execution management, monitoring
**IGOC**: iVDGL Grid Operations Center

## Grid2003 Metrics

| Metric | Target | Achieved |
| --- | --- | --- |
| Number of CPUs | 400 | 2762 (28 sites) |
| Number of users | > 10 | 102 (16) |
| Number of applications | > 4 | 10 (+CS) |
| Number of sites running concurrent apps | > 10 | 17 |
| Peak number of concurrent jobs | 1000 | 1100  |
| Data transfer per day | > 2-3 TB | 4.4 TB max |

## Grid2003 Applications To Date

- CMS proton-proton collision simulation
- ATLAS proton-proton collision simulation
- LIGO gravitational wave search
- SDSS galaxy cluster detection
- ATLAS interactive analysis
- BTeV proton-antiproton collision simulation
- SnB biomolecular analysis
- GADU/Gnare genone analysis
- Various computer science experiments

- www.ivdgl.org/grid2003/applications

## Metabolic 

**Module**

**Whole genome **
**Analysis**
**And Architecture**
**Module**

**Experimentation**

**Proteomics**

**Networks Analysis**
**Module**

**Metabolic **
**Simulation**

**Phenotypes**
**Module**

**Metabolic **
**Engineering**

- Gene Functions Assignments

**Experimentation**

- Conjectures about Gene Functions

- Gene Annotations
- Annotated Data Sets
- Visualization

- Genome Features
- Annotated Genome Maps
- Genomes Comparisons
- Visualization

**Metabolic Reconstructions**
**(Annotated stoichiometric Matricies)**

- Operons, regulons
- networks

- Predictions of Regulation
- Predictions of New pathways
- Functions of Hypotheticals
- Networks Comparisons

- Conserved Chromosomal Gene Clusters

## Grid2003 Usage

## Overview

- Part I: Technology, science, & industry
- Part II: New modes of working
- Grid technologies
- Upper Atmosphere Research Collaboratory
- Network for Earthquake Eng. Simulation
- Data-intensive science & virtual data
- Grid2003 Data Grid
**Earth System Grid**
- New modes of research & funding

## Earth System Grid

-    _Goal_: address technical obstacles to the sharing & analysis of high-volume data from advanced earth system models

## Under the Covers of ESG

## Overview

- Part I: Technology, science, & industry
- Part II: New modes of working
- Grid technologies
- Upper Atmosphere Research Collaboratory
- Network for Earthquake Eng. Simulation
- Data-intensive science & virtual data
- Grid2003 Data Grid
- Earth System Grid
**New modes of research & funding**

## A Powerful New Three-way Allianceis Transforming Science (& Industry)

- Requires much engineering and innovation

- Changes culture, mores, andbehaviours

*CS as the “new mathematics” – George Djorgovski*

**Notes:**
Demands new multi-national, multi-discipline, computer-enabled consortia

Computer science plays the role in relation to other sciences that mathematics did from the 17th to 20th centuries

## New Modes of Science  New Modes of Funding & Working

- Effective Grid-enabled science demands
- Discipline scientists _and_ computer scientists
- True multidisciplinary teams with time to experiment, make mistakes, build confidence
- Reward structures that encourage risk taking
- Projects at a scale to enable real progress
- Large-scale multidisciplinary projects can
- Attract international attention & collaboration
- Attract students & show them science is fun
- Train new workforce with appropriate skills

## Classic Linear Research Model

- Creation of knowledge: basic, curiosity-driven research

- Application of knowledge

- Creating effective Grids **requires** PQ research models

- Grids can **support** PQ research models

- Slide courtesy Dan Atkins, U.Michigan

## Experimental & Collaborative Approach

- Experimental procedure:
- Mix together, and shake well:
- Scientists* with an overwhelming need to pool resources to solve fundamental science problems
- Computer scientists with a vision of a Grid that will enable virtual communities to share resources
- Monitor byproducts
**Heat**: sometimes incendiary
**Light**: considerable, in eScience, computer science, & cyberinfrastructure engineering
- Operational **cyberinfrastructure** (hardware & software), with an enthusiastic and knowledgeable **user community**, and real **scientific benefits**

## Problem-Driven, Collaborative Research Methodology

- Design

- Deploy

- Build

- Apply

- Analyze

- Apply

- Apply

- Deploy

- Apply

- Global
- Community

## Summary

- Technologies rapidly reshaping landscape
- Networks, data, computers
- Distributed, multidisciplinary teams as locales for creative work
- Human capital pre-eminent
- Integrate diverse people & other resources to address challenging problems
- Require infrastructure to participate
- Grid tools are enabling a broad spectrum of collaborative/distributed computing scenarios

## For More Information

- Globus Alliance
- www.globus.org
- Global Grid Forum
- www.ggf.org
- GriPhyN
- www.griphyn.org
- Background information
- www.mcs.anl.gov/~foster
- “Collaborating at Speed”
- www.internetnz.net.nz/public/ngi

- 2nd Edition
- www.mkp.com/grid2