GriPhyN: Grid Physics Network and iVDGL: International Virtual Data Grid Laboratory

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GriPhyN Grid Physics NetworkandiVDGL
International Virtual Data Grid Laboratory
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Collaboratory Basics

• Two NSF-funded Grid projects in HENP (high energy
  and nuclear physics) and computer science
• MPS and CISE have oversight
• GriPhyN and iVDGL are too closely related to
  discuss one without discussing the other
• One is CS research and test application, the
  other is to build an international scale facility
  to do these tests, and to address other goals as
  well
• Share vision, personnel and components
• These two collaboratories are part of a larger
  effort to develop the components and
  infrastructure for supporting data intensive
  science

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Some Science Drivers

• Computation is becoming an increasingly important
  tool of scientific discovery
• Computationally intense analyses
• Global collaborations
• Large datasets
• The increasing importance of computation in
  science is more pronounced in some fields
• Complex (e.g. climate modeling) and high volume
  (HEP) simulations
• Detailed rendering (e.g. biomedical informatics)
• Data intensive science (e.g. astronomy and
  physics)
• GriPhyN and iVDGL were founded to provide the
  models and software for the data management
  infrastructure for four large projects

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SDSS / NVO

• SDSS / NVO are in full production
• Explore how the Grid can be used in astronomy
• Whats the benefit?
• How to integrate?
• How can the Grid be used for future sky surveys?
• Data processing pipelines are complex
• Has made the most sophisticated use of the
  virtual data concept

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LIGO

• Not in full production, but real data is being
  taken
• LIGO I Engineering Runs
• 35 TB since 1999 and growing
• LIGO I Science Runs
• 62 TB in two science runs, additional run planned
  that will generate 135 TB
• Eventual constant operation at 270 TB/year
• LIGO II Upgrade
• Eventual Operation at 1-2 PB / year
• Need distributed computing power of the Grid
• Need virtual data catalogs for efficient
  dissemination of data and management of workflow

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CMS / ATLAS

• CMS and ATLAS are two experiments being
  developed for the Large Hadron Collider at CERN
• Two projects, two cultures, but
• Similar data challenges
• Similar geographic distribution
• Moving closer to common tools through the LCG
  computing grid.
• Petabytes of data per year (100 PB by 2012)

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Function Types

• GriPhyN
• Distributed Research Center
• iVDGL
• Community Data System

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GriPhyN
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GriPhyN Funding

• Funded in 2000 through NSF ITR program
• 11.9M 1.6M matching

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GriPhyN Project Team

• Led by U. Florida and U. Chicago
• PDs Paul Avery (UF) and Ian Foster (UC)
• 22 Participant institutions
• 13 funded
• 9 unfunded
• Roughly 82 people involved
• 2/3 of activity computer science, 1/3 physics

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• Funded Institutions
• U. Florida
• U. Chicago
• CalTech
• U. Wisconsin - Madison
• USC / ISI
• Indiana U.
• Johns Hopkins U.
• Texas A M
• UT Brownsville
• UC Berkeley
• U Wisconsin Milwaukee
• SDSC

• Unfunded Institutions
• Argonne NL
• Fermi NAL
• Brookhaven NL
• UC San Diego
• U. Pennsylvania
• U. Illinois - Chicago
• Stanford
• Harvard
• Boston U.
• Lawrence Berkeley Lab

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Technology

• GriPhyNs science drivers demand timely access to
  very large datasets and the computer cycles and
  information management infrastructure needed to
  manipulate and transform those datasets in a
  meaningful way
• Data Grids are an approach to data management and
  resource sharing in environments where datasets
  are very large
• Policy-driven resource sharing, distributed
  storage, distributed computation, replication and
  provenance tracking
• GriPhyN and iVDGL aim to enable petascale virtual
  data grids

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Petascale Virtual DataGrids
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GriPhyN Datagrid Contributions

• GriPhyN has three areas of contribution for
  achieving the DataGrid vision
• Contributing CS research
• Virtual Data as a unifying concept
• Planning, execution and performance monitoring
• Integrating these facilities in a transparent and
  high-productivity manner Making the grid as easy
  to use as a workstation and the web
• Disseminating this research through the Virtual
  Data Toolkit and other tools
• Chimera
• Pegasus
• Integrate CS research results into GriPhyN
  science projects
• GriPhyN experiments serve as an exciting but
  demanding CS and HCI laboratory

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Virtual Data Toolkit VDT

• A suite of tools developed by the CS team to
  support science on the Grid
• Uniting theme is virtual data
• Nearly all data in physics / astronomy is virtual
  data - derivations of a large, well known data
  set
• It is possible to represent derived data as the
  set of instructions that created it
• There is no need to always copy a derived data
  set - it can be recomputed if you have the
  workflow
• Virtual data also has a number of beneficial side
  effects, e.g. data provenance,discovery,
  re-creation, workflow automation
• Many packages, a few are unique to GriPhyN,
  others are common across many Grid projects

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Motivations
Ive come across some interesting data, but I
need to understand the nature of the corrections
applied when it was constructed before I can
trust it for my purposes.
Ive detected a calibration error in an
instrument and want to know which derived data to
recompute.
Data
created-by
consumed-by/ generated-by
Transformation
Derivation
execution-of
I want to apply an astronomical analysis program
to millions of objects. If the results already
exist, Ill save weeks of computation.
I want to search an astronomical database for
galaxies with certain characteristics. If a
program that performs this analysis exists, I
wont have to write one from scratch.
Slide courtesy Ian Foster
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Chimera

• The Chimera Virtual Data System is one of the
  core tools of the GriPhyN Virtual Data Toolkit
• Virtual Data Catalog
• Represents transformation procedures and derived
  data
• Virtual Data Language Interpreter
• Translates user requests into Grid workflow

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Pegasus

• Planning Execution in Grids
• Tool for mapping complex workflows onto the Grid
• Converts abstract Chimera workflow into a
  concrete workflow, which is sent to DAGman for
  execution
• DAGman is the Condor meta-scheduler
• Determines sites and data transfers

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Virtual Data Processing Tools
VDLx
abstract planner
XML DAG
Pegasus concrete planner
Local shell planner
Condor DAG
shell DAG
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ExampleSloan Galaxy Cluster Finder
DAG
Sloan Data
Galaxy cluster size distribution
Jim Annis, Steve Kent, Vijay Sehkri, Fermilab,
Michael Milligan, Yong Zhao, Chicago
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ExampleSloan Galaxy Cluster
DAG
Sloan Data
Galaxy cluster size distribution
With Jim Annis Steve Kent, FNAL
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Resource Diagram
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International Virtual Data Grid Laboratory iVDGL
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Some Context

• There is much more to the DataGrid world than
  GriPhyN
• Broad problem space, with many cooperative
  projects
• U.S.
• Particle Physics Data Grid (PPDG)
• GriPhyN
• Europe
• DataTAG
• EU DataGrid
• International
• iVDGL

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Background and Goals

• U.S. portion funded in 2001 as a Large ITR
  through NSF
• 13.7M 2M matching
• International partners responsible for own
  funding
• Aims of iVDGL
• Establish a Global Grid Laboratory
• Conduct DataGrid tests at scale
• Promote interoperability
• Promote testbeds for non-physics applications

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Relationship to GriPhyN

• Significant overlap
• Common management, personnel overlap
• Roughly 80 people on each project, 120 total
• Tight technical coordination
• VDT
• Outreach
• Testbeds
• Common External Advisory Committee
• Different focus - domain challenges
• GriPhyN - 2/3 CS, 1/3 Physics IT Research
• iVDGL - 1/3 CS, 2/3 Physics Testbed deployment
  and operation

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Project Composition

• CS Research
• U.S. iVDGL Institutions
• UK e-science programme
• DataTAG
• EU DataGrid
• Testbeds
• ATLAS / CMS
• LIGO
• National Virtual Observatory
• SDSS

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iVDGL Institutions

• U Florida CMS
• Caltech CMS, LIGO
• UC San Diego CMS, CS
• Indiana U ATLAS, iGOC
• Boston U ATLAS
• U Wisconsin, Milwaukee LIGO
• Penn State LIGO
• Johns Hopkins SDSS, NVO
• U Chicago CS
• U Southern California CS
• U Wisconsin, Madison CS
• Salish Kootenai Outreach, LIGO
• Hampton U Outreach, ATLAS
• U Texas, Brownsville Outreach, LIGO
• Fermilab CMS, SDSS, NVO
• Brookhaven ATLAS
• Argonne Lab ATLAS, CS

T2 / Software
CS support
T3 / Outreach
T1 / Labs(not funded)
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US-iVDGL Sites

• Partners?
• EU
• CERN
• Brazil
• Australia
• Korea
• Japan

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Component Projects

• iVDGL contains several core projects
• iGOC
• International Grid Operations Center
• GLUE
• Grid Laboratory Uniform Environment
• WorldGrid 2002 international demo
• Grid3 2003 deployment effort

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iGOC

• International Grid Operations Center
• iVDGL headquarters
• Analogous to a Network Operations Center
• Located at Indiana University
• Single point of contact for iVDGL operations
• Database of contact information
• Centralized information about storage, network
  and compute resources
• Directory for monitoring services at iVDGL sites

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GLUE

• Grid Laboratory Uniform Environment
• A grid interface subset specification that
  permits applications to run on grids from VDT and
  EDG sources
• Effort to ensure interoperability across
  numerous physics grid projects
• GriPhyN, iVDGL, PPDG
• EU DataGrid, DataTAG, CrossGrid, etc.
• Interoperability effort focuses on
• Software
• Configuration
• Documentation
• Test suites

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WorldGrid

• Effort at a world wide DataGrid
• Easy to deploy and administer
• Middleware based on VDT
• Chimera development
• Scalability
• Demo at SC2002
• United DataTAG and iVDGL

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Resource Diagram
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iVDGL Management
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Issues across projects

• Technical readiness
• Infrastructure readiness
• Collaboration readiness
• Common ground
• Coupling of tasks
• Incentives

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Technical readiness

• Very high
• Physics and CS are both very high on the adoption
  curve, generally
• Long history of infrastructure development to
  support national and global experiments

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Infrastructure readiness

• Also quite high
• Not all of the pieces are in place to meet demand
• The expertise exists within these communities to
  build and maintain the necessary infrastructure
• Community is inventing the infrastructure
• Real understanding in the project that
  interoperability and standards are part of
  infrastructure

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Collaboration readiness

• Again, quite high
• Physicists have a long history of large scale
  collaboration
• CS collaborations built on old relationships with
  long time collaborators

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Common ground

• Perhaps a bit too high
• What you can do with a physics background
• Win the ACM Turing Award
• Co-invent the World Wide Web
• Direct the development of the Abilene backbone
• Because application community has a strong
  understanding of the required work and the
  technical aspects of the work, some friction
  about how work separates
• History of physicists building computational
  tools e.g. ROOT

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Coupling of tasks

• Tasks decompose into subtasks that are somewhat
  tightly coupled
• Locate tightly coupled tasks at individual sites

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Incentives

• Both groups are well motivated, but for different
  reasons
• CS is engaging in extremely cutting edge research
  across a large range of activities
• Funded for deployment as well as development
• Physics is structurally committed to global
  collaborations

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Some successes

• Lessons in infrastructure development
• Outreach and engagement
• Community buy-in / investment
• Achieving the CS research goals for Virtual Data
  and Grid execution

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Infrastructure Dev

• Looking at the history of the Grid (electrical,
  not computational)
• Long phases
• Invention
• Initial production use
• Adaptation
• Standardization / regulation
• Geographically bounded dominant design
• I.e. 220 vs. 110

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Infrastructure Dev

• We dont see this with GriPhyN / iVDGL
• Projects concurrent, not consecutive
• Pipeline approach to phases of infrastructure
  development
• Real efforts at cooperation with other DataGrid
  communities
• Why?
• Deep understanding at high levels of project that
  building it alone is not enough
• Directive and funding from NSF to do deployment

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Outreach

• The GriPhyN / iVDGL community is extremely active
  in outreach to other projects and communities
• Evangelizing virtual data
• Distributed tools
• This is a huge win for building CI that others
  can use

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Community buy-in

• Together, these projects are funded at nearly
  30M over 6 years
• This does not represent the total investment that
  was needed to make this work
• Leveraged FTE
• Unfunded testbed sites
• International partners
• Lots of collaboration with PPDG starting some
  with Alliance Expeditions, etc
• This kind of community commitment necessary for a
  project of this size to succeed

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Challenges

• Staying relevant
• Building infrastructure with term limited funding

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Staying Relevant (1)

• The application communities are fast paced, high
  power groups of people
• Real danger in those communities developing tools
  that satisfice while they wait for the tools that
  are optimal and fit into a greater
  cyberinfrastructure
• Each experiment ideally wants tools perfectly
  tailored to their needs
• Maintaining user engagement and meeting the needs
  of each community is critical, but difficult

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Staying Relevant (2)

• In addition to staying relevant to the
  experiments, GriPhyN must also be relevant to the
  greater scientific community
• To CS researchers
• To similarly data-intensive projects
• Easy to understand code, concepts, APIs, etc.
• How do you accommodate both a focused client
  community and the broader scientific community
• Common challenge across many CI initiatives

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Limited Term Investment

• These projects are both funded under the NSF ITR
  mechanism
• 5 year limit
• Would you buy your telephone service from a
  company that was going to shut down after 5
  years?
• Challenge to find a sustainable support mechanism
  for CI