Grid Computing 1

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Grid Computing 1

• Grid Book, Chapters 1, 2, 3, 22
• Implementing Distributed Synthetic Forces
  Simulations in Metacomputing Environments
• Brunett, Davis, Gottschalk, Messina, Kesselman
• http//www.globus.org

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Outline

• What is Grid computing?
• Grid computing applications
• Grid computing history
• Issues in Grid Computing
• Condor, Globus, Legion
• The next step

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What is Grid Computing?

• Computational Grid is a collection of
  distributed, possibly heterogeneous resources
  which can be used as an ensemble to execute
  large-scale applications
• Computational Grid also called metacomputer

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Computational Grids

• Term computational grid comes from an analogy
  with the electric power grid
• Electric power is ubiquitous
• Dont need to know the source (transformer,
  generator) of the power or the power company that
  serves it
• Analogy falls down in the area of performance
• Ever-present search for cycles in HPC. Two foci
  of research
• In the box parallel computers -- PetaFLOPS
  architectures
• Increasing development of infrastructure and
  middleware to leverage the performance potential
  of distributed Computational Grids

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Grid Applications

• Distributed Supercomputing
• Distributed Supercomputing applications couple
  multiple computational resources supercomputers
  and/or workstations
• Examples include
• SFExpress (large-scale modeling of battle
  entities with complex interactive behavior for
  distributed interactive simulation)
• Climate Modeling (high resolution, long time
  scales, complex models)

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Distributed Supercomputing Example SF Express

• SF Express (Synthetic Forces Express) large
  scale distributed simulation of behavior and
  movement of entities (tanks, trucks, airplanes,
  etc.) for interactive battle simulation.
• Entities require information about
• State of terrain
• Location and state of other entities
• Info updated several times a second
• Interest management allows entities to only look
  at relevant information, enabling scalability

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SF Express

• Large scale SF Express run goals
• Simulation of 50,000 entities in 8/97, 100,000
  entries in 3/98
• Increase fidelity and resolution of simulation
  over previous runs
• Improve
• Refresh rate
• Training environment responsiveness
• Number of automatic behaviors
• Ultimately use simulation for real-time planning
  as well as training
• Large scale runs extremely resource-intensive

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SF Express Programming Issues

• How should entities be mapped to computational
  resources?
• Entities receive information based on interests
• Communication reduced and localized based on
  interest management
• Consistency model for entity information must be
  developed
• Which entities can/should be replicated?
• How should updates be performed?

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SF Express Distributed Application Architecture

• D data server, I interest management, R
  router, S simulation node

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50,000 entity SF Express Run

• 2 large-scale simulations run on August 11, 1997

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50,000 entity SF Express Run

• Simulation decomposed terrain (Saudi Arabia,
  Kuwait, Iraq) contiguously among supercomputers
• Each supercomputer simulated a specific area and
  exchanged interest and state information with
  other supercomputers
• All data exchanges were flow-controlled
• Supercomputers fully interconnected, dedicated
  for experiment
• Success depended on moderate to significant
  system administration, interventions, competent
  system support personnel, and numerous phone
  calls.
• Subsequent Globus runs focused on improving data,
  control management and operational issues for
  wide area

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High-Throughput Applications

• Grid used to schedule large numbers of
  independent or loosely coupled tasks with the
  goal of putting unused cycles to work
• High-throughput applications include RSA
  keycracking, Seti_at_home (detection of
  extra-terrestrial intelligence), MCell

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High-Throughput Applications

• Biggest master/slave parallel program in the
  world with master website, slaves individual
  computers

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High-Throughput Example - MCell

• MCell Monte Carlo simulation of cellular
  microphysiology. Simulation implemented as
  large-scale parameter sweep.

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MCell

• MCell architecture simulations performed by
  independent processors with distinct parameter
  sets and shared input files

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MCell Programming Issues

• How should we assign tasks to processors to
  optimize locality?
• How can we use partial results during execution
  to steer the computation?
• How do we mine all the resulting data from
  experiments for results
• During execution
• After execution
• How can we use all available resources?

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Data-Intensive Applications

• Focus is on synthesizing new information from
  large amounts of physically distributed data
• Examples include NILE (distributed system for
  high energy physics experiments using data from
  CLEO), SAR/SRB applications (Grid version of MS
  Terraserver), digital library applications

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Data-Intensive Example - SARA

• SARA Synthetic Aperture Radar Atlas
• application developed at JPL and SDSC
• Goal Assemble/process files for users desired
  image
• Radar organized into tracks
• User selects track of interestand properties to
  be highlighted
• Raw data is filtered and converted to an image
  format
• Image displayed in web browser

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SARA Application Architecture

• Application structure focused around optimizing
  the delivery and processing of distributed data

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SARA Programming Issues

• Which data server should replicated data be
  accessed from?
• Should computation be done at the data server or
  data moved to a compute server or something in
  between?
• How big are the data files and how often will
  they be accessed?

AppLeS/NWS
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TeleImmersion

• Focus is on use of immersive virtual reality
  systems over a network
• Combines generators, data sets and simulations
  remote from users display environment
• Often used to support collaboration
• Examples include
• Interactive scientific visualization (being
  there with the data), industrial design, art and
  entertainment

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Teleimmersion Example Combustion System Modeling

• A shared collaborative space
• Link people at multiple locations
• Share and steer scientific simulations on
  supercomputer
• Combustion code developed by Lori Freitag at ANL
• Boiler application used to troubleshoot and
  design better products

Chicago
San Diego
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Early Experiences with Grid Computing

• Gigabit Testbeds Program
• Late 80s, early 90s, gigabit testbed program
  was developed as joint NSF, DARPA, CNRI
  (Corporation for Networking Research, Bob Kahn)
  initiative
• Goals were to
• investigate potential architecture for a
  gigabit/sec network testbed
• explore usefulness for end-users

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Gigabit Testbeds Early 90s

• 6 testbeds formed
• CASA (southwest)
• MAGIC (midwest)
• BLANCA (midwest)
• AURORA (northeast)
• NECTAR (northeast)
• VISTANET (southeast)
• Each had a unique blend of research in
  applications and in networking and computer
  science research

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Gigabit Testbeds
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Gigabit Testbeds
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I-Way

• First large-scale modern Grid experiment
• Put together for SC95 (the Supercomputing
  Conference)
• I-Way consisted of a Grid of 17 sites connected
  by vBNS
• Over 60 applications ran on the I-WAY during SC95

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I-Way Architecture

• Each I-WAY site served by an I-POP (I-WAY Point
  of Presence) used for
• authentication of distributed applications
• distribution of associated libraries and other
  software
• monitoring the connectivity of the I-WAY virtual
  network
• Users could use single authentication and job
  submission across multiple sites or they could
  work directly with end-users
• Scheduling done with a human-in-the-loop

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I-Soft Software for I-Way

• Kerberos based authentication
• I-POP initiated rsh to local resources
• AFS for distribution of software and state
• Central scheduler
• Dedicated I-WAY nodes on resource
• Interface to local scheduler
• Nexus based communication libraries
• MPI, CaveComm, CC
• In many ways, I-Way experience formed foundation
  of Globus

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I-Way Application Cloud Detection

• Cloud detection from multimodal satellite data
• Want to determine if satellite image is clear,
  partially cloudy or completely cloudy
• Used remote supercomputer to enhance instruments
  with
• Real-time response
• Enhanced function, accuracy (of pixel image)
• Developed by C. Lee, Aerospace Corporation,
  Kesselman, Caltech et al.

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PACIs

• 2 NSF Supercomputer Centers (PACIs) SDSC/NPACI
  and NCSA/Alliance, both committed to Grid
  computing
• vBNS backbone between NCSA and SDSC running at
  OC-12 with connectivity to over 100 locations at
  speeds ranging from 45 Mb/s to 155 Mb/s or more

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PACI Grid
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NPACI Grid Activities

• Metasystems Thrust Area one of the NPACI
  technology thrust areas
• Goal is to create an operational metasystems for
  NPACI
• Metasystems players
• Globus (Kesselman)
• Legion (Grimshaw)
• AppLeS (Berman and Wolski)
• Network Weather Service (Wolski)

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Alliance Grid Activities

• Grid Task Force and Distributed Computing team
  are Alliance teams
• Globus supported as exclusive grid infrastructure
  by Alliance
• Grid concept pervasive throughout Alliance
• Access Grid developed for use by distributed
  collaborative groups
• Allliance grid players include Foster (Globus),
  Livny (Condor), Stevens (ANL), Reed (Pablo), etc.

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 Other Efforts

• Centurion Cluster Legion testbed
• Legion cluster housed at UVA
• 128 533 MHz Dec Alphas
• 128 Dual 400 MHz Pentium2
• Fast ethernet and myrinet
• Globus testbed GUSTO which supports Globus
  infrastructure and application development
• 125 sites in 23 countries as of 2/2000
• Testbed aggregated from partner sites (including
  NPACI)

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GUSTO (Globus) Computational Grid
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IPG

• IPG Information Power Grid
• NASA effort in grid computing
• Globus supported as underlying infrastructure
• Application focus include aerospace design,
  environmental and space applications

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Research and Development Foci for the Grid

• Applications
• Questions revolve around design and development
  of Grid-aware applications
• Different programming models polyalgorithms,
  components, mixed languages, etc.
• Program development environment and tools
  required for development and execution of
  performance-efficient applications

Applications
Middleware
Infrastructure
Resources
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Research and Development Foci for the Grid

• Middleware
• Questions revolve around the development of tools
  and environments which facilitate application
  performance
• Software must be able to assess and utilize
  dynamic performance characteristics of resources
  to support application
• Agent-based computing and resource negotiation

Applications
Middleware
Infrastructure
Resources
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Research and Development Foci for the Grid

• Infrastructure
• Development of infrastructure that presents a
  virtual machine view of the Grid to users
• Questions revolve around providing basic services
  to user security, remote file transfer,
  resource management, etc., as well as exposing
  performance characteristics.
• Services must be supported by heterogeneous and
  interoperate

Applications
Middleware
Infrastructure
Resources
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Research and Development Foci for the Grid

• Resources
• Questions revolve around heterogeneity and scale.
• New challenges focus on combining wireless and
  wired, static and dynamic, low-power and
  high-power, cheap and expensive resources
• Performance characteristics of grid resources
  vary dramatically, integrating them to support
  performance of individual and multiple
  applciations extremely challenging

Applications
Middleware
Infrastructure
Resources
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What is the difference between Grid Computing,
Cluster Computing and the Web?

• Cluster computing focuses on platforms consisting
  of often homogeneous interconnected nodes in a
  single administrative domain.
• Clusters often consist of PCs or workstations
  and relatively fast networks
• Cluster components can be shared or dedicated
• Application focus is on cycle-stealing
  computations, high-throughput computations,
  distributed computations
• Beowulfs are clustered essentially administered
  as a single multicomputer XXX

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The Web and Grids

• Web focuses on platforms consisting of large
  number of resources and networks which support
  naming services, protocols, search engines, etc.
• Web consists of very diverse set of
  computational, storage, communication, and other
  resources shared by an immense number of users
• Application focus is on access to information,
  electronic commerce, etc. 
• Grid focuses on computing on ensembles of
  distributed heterogeneous resources
• Typically used as a platform for high performance
  computing
• Some grid resources may be shared, other may be
  dedicated or reserved
• Application focus is on high-performance,
  resource-intensive applications