The GRID Era Vanguard, Miami 23 September 2002

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The GRID EraVanguard, Miami23 September 2002

• Gordon Bell gbell_at_microsoft.com
• Bay Area Research Center
• Microsoft Corporation

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Grid Technology

• Background
• Taxonomy
• Grids from seti_at_home to arbitrary cluster
  platform
• Grid-type examples and web services
• Summary

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Bright spots in the evolution from prototypes
to early suppliers

• Early efforts
• UC/Berkeley NOW U of WI Condor NASA
  BeowulfgtAirframes
• Argonne (Foster el al) Grid Globus Toolkit,
  Grid Forum
• Entropia startup (Andrew Chien)
• Andrew Grimshaw - Avaki
• Making Legion vision real. A reality check.
• United Devices MetaProcessor Platform
• UK e-Sciences research program. Apps-based
  funding. Web services based Grid data
  orientation.
• Nimrod at Monash University
• Parameter scans other low hanging fruit
• Encapsulate apps! Excel-- language/control
  mgmt.
• Legacy apps. No time or resources to modify
  code independent of age, author, or language
  e.g. Java.
• Grid Services Gray et al Skyservice and
  Terraservice
• Goal providing a web service must be as easy as
  publishing and using a web pageand will occur!!!

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Grid Taxonomy c2002
X

• Taxonomy interesting vs necessity
• Cycle scavenging and object evaluation (e.g.
  seti_at_home, QCD)
• File distribution/sharing for IP theft e.g.
  Napster
• Databases programs for a community(astronomy,
  bioinformatics, CERN, NCAR)
• Workbenches web workflow chem, bio
• Exchanges many sites operating together
• Single, large objectified pipeline e.g. NASA.
• Grid as a cluster platform! Transparent
  arbitrary access including load balancing
• Homogeneous/heterogeneous computers
• Fixed or variable network loading
• Intranet, extranet, internet (many organizations)

Web SVCs
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Grids Ready for prime time.

• Economics thief, scavenger, power, efficiency or
  resource e.g. programs and database sharing?
• Embarrassingly parallel apps e.g. parameter
  scans killer apps
• Coupling large, separated apps
• Entry points for web services
• Research funding thats where the money is.

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Grid ComputingConcepts, Appplications, and
Technologies

• Ian Foster
• Mathematics and Computer Science Division
• Argonne National Laboratory
• and
• Department of Computer Science
• The University of Chicago
• www.mcs.anl.gov/foster/talks.htm.

Grid Computing in Canada Workshop, University of
Alberta, May 1, 2002
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Globus Toolkit

• A software toolkit addressing key technical
  problems in the development of Grid-enabled
  tools, services, and applications
• Offer a modular set of orthogonal services
• Enable incremental development of grid-enabled
  tools and applications
• Implement standard Grid protocols and APIs
• Available under liberal open source license
• Large community of developers users
• Commercial support

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Globus Toolkit Core Services(work in progress
since 1996)

• Small, standards based set of protocols.Embedded
  in Open Source ToolkitEnabling web services and
  applications
• Scheduling (Globus Resource Alloc. Manager)
• Low-level scheduler API
• Information (Directory Service) UDDI
• Uniform access to structure/state information
• Communications (Nexus)
• Multimethod communication QoS management
• Security (Globus Security Infrastructure)
• Single sign-on, key management
• Health and status (Heartbeat monitor)
• Remote file access (Global Access to Storage)

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Living in an Exponential World(1) Computing
Sensors

• Moores Law transistor count doubles each 18
  months

Magnetohydro- dynamics star formation
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The 13.6 TF TeraGridComputing at 40 Gb/s
Site Resources
Site Resources
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HPSS
HPSS
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External Networks
External Networks
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Caltech
Argonne
External Networks
External Networks
NCSA/PACI 8 TF 240 TB
SDSC 4.1 TF 225 TB
Site Resources
Site Resources
HPSS
UniTree
NCSA, SDSC, Caltech, Argonne
www.teragrid.org
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Access Grid

• High-end group work and collaboration technology
• Grid services being used for discovery,
  configuration, authentication
• O(50) systems deployed worldwide
• Basis for SC2001 SC Global event in November
  2001
• www.scglobal.org

www.accessgrid.org
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Grids at NASA Aviation Safety
Wing Models

• Lift Capabilities
• Drag Capabilities
• Responsiveness

Stabilizer Models
Airframe Models

• Deflection capabilities
• Responsiveness

Crew Capabilities - accuracy - perception -
stamina - re-action times - SOPs
Engine Models

• Braking performance
• Steering capabilities
• Traction
• Dampening capabilities

• Thrust performance
• Reverse Thrust performance
• Responsiveness
• Fuel Consumption

Landing Gear Models
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A Large Virtual Organization CERNs Large Hadron
Collider

• 1800 Physicists, 150 Institutes, 32 Countries
• 100 PB of data by 2010 50,000 CPUs?

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Life Sciences Telemicroscopy
DATA ACQUISITION
PROCESSING,ANALYSIS
ADVANCEDVISUALIZATION
NETWORK
COMPUTATIONALRESOURCES
IMAGING INSTRUMENTS
LARGE DATABASES
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Nimrod/G and GriddLeS Grid Programming with Ease

• David Abramson
• Monash University
• DSTC

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Building on Legacy Software

• Nimrod
• Support parametric computation without
  programming
• High performance distributed computing
• Clusters (1994 1997)
• The Grid (1997 - ) (Added QOS through
  Computational Economy)
• Nimrod/O Optimisation on the Grid
• Active Sheets Spreadsheet interface
• GriddLeS
• General Grid Applications using Legacy Software
• Whole applications as components
• Using no new primitives in application

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Parametric Execution

• Study the behaviour of some of the output
  variables against a range of different input
  scenarios.
• Allows real time analysis for many applications
• More realistic simulations
• More rigorous science
• More robust engineering

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Some science is hitting a wallFTP and GREP are
not adequate (Jim Gray)

• You can FTP 1 MB in 1 sec.
• You can FTP 1 GB / min.
• 2 days and 1K
• 3 years and 1M

• You can GREP 1 GB in a minute
• You can GREP 1 TB in 2 days
• You can GREP 1 PB in 3 years.
• 1PB 10,000 gtgt 1,000 disks
• At some point you need indices to limit
  search parallel data search and analysis
• Goal using dbases. Make it easy to
• Publish Record structured data
• Find data anywhere in the network
• Get the subset you need!
• Explore datasets interactively
• Database becomes the file system!!!

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SkyServer delivering a web service to the
astronomy community. Prototype for other
sciences? Gray, Szalay, et al

• First paper on the SkyServer
• http//research.microsoft.com/gray/Papers/MSR_TR
  _2001_77_Virtual_Observatory.pdf
• http//research.microsoft.com/gray/Papers/MSR_TR
  _2001_77_Virtual_Observatory.doc
• Later, more detailed paper for database community
• http//research.microsoft.com/gray/Papers/MSR_TR
  _01_104_SkyServer_V1.pdf
• http//research.microsoft.com/gray/Papers/MSR_TR
  _01_104_SkyServer_V1.doc

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What can be learned from Sky Server?

• Its about data, not about harvesting flops
• 1-2 hr. query programs versus 1 wk programs based
  on grep
• 10 minute runs versus 3 day compute searches
• Database viewpoint. 100x speed-ups
• Avoid costly re-computation and searches
• Use indices and PARALLEL I/O. Read / Write gtgt1.
• Parallelism is automatic, transparent, and just
  depends on the number of computers/disks.
• Limited experience and talent to use dbases.

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Sloan Digital Sky Survey Analysis
Size distribution of galaxy clusters?
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Network concerns

• Very high cost
• (1 1) / GByte to send on the net Fedex and
  160 GByte shipments are cheaper
• DSL at home is 0.15 - 0.30
• Disks cost less than 2/GByte to purchase
• Low availability of fast links (last mile
  problem)
• Labs universities have DS3 links at most, and
  they are very expensive
• Traffic Instant messaging, music stealing
• Performance at desktop is poor
• 1- 10 Mbps very poor communication links
• Manage trade-in fast links for cheap links!!

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For More Information

• www.gridtoday.com
• Grid concepts, projects
• www.mcs.anl.gov/foster
• The Globus Project
• www.globus.org
• Open Grid Services Arch.
• www.globus.org/ogsa
• Global Grid Forum
• www.gridforum.org
• GriPhyN project
• www.griphyn.org
• Avika, Entropia, UK eSciences, Condor,
• Grid books in press

Published July 1998
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The EndAre GRIDs already a real, useful,
computing structure?When will Grids be
ubiquitous?
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Toward a Framework for Preparing and Executing
Adaptive Grid Programs An Overview of the GrADS
Project Sponsored by NSF NGS Ken Kennedy Center
for High Performance Software Rice
University http//www.cs.rice.edu/ken/Presentati
ons/GrADSOverview.pdf
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GrADS Vision

• Build a National Problem-Solving System on the
  Grid
• Transparent to the user, who sees a
  problem-solving system
• Software Support for Application Development on
  Grids
• Goal Design and build programming systems for
  the Grid that broaden the community of users who
  can develop and run applications in this complex
  environment
• Challenges
• Presenting a high-level application development
  interface
• If programming is hard, the Grid will not not
  reach its potential
• Designing and constructing applications for
  adaptability
• Late mapping of applications to Grid resources
• Monitoring and control of performance
• When should the application be interrupted and
  remapped?

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Today Globus

• Developed by Ian Foster and Carl Kesselman
• Grew from the I-Way (SC-95)
• Basic Services for distributed computing
• Resource discovery and information services
• User authentication and access control
• Job initiation
• Communication services (Nexus and MPI)
• Applications are programmed by hand
• Many applications
• User responsible for resource mapping and all
  communication
• Existing users acknowledge how hard this is

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GrADSoft Architecture
Program Preparation System
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Configurable Object Program

• Goal Provide minimum needed to automate resource
  selection and program launch
• Code
• Today MPI program
• Tomorrow more general representations
• Mapper
• Defines required resources and affinities to
  specialized resources
• Given a set of resources, maps computation to
  those resources
• Optimal performance, given all requirements met
• Performance Model
• Given a set of resources and mapping, estimates
  performance
• Serves as objective function for Resource
  Negotiator/Scheduler

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GrADSoft Architecture
Execution Environment
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Grid nj. An arbitrary distributed, cluster
platform

• A geographical and multi-organizational
  collection of diverse computers dynamically
  configured as cluster platforms responding to
  arbitrary, ill-defined jobs thrown at it.
• Costs are not necessarily favorable e.g. disks
  are less expensive than cost to transfer data.
• Latency and bandwidth are non-deterministic, gt
  cluster with unknown, dynamic parameters
• Once a large body of data exists for a job, it is
  inherently bound to (set into) fixed resources.
• Large datasets I/O bound programs need to be
  with their data or be database accesses
• But are there resources there to share?
• Costs may vary, depending on organization

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Cactus(Allen, Dramlitsch, Seidel, Shalf, Radke)

• Modular, portable framework for parallel,
  multidimensional simulations
• Construct codes by linking
• Small core (flesh) mgmt services
• Selected modules (thorns) Numerical methods,
  grids domain decomps, visualization and
  steering, etc.
• Custom linking/configuration tools
• Developed for astrophysics, but not
  astrophysics-specific

Thorns
Cactus flesh
www.cactuscode.org
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Cactus ExampleTerascale Computing

• Solved EEs for gravitational waves (real code)
• Tightly coupled, communications required through
  derivatives
• Must communicate 30MB/step between machines
• Time step take 1.6 sec
• Used 10 ghost zones along direction of machines
  communicate every 10 steps
• Compression/decomp. on all data passed in this
  direction
• Achieved 70-80 scaling, 200GF (only 14 scaling
  without tricks)

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Grid Projects in eScience
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Nimrod/G and GriddLeS Grid Programming with Ease

• David Abramson
• Monash University
• DSTC

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Distributed computing comes to the rescue .
For each scenario Generate input files Copy
them to remote node Run SMOG model Post-process
output files Copy results back to root
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Its just too hard!

• Doing by hand
• Nightmare!!
• Programming with (say) MPI
• Overkill
• No fault tolerance
• Codes no longer work as stand alone code.
• Scientists dont want to know about underlying
  technologies

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Building on Legacy Software

• Nimrod
• Support parametric computation without
  programming
• High performance distributed computing
• Clusters (1994 1997)
• The Grid (1997 - ) (Added QOS through
  Computational Economy)
• Nimrod/O Optimisation on the Grid
• Active Sheets Spreadsheet interface
• GriddLeS
• General Grid Applications using Legacy Software
• Whole applications as components
• Using no new primitives in application

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Parametric Execution

• Study the behaviour of some of the output
  variables against a range of different input
  scenarios.
• Allows real time analysis for many applications
• More realistic simulations
• More rigorous science
• More robust engineering

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In Nimrod, an application doesnt know it has
been Grid enabled
Input Files Substitution
Output Files
Computational Nodes
Root Machine
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How does a user develop an application using
Nimrod?
Description of Parameters PLAN FILE
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GriddLeS

• Significant body of useful applications that are
  not Grid Enabled
• Lessons from Nimrod
• Users will avoid rewriting applications if
  possible
• Applications need to function in the Grid and
  standalone
• Users are not experts in parallel/distributed
  computing
• General Grid computations have much more general
  interconnections than possible with Nimrod.
• Legacy Applications are Components!

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GriddLeS

• Specification of the interconnections between
  components
• Interfaces for discovering resources and mapping
  the computations to them
• Locate data files in the grid and connect the
  applications to them
• Schedule computations on the underlying platforms
  and making sure the network bandwidth is
  available and
• Monitor the progress of the grid computation and
  reassign work to other parts of the Grid as
  necessary.

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Today Condor

• Support for matching application requirements to
  resources
• User and resource provider write ClassAD
  specifications
• System matches ClassADs for applications with
  ClassADs for resources
• Selects the best match based on a
  user-specified priority
• Can extend to Grid via Globus (Condor-G)
• What is missing?
• User must handle application mapping tasks
• No dynamic resource selection
• No checkpoint/migration (resource re-selection)
• Performance matching is straightforward
• Priorities coded into ClassADs