An Introduction to Grid Computing Research at Notre Dame

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An Introduction toGrid Computing Researchat
Notre Dame

• Prof. Douglas Thain
• University of Notre Dame
• http//www.cse.nd.edu/dthain

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What is Grid Computing?

• Grid computing is the idea that we can attack
  problems of enormous scale by harnessing lots of
  machines to work on one problem.
• When people refer to The Grid, they are imagining
  a future where computers all over the globe are
  connected in one colossal system open for use.
• Today, we have a variety of large, useful grids,
  but we dont yet have The Grid.

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Campus Scale Grids at Notre Dame

• ND BOB Bunch of Boxes
• A closet grid of conventional PCs.
• 212 CPUs in Stepan Hall
• http//bob.nd.edu
• ND Center for Research Computing
• A cluster grid of dedicated rackmount computers
  downtown.
• 900 CPUs in Union Station.
• http//crc.nd.edu
• ND Condor Pool
• A workstation grid of classroom and desktop
  machines used when idle.
• 405 CPUs in Fitzpatrick/Nieuwland
• http//www.nd.edu/condor

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Volunteer Grids

• Simple Idea
• Most computers are idle 90 of the day.
• Can we harness their unused capacity for real
  work?
• Examples
• Pioneered by Condor in 1987 at the Univ
  Wisconsin.
• Popularized by SETI_at_Home in 1999 at Berkeley
• Over 300,000 active participants today.
• Successor is the more general BOINC.
• Folding_at_Home
• About 200,000 CPUs today.
• Makes use of GPU cards about 100x faster than
  CPU!
• Xgrid deployed with every Macintosh today.
• Challenge The user must be flexible!

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National Computing Grids

• NSF Teragrid
• Open to any NSF research.
• 21,972 CPUs / 220 TB / 6 sites
• Open Science Grid
• Open to any university.
• 21,156 CPUs / 83 TB / 61 sites
• Condor Worldwide
• Anyone can install a pool.
• 96,352 CPUs / 1608 sites
• PlanetLab
• Open to CS research sites.
• 753 CPUs / 363 sites

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Who Needs Grid Computing?

• Anyone with unlimited computing needs!
• High Energy Physics
• Simulating the detector a particle accelerator
  before turning it on allows one to understand the
  output.
• Biochemistry
• Simulate complex molecules under different forces
  to understand how they fold/mate/react.
• Biometrics
• Given a large database of human images, evaluate
  matching algorithms by comparing all to all.
• Climatology
• Given a starting global climate, simulate how
  climate develops under varying assumptions or
  events.

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What are the Challenges?

• Why dont we have The Grid yet?
• Technical Challenges
• Enforcing the wishes of all the owners.
• Automatically negotiating expectations.
• Limiting what resources a user can consume.
• Performance and scalability.
• Debugging and troubleshooting.
• Managing access to data!
• Making it easy to use!

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An Example ofa Workstation Gridat Notre Dame
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Computing Environment
I will only run jobs between midnight and 8 AM
I will only run jobs when there is no-one working
at the keyboard
Miscellaneous CSE Workstations
CPU
CPU
Fitzpatrick Workstation Cluster
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
Disk
Disk
Disk
Disk
Disk
Disk
Disk
Disk
Condor Match Maker
I prefer to run a job submitted by a CCL student.
CPU
CPU
CPU
CPU
CPU
CPU
CPU
Disk
Disk
Disk
Disk
Disk
Disk
Disk
CVRL Research Cluster
CCL Research Cluster
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CPU History
Storage History
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Flocking Between Universities
Wisconsin 1200 CPUs
Purdue A 541 CPUs
Notre Dame 300 CPUs
Purdue B 1016 CPUs
http//www.cse.nd.edu/ccl/operations/condor/
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http//www.cse.nd.edu/ccl/viz
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An Example ofGrid Computing Researchat Notre
Dame
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Scalable I/O for Biometrics

• Computer Vision Research Lab in CSE
• Goal Develop robust algorithms for identifying
  humans from (non-ideal) images.
• Technique Collect lots of images. Think up
  clever new matching function. Compare them.
• How do you test a matching function?
• For a set S of images,
• Compute F(Si,Sj) for all Si and Sj in S.
• Compare the result matrix to known functions.

Credit Patrick Flynn at Notre Dame CSE
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Computing Similarities

1 0 .1 .8 0 .1
1 0 .1 .1 0
1 0 .1 .7
1 0 0
1 .1
1
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A Big Data Problem

• Data Size 10k images of 1MB 10 GB
• Total I/O 10k 10k 2 MB 1/2 100 TB
• Would like to repeat many times!
• In order to execute such a workload, we must be
  careful to partition both the I/O and the CPU
  needs, taking advantage of distributed capacity.

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Conventional Solution
Disk
Disk
Disk
Disk
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
Disk
Disk
Disk
Disk
Disk
Disk
Disk
Disk
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A More Scalable Solution
3. Jobs find nearby data copy, and make full
use before discarding.
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
Disk
Disk
Disk
Disk
Disk
Disk
Disk
Disk
2. Replicate data to many disks.
Result Biometric users can accomplish in three
days what used to take one month!
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The All-Pairs Abstraction

• All-Pairs
• For a set S and a function F
• Compute F(Si,Sj) for all Si and Sj in S.
• The end user provides
• Set S A bunch of files.
• Function F A self-contained program.
• Applies to lots of different problems
• Comparing proteins for interactions.
• Searching documents for similarities.
• Any kind of optimization problems.

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An All-Pairs Facility at Notre Dame
100s-1000s of machines
All Pairs Web Portal
CPU
CPU
CPU
CPU
Disk
Disk
Disk
Disk
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Research Opportunities

• Openings for undergraduate students.
• Research for class credit during the year.
• Research for paycheck during the summer.
• Must enjoy programming and making things work.
• Some Project Ideas
• Build a easy-to-use web front-end for using a
  grid computing system to process biometric data.
• Find a way to get data from your workstation to
  500 other machines as fast as possible.
• Build and manage a filesystem that ties together
  500 disks at once to create one gigantic 20TB
  system.

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For more information...

• To learn more about Condor_at_ND
• http//www.nd.edu/condor
• Prof. Douglas Thain
• dthain_at_nd.edu
• http//www.cse.nd.edu/dthain
• 382 Fitzpatrick Hall