Is 99% Utilization of a Supercomputer a Good Thing? Scheduling in Context: User Utility Functions - PowerPoint PPT Presentation

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Is 99% Utilization of a Supercomputer a Good Thing? Scheduling in Context: User Utility Functions

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Title: Is 99% Utilization of a Supercomputer a Good Thing? Scheduling in Context: User Utility Functions


1
Is 99 Utilization of a Supercomputer a Good
Thing?Scheduling in Context User Utility
Functions
  • Cynthia Bailey Lee
  • Department of Computer Science and Engineering
    and
    San Diego Supercomputer Center
  • University of California, San Diego
  • November 13, 2006

2
What is a Utility Function?
Outline What is a Utility Function?
Metrics Metric Pitfalls
Other factors coordinate with other grid sites
or sensors, paper deadlines, weather and
hurricane prediction,
3
Some Proposed Models
What is a Utility Function? Proposed
Models Real Functions Scheduling
My favorite
4
Real Functions
What is a Utility Function? Proposed
Models Real Functions Scheduling
  • Randomly selected SDSC users provided these
    functions for jobs they were submitting
  • Utility is in terms of the SDSC charge unit
    (SU)

5
More Real Functions
What is a Utility Function? Proposed
Models Real Functions Scheduling
6
Parallel Job Scheduling Explicitly by Utility
Function
What is a Utility Function? Proposed
Models Real Functions Scheduling
?
Finding the best solution is NP-hard
  • Tennis Court Scheduling (Human-powered
    scheduling)
  • Still practiced occasionally at most centers
    (officially and not) -- a phone call to sysadmins
    gets a job a reservation or to the front of the
    queue)
  • Custom Heuristics
  • Sort by current value, a combination of start
    value and slope Chun and Culler 02, Irwin,
    Grit, Chase 04
  • Linear Solver / CPLEX

7
Comparing Schedulers
What is a Utility Function? Proposed
Models Real Functions Scheduling
Expansion Factor 25/50/75 ile Aggregate Utility rounded, x107 Time to Solution
EASY 0/0/3307 4.33 0.7 s
Conservative Backfilling 0/82/9071 4.24 1.9 s
Priority Queues EASY 0/2883/28124 4.20 0.8 s
Genetic Algorithm 0/289/6582 110. 886 s
Expansion Factor 25/50/75 ile Aggregate Utility rounded, x107
EASY 0/0/3307 4.33
Conservative Backfilling 0/82/9071 4.24
Priority Queues EASY 0/2883/28124 4.20
For 500 scheduling rounds (need to do a round
when a job arrives or ends). All timings done
with a (not particularly optimized) modular
scheduling simulator framework in Python (email
me for code).
8
Questions
  • For more information, see paper
  • Gathering real users utility functions
  • Lee, Cynthia Bailey and Allan Snavely. "On the
    User-Scheduler Dialogue Studies of User-Provided
    Runtime Estimates and Utility Functions."
    International Journal of High Performance
    Computing Applications, vol. 20, pp.495-506,
    2006.
  • Contact Cynthia Lee CL_at_SDSC.EDU
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