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Predictions for Parallel Applications and Systems. Sathish Vadhiyar Grid Applications Research Laboratory (GARL). GARL Research. Grid Applications Climate Modeling Gene Mutations Performance Modeling Rescheduling Others Prediction of queue wait times. GARL Research. Grid Applications

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predictions for parallel applications and systems

Predictions for Parallel Applications and Systems

Sathish Vadhiyar

Grid Applications Research Laboratory (GARL)

garl research
GARL Research
  • Grid Applications
    • Climate Modeling
    • Gene Mutations
  • Performance Modeling
  • Rescheduling
  • Others
    • Prediction of queue wait times
garl research1
GARL Research
  • Grid Applications
    • Climate Modeling
    • Gene Mutations
  • Performance Modeling
  • Rescheduling
  • Others
    • Prediction of queue wait times
rescheduling
Rescheduling
  • The base is a parallel checkpointing library called SRS
  • Checkpointing? – storing application’s state so as to continue from the previous state after interruption
  • Interruption either by a scheduler or system faults
  • SRS allows processor reconfiguration
application progress
Application Progress

System 1

System 2

Storage

optimal checkpoint interval
Optimal Checkpoint Interval
  • Storing checkpoints periodically will help in fault-tolerance
  • How periodic?
  • What is the optimal checkpoint interval?
    • More checkpointing will lead to increased checkpoint overhead
    • Less checkpointing frequency will lead to increase times for recovery from failures
dynamic determination of optimal checkpointing intervals
Dynamic Determination of Optimal Checkpointing Intervals
  • Start the application on a set of resources
  • Predict the next failure on the set of resources
  • Checkpoint “just before” the next failure
  • The prediction has to be really accurate
  • But no prediction can be 100% accurate
probability distribution of failures
Probability Distribution of Failures
  • Use a probability distribution of failures on the resources
  • Need to know: The next time of failure with x% certainty
  • But more certainty is also not good
markov chains
Markov Chains

For parallel M-M checkpointing

In SRS, there is almost no system down phase

For sequential applications

In SRS, transition from state 0 can lead to many states

garl research2
GARL Research
  • Grid Applications
    • Climate Modeling
    • Gene Mutations
  • Performance Modeling
  • Rescheduling
  • Others
    • Prediction of queue wait times
motivation for queue wait times
Motivation for Queue Wait Times
  • A Grid consisting of number of batch queues
  • A meta system that will:
    • predict the wait times and execution times of jobs
    • Decide which queue is “most suitable” for the job
what is a good predictor
What is a good predictor?
  • There are number of prediction strategies
  • Evaluating a predictor’s goodness:
    • Mean Absolute Percentage Error (MAPE)
    • Upper bound for actual/predicted
    • Average of (actual-predicted) [absolute error]
    • Absolute error/actual wait time [relative error]
    • Average error/average queue wait time
    • Coefficient of correlation
  • Each of these metrics has flaws
illustration1
Illustration

Method 1

Method 2

Metric 3 value of Method 1 < Metric 3 value of Method 2

i.e. Method 1 is better

our goals
Our goals
  • To define useful metrics that can clearly say whether a method is “good” or “bad”
  • Goodness of predictors
    • In terms of absolute wait times
    • In terms of execution times
    • In terms of resource demand
what we want to do
What we want to do…
  • Define metrics that can evaluate a method in the “absolute” sense, not “comparative” sense
    • Stare at a single graph and ask “Is this graph good” as much as possible
  • In some cases, it may just not be possible
    • Use comparisons
  • Evaluate the existing methods on these sets of metrics
  • Come up with a method that performs the best in terms of all of the defined metrics
garl research3
GARL Research
  • Grid Applications
    • Climate Modeling
    • Gene Mutations
  • Performance Modeling
  • Rescheduling
  • Others
    • Prediction of queue wait times
motivation
Motivation
  • Certain large computational phases of climate modeling (CCSM) are done only by some processors
  • Load balancing – offload work from these processors to other processors
    • Increased processor utilization
    • Decreased execution time
  • How much offloading?
    • Need to predict workload based on previous computations
what is happening
What is happening…

Proc 0

Proc 1

Proc 2

Proc 3

Proc 4

Phase 1

Phase 2

what should happen
What should happen…

Proc 0

Proc 1

Proc 2

Proc 3

Proc 4

Phase 1

Phase 2

For this, we need to know the workload in phase 1

We predict the workload based on previous time steps

garlians
GARLians
  • Yadnyesh Joshi (M.Sc)
  • Karthikeyan Raman (M.Tech, jointly with Prof. Govindarajan)
  • H.A. Sanjay (Ph.D, jointly with Prof. Ravi Nanjundiah, CAOS)
  • Sivagama Sundari (Ph.D)
  • Ashish Srivatsava (Project Assistant)
  • Alumni
    • 1 student intern from INSA, Lyon, France
    • Summer interns
    • Project assistants
    • 2 M.Scs
questions
Questions ????

Thank U

http://garl.serc.iisc.ernet.in

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