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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. 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

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  1. Predictions for Parallel Applications and Systems Sathish Vadhiyar Grid Applications Research Laboratory (GARL)

  2. GARL Research • Grid Applications • Climate Modeling • Gene Mutations • Performance Modeling • Rescheduling • Others • Prediction of queue wait times

  3. GARL Research • Grid Applications • Climate Modeling • Gene Mutations • Performance Modeling • Rescheduling • Others • Prediction of queue wait times

  4. 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

  5. Application Progress System 1 System 2 Storage

  6. 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

  7. Illustration

  8. 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

  9. 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

  10. 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

  11. GARL Research • Grid Applications • Climate Modeling • Gene Mutations • Performance Modeling • Rescheduling • Others • Prediction of queue wait times

  12. 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

  13. 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

  14. Illustration Method 1 Method 2 Metric 3 value of Method 1 < Metric 3 value of Method 2 i.e. Method 1 is better

  15. 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

  16. Illustration:Prediction errors versus absolute wait times y1 x1, y1 (A-P)/A% f(x) x2, y2 Wait times

  17. Reality??

  18. 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

  19. GARL Research • Grid Applications • Climate Modeling • Gene Mutations • Performance Modeling • Rescheduling • Others • Prediction of queue wait times

  20. 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

  21. What is happening… Proc 0 Proc 1 Proc 2 Proc 3 Proc 4 Phase 1 Phase 2

  22. 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

  23. Advantages

  24. 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

  25. Questions ???? Thank U http://garl.serc.iisc.ernet.in

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