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