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A Framework for Elastic Execution of Existing MPI Programs

A Framework for Elastic Execution of Existing MPI Programs. Aarthi Raveendran Graduate Student Department Of CSE. Motivation. Emergence of Cloud Computing Including for HPC Applications Key Advantages of Cloud Computing Elasticity (dynamically acquire resources) Pay-as-you model

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A Framework for Elastic Execution of Existing MPI Programs

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  1. A Framework for Elastic Execution of Existing MPI Programs Aarthi Raveendran Graduate Student Department Of CSE

  2. Motivation • Emergence of Cloud Computing • Including for HPC Applications • Key Advantages of Cloud Computing • Elasticity (dynamically acquire resources) • Pay-as-you model • Can be exploited to meet cost and/or time constraints • Existing HPC Applications • MPI-based, use fixed number of nodes • Need to make Existing MPI Applications Elastic A Framework for Elastic Execution of Existing MPI Programs

  3. Outline • Research Objective • Framework Design • Run time support modules • Experimental Platform: Amazon Cloud Services • Applications and Experimental Evaluation • Decision layer Design • Feedback Model • Decision Layer Implementation • Experimental Results for Time and Cost Criteria • Conclusion A Framework for Elastic Execution of Existing MPI Programs

  4. Detailed Research Objective • To make MPI applications elastic • Exploit key advantage of Cloud Computing • Meet user defined time and/or cost constraints • Avoid new programming model or significant recoding • Design a framework for • Decision making • When to expand or contract • Actual Support for Elasticity • Allocation, Data Redistribution, Restart A Framework for Elastic Execution of Existing MPI Programs

  5. Framework components A Framework for Elastic Execution of Existing MPI Programs

  6. Framework design – Approach and Assumptions • Target – Iterative HPC Applications • Assumption : Uniform work done at every iteration • Monitoring at the start of every few iterations of the time-step loop • Checkpointing • Resource allocation and redistribution A Framework for Elastic Execution of Existing MPI Programs

  7. Framework design - A Simple Illustration of the Idea • Progress checked based on current average iteration time • Decision made to stop and restart if necessary • Reallocation should not be done too frequently • If restarting is not necessary, the application continues running A Framework for Elastic Execution of Existing MPI Programs

  8. Framework design – A Simple illustration of the idea A Framework for Elastic Execution of Existing MPI Programs

  9. Framework Design Execution flow A Framework for Elastic Execution of Existing MPI Programs

  10. Other Runtime Steps • Steps taken to perform scaling to a different number of nodes: • Live variables and arrays need to be collected at the master node and redistributed • Read only need not be restored – just retrieve • Application is restarted with each node reading the local portions of the redistributed data. A Framework for Elastic Execution of Existing MPI Programs

  11. Background – Amazon cloud Amazon Elastic compute cloud (EC2) Amazon Simple Storage Service (S3) Provides key - value store Data stored in files Each file restricted to 5 GB Unlimited number of files • Small instances : 1.7 GB of memory, 1 EC2 Compute Unit, 160 GB of local instance storage, 32-bit platform • Large instances : 7.5 GB of memory, 4 EC2 Compute Units, 850 GB of local instance storage, 64-bit platform • On demand , reserved , spot instances A Framework for Elastic Execution of Existing MPI Programs

  12. Runtime support modulesResource allocator • Elastic execution • Input taken from the decision layer on the number of resources • Allocating de- allocating resources in AWS environment • MPI configuration for these instances • Setting up of the MPI cluster • Configuring for password less login among nodes A Framework for Elastic Execution of Existing MPI Programs

  13. Runtime support modules Check pointing and redistribution • Multiple design options feasible with the support available on AWS • Amazon S3 • Unmodified Arrays • Quick access from EC2 instances • Arrays stored in small sized chunks • Remote file copy • Modified arrays (live arrays) • File writes and reads A Framework for Elastic Execution of Existing MPI Programs

  14. Runtime support modules Check pointing and redistribution • Current design • Knowledge of division of the original dataset necessary • Aggregation and redistribution done centrally on a single node • Future work • Source to source transformation tool • Decentralized array distribution schemes A Framework for Elastic Execution of Existing MPI Programs

  15. Experiments • Framework and approach evaluated using • Jacobi • Conjugate Gradient (CG ) • MPICH 2 used • 4, 8 and 16 small instances used for processing the data • Observation made with and without scaling the resources - Overheads 5-10% , which is negligible A Framework for Elastic Execution of Existing MPI Programs

  16. Experiments – Jacobi A Framework for Elastic Execution of Existing MPI Programs

  17. Experiments – Jacobi • Matrix updated at every iteration • Updated matrix collected and redistributed at node change • Worst case total redistribution overhead – less than 2% • Scalable application – performance increases with number of nodes A Framework for Elastic Execution of Existing MPI Programs

  18. Experiments - CG A Framework for Elastic Execution of Existing MPI Programs

  19. Experiments - CG • Single vector which needs to be redistributed • Communication intensive application • Not scalable • Overheads are still low A Framework for Elastic Execution of Existing MPI Programs

  20. Decision Layer - Design • Main Goal – To meet user demands • Constraints – Time and Cost – “Soft” and not “Hard” • Measuring iteration time to determine progress • Measuring communication overhead to estimate scalability • Moving to large – type instances if necessary A Framework for Elastic Execution of Existing MPI Programs

  21. Feedback Model (I) • Dynamic estimation of node count based on inputs : • Input time / Cost • Per iteration time • Current node count • Communication time per iteration • Overhead costs – restart , redistribution, data read A Framework for Elastic Execution of Existing MPI Programs

  22. Feedback Model (II) • Move to large instances if communication time is greater than 30 % of total time • Time Criteria : • New node count found based on the current progress • If time criteria cannot be met with max nodes also, shift to max nodes to get best results • Cost Criteria : • Change at the end of billing cycle • If cost criteria cannot be met with min nodes also, shift to min nodes. A Framework for Elastic Execution of Existing MPI Programs

  23. Decision layer - Implementation • Input : Monitoring Interval, Criteria, Initial Node Count, Input Time / Cost • Output : Total Process Time, Total Cost A Framework for Elastic Execution of Existing MPI Programs

  24. Experiments – Time CriteriaJacobi A Framework for Elastic Execution of Existing MPI Programs

  25. Experiments – Time CriteriaJacobi Input Time (In secs) A Framework for Elastic Execution of Existing MPI Programs

  26. Experiments – Time CriteriaCG A Framework for Elastic Execution of Existing MPI Programs

  27. Experiments – Cost CriteriaJacobi A Framework for Elastic Execution of Existing MPI Programs

  28. Experiments – Cost CriteriaJacobi Input Cost (in $) A Framework for Elastic Execution of Existing MPI Programs

  29. Experiments – Cost CriteriaJacobi A Framework for Elastic Execution of Existing MPI Programs

  30. Experiments – Cost CriteriaCG A Framework for Elastic Execution of Existing MPI Programs

  31. Experiments – Cost CriteriaCG A Framework for Elastic Execution of Existing MPI Programs

  32. Experiments – Cost CriteriaCG A Framework for Elastic Execution of Existing MPI Programs

  33. Conclusion • An approach to make MPI applications elastic and adaptable • An automated framework for deciding the number of instances for execution based on user demands – time / cost • Framework tested using 2 MPI applications showing low overheads during elastic execution and best efforts to meet user constraints A Framework for Elastic Execution of Existing MPI Programs

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