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A Framework for Elastic Execution of Existing MPI Programs

This research presents a framework aimed at making existing MPI-based high-performance computing (HPC) applications elastic by leveraging cloud computing. It addresses the need for dynamic resource allocation, allowing applications to scale based on user-defined time and cost constraints without necessitating significant code changes. The framework includes runtime support modules for resource allocation, data redistribution, and decision-making to optimize performance. Evaluations on Amazon EC2 demonstrate the framework's effectiveness, achieving negligible overhead while executing iterative applications like Jacobi and Conjugate Gradient.

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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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