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MW: A framework to support Master Worker Applications

Sanjeev R. Kulkarni Computer Sciences Department University of Wisconsin-Madison sanjeevk@cs.wisc.edu. MW: A framework to support Master Worker Applications. Outline of the talk. Introduction to MW Architecture of MW How to use MW? Records shattered by MW! Extensions. What is MW?.

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MW: A framework to support Master Worker Applications

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  1. Sanjeev R. Kulkarni Computer Sciences Department University of Wisconsin-Madison sanjeevk@cs.wisc.edu MW: A framework to support Master Worker Applications

  2. Outline of the talk • Introduction to MW • Architecture of MW • How to use MW? • Records shattered by MW! • Extensions

  3. What is MW? • MW = Master Worker • Object Oriented, Fault Tolerant framework for Master-Worker Applications • Can run on a variety of resource managers like Condor, PVM, Globus, ...

  4. MW • Object Oriented • MW is a set of C++ base classes • Users write a few virtual functions • Fault Tolerant • Handles workers joining/leaving • Handles suspended/resumed workers • Checkpointing

  5. MW Framework Application MWLayer Resource Management and Communication Layer E.g. Condor, PVM, Globus,...

  6. Why use MW? • Handles communication layer • Handles resource Management • Useful especially in an opportunistic environment like Condor • Same application code can run on various resource managers

  7. MW Layer • Three main entities • MWDriver • corresponds to the (Tyrant) Master • MWWorker • corresponds to the (exploited) Worker • MWTask • The work itself!

  8. MWDriver: The Master • Setup • Manages workers joining/leaving • Deals with HostSuspend/HostDelete • Maintains lists of tasks • ToDo, Done, Running • Acts on completed tasks • Checkpointing

  9. MWWorker: The Worker • Executes the given task • Unpacks the task got from Master • Executes the task • Packs results and sends back to Master

  10. MWTask: The Work • Definition of a Unit of work • Holds work to be done and results • Follows the path Master-Worker-Master

  11. Master To Do Global Data T1 T2 T3 ... Done Running Workers

  12. W1 Master To Do Global Data T2 T3 T4 ... Done Running T1 Wid 1 Workers

  13. W1 Master To Do Global Data T5 T6 T7 ... Done Running T1 T2 T3 T4 Wid 1 Wid 4 Wid 2 Wid 3 W4 W2 W3 Workers

  14. W1 Master To Do Global Data T6 T7 T8 ... Done Running T3 T1 T2 T5 T4 Wid 1 Wid 4 Wid 2 Wid 3 W4 W2 W3 Workers

  15. W1 Master To Do Global Data T2 T6 T7 ... Done Running T3 T1 T5 T4 Wid 1 Wid 4 Wid 2 Wid 3 W4 W2 W3 Workers

  16. Intelligent Scheduling of Tasks • Some tasks may be harder • Some machines may be faster • Task ordering based on user defined MWKey • Machine ordering based on Resource Manager information • e.g. Condor ClassAds

  17. Checkpointing • Save master state in case of failure • Automatic restart from checkpoint file in case of master failure • User controlled checkpoint frequency • Users need to implement two additional functions to take advantage of these.

  18. How can you use MW? • MWDriver’s virtual functions • get_user_info() - Processes arguments and does basic setup • setup_initial_tasks() - Fills in the ToDo list • pack_worker_init_data() - packs init data for worker • act_on_completed_tasks() - called every time a task completes. Optional.

  19. Using MW (cont.) • MWWorker • unpack_init_data() - unpack the init data sent by the master • execute_task() - execute a task and fill in the results • MWTask • pack_work(), unpack_work() • pack_results(), unpack_results()

  20. Resource Management and Communication Layer • Presently two implementations exist • MW-PVM • Communication :PVM • Resource Manager : Condor-PVM • MW-File • Communication : Files • Resource Manager : Condor

  21. MW-Independent • Single process version • master • single worker • sends and receives become memcpy • No changes in source! • Why? • Faster debugging

  22. Data Management • Exploitation of available memory as compared to CPU cycles • Aims to reduce access to storage site by caching data on network • Aimed at applications involving extremely large data sets

  23. Data Manager W2 W1 W3 Data Manager • Partitions large data sets into chunks • Workers work on chunks allocated by manager Storage Device Memory

  24. World Record Spree!! • Stochastic Linear Programming • “Record breaking” problem of over 8.5M rows and 35M columns solved • Quadratic Assignment Problem • nug27 in a little more than two days! • Jeff will talk more in his talk

  25. Extensions • More Resource Manager ports • Globus • A thread based version for SMPs • <put your suggestions here>

  26. Scalability Issues • Scales linearly with respect to the number of workers • OK for 200 workers but for 1000? • Solution • Hierarchical MW?

  27. More about MW • http://www.cs.wisc.edu/condor/mw • Demos of MW in action using Condor pool tomorrow in Room 3397

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