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Introduction to Scalable Programming with Makeflow and Work Queue

This tutorial introduces scalable programming concepts using Makeflow and Work Queue, designed for those looking to enhance their computational simulations. Learn how to optimize your applications to run efficiently across multiple machines, transitioning from traditional methods to leveraging cloud and grid computing. The workshop will address key topics such as parallelism, fault tolerance, and resource management, demonstrating how to use Makeflow for workflow organization. Join us to explore practical applications and hands-on exercises to boost your computational capabilities.

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Introduction to Scalable Programming with Makeflow and Work Queue

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  1. Introduction to Scalable Programming using Makeflow and Work Queue Dinesh Rajan and Mike Albrecht University of Notre Dame October 24 and November 7, 2012

  2. Go to: http://nd.edu/~ccl Click “Tutorial: Introduction to Scalable Programming”

  3. I have a standard, debugged, trusted application that runs on my laptop. One simulation runs in an hour. I have to run 100. Then I have to analyze the results, tweak the simulation, and run 100 more. Can I get a single result faster? Can I get more results in the same time?

  4. Last year, I heard about this grid thing. This year, I heard about this cloud thing. What do I do next?

  5. Should I port my program to MPI or Hadoop? Learn C / Java Learn MPI / Hadoop Re-architect Re-write Re-test Re-debug Re-certify

  6. What if my application looks like this?

  7. I can get as many machineson the cloud as I want!How do I organize my applicationto run on those machines?

  8. Makeflow:A Portable Workflow System

  9. An Old Idea: Makefiles part1 part2 part3: input.data split.py ./split.py input.data out1: part1 mysim.exe ./mysim.exe part1 >out1 out2: part2 mysim.exe ./mysim.exe part2 >out2 out3: part3 mysim.exe ./mysim.exe part3 >out3 result: out1 out2 out3 join.py ./join.py out1 out2 out3 > result

  10. Makeflow = Make + Workflow • Provides portability across batch systems. • Enable parallelism (but not too much!) • Fault tolerance at multiple scales. • Data and resource management. Makeflow Local Condor SGE Work Queue http://www.nd.edu/~ccl/software/makeflow

  11. Makeflow Language - Rules part1 part2 part3: input.data split.py ./split.py input.data out1: part1 mysim.exe ./mysim.exe part1 >out1 out2: part2 mysim.exe ./mysim.exe part2 >out2 out3: part3 mysim.exe ./mysim.exe part3 >out3 result: out1 out2 out3 join.py ./join.py out1 out2 out3 > result • Each rule specifies: • a set of target files to create; • a set of source files needed to create them; • a commandthat generates the target files from the source files. out1 :part1 mysim.exe mysim.exe part1 > out1

  12. You must stateall the filesneeded by the command.

  13. Makeflow + Batch System CRCSGE Cluster Private Cluster Makefile makeflow –T sge Work Queue Makeflow Work Queue Campus Condor Pool Public Cloud Provider makeflow –T condor Local Files and Programs

  14. Drivers • Local • Condor • SGE • Batch • Hadoop • WorkQueue • Torque • MPI-Queue • XGrid • Moab

  15. How to run a Makeflow • Run a workflow locally (multicore?) • makeflow -T local sims.mf • Clean up the workflow outputs: • makeflow –c sims.mf • Run the workflow on SGE: • makeflow –T sgesims.mf

  16. Hands On http://nd.edu/~ccl/software/tutorials/ndtut12/mf-tutorial.php

  17. Practice Problems http://nd.edu/~ccl/software/tutorials/ndtut12/mf-hw.php • Construct a makeflow to render a short movie featuring a Rubik’s cube • Launch the makeflow on both your laptop and SGE • Consider ways you might use Makeflow for your research

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