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Parallel Reconstruction of CLEO III Data

Parallel Reconstruction of CLEO III Data. Gregory J. Sharp Christopher D. Jones Wilson Synchrotron Laboratory Cornell University. Outline. Overview of CLEO reconstruction environment. The problems with the old reconstruction system. The solution - finer-grained parallelism.

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Parallel Reconstruction of CLEO III Data

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  1. Parallel Reconstruction ofCLEO III Data Gregory J. Sharp Christopher D. Jones Wilson Synchrotron Laboratory Cornell University

  2. Outline • Overview of CLEO reconstruction environment • The problems with the old reconstruction system • The solution - finer-grained parallelism • The benefits

  3. CLEO IIIReconstruction Environment • Uses a farm of more than 130 Sun Netras • Sun Grid Engine ™ manages CPU allocation • Data read from & written to Objectivity/DB™ • Events must be written to DB in event-number order • Reconstruction rate has to equal average DAQ rate

  4. Former Reconstruction System • Output was written directly to the offline database • ~130 runs may be processed in parallel on the farm • Each run is processed in its entirety by a single CPU • Up to 9 days to reconstruct a single run on a single CPU • All failures required operator and/or DBA intervention

  5. Problems • Need to maximize CPU utilization • Load balancing between farms is difficult • Takes a long time to stop the farm safely • Output of the first few runs must be checked • Debugging reconstruction code

  6. More Problems • Low I/O rates to the database • Many locks held for long periods • Large window for failures to occur • Failure leaves database in an invalid state • No automation of failure detection and recovery

  7. The Solution • Split each run into roughly equal-sized chunks • Assign each chunk to a CPU • Save sub-job output in intermediate binary files in event-number order • Once all sub-jobs complete, collate binary files into database in event-number order

  8. The Job Manager • The JM submits all the reconstruction sub-jobs and monitors their progress, retrying failures • Once all reconstruction completes successfully the JM starts the collation sub-job • Once collation completes successfully the JM starts the merge histogram sub-job • Can be restarted at any time if it dies

  9. Structure Diagram

  10. Automation • JM restarts subjobs with transient failures • Runs may be submitted automatically when SGE queue is (almost) empty • A cron job generates status web pages

  11. Implementation Details • Written in Perl • Uses Sun Grid Engine to submit and track jobs • Uses CLEO III software infrastructure for reconstruction and population • Uses PAW for merging histograms

  12. Benefits • Less operator intervention/management • Faster debugging • Increased CPU utilization, which offsets extra CPU use • 20% Faster completion of reconstruction • Just-in-time pre-staging of data from HSM file system • The January ice storm

  13. Future Steps • Automate staging of data to cache disks • Automate posting of staged runs info to Reconstruction

  14. Conclusions • Multiple file formats made this possible • Substantial productivity gains • Higher utilization of computing resources • For more details: • http://www.lepp.cornell.edu/~gregor/projects/parallelpass2

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