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PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20111116T233000Z
DTEND:20111117T000000Z
LOCATION:TCC 304
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: Developing correct HPC applications continues to be a challenge as the number of cores increases in today's largest systems. Most existing debugging techniques perform poorly at large scales and do not automatically localize the parts of the parallel application in which errors occur. We present novel highly efficient techniques that facilitate the process of debugging large-scale parallel applications. Our approach extends our previous work, AutomaDeD, in three areas to isolate anomalous tasks in a scalable manner: (i) we efficiently compare elements of graph models (used in AutomaDeD to model parallel tasks) using precomputed lookup-tables and by pointer comparison; (ii) we compress graph models so that comparison between models involves many fewer elements; (iii) we use scalable sampling-based clustering and nearest-neighbor techniques to isolate anomalous tasks. Our evaluation with fault injections shows that AutomaDeD scales well to thousands of tasks and that it can find anomalous tasks in under 5 seconds.
SUMMARY:Large Scale Debugging of Parallel Tasks with AutomaDeD
PRIORITY:3
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