SCHEDULE: NOV 12-18, 2011

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Large Scale Debugging of Parallel Tasks with AutomaDeD

SESSION: Debugging

EVENT TYPE: Paper

TIME: 3:30PM - 4:00PM

AUTHOR(S):Ignacio Laguna, Todd Gamblin, Bronis R. de Supinski, Saurabh Bagchi, Greg Bronevetsky, Dong H. Ahn, Martin Schulz, Barry Rountree

ROOM:TCC 304

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.

Chair/Author Details:

Ignacio Laguna - Purdue University

Todd Gamblin - Lawrence Livermore National Laboratory

Bronis R. de Supinski - Lawrence Livermore National Laboratory

Saurabh Bagchi - Purdue University

Greg Bronevetsky - Lawrence Livermore National Laboratory

Dong H. Ahn - Lawrence Livermore National Laboratory

Martin Schulz - Lawrence Livermore National Laboratory

Barry Rountree - Lawrence Livermore National Laboratory

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The full paper can be found in the ACM Digital Library