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Large Scale Debugging of Parallel Tasks with AutomaDeD
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
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.
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