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Parallel and Distributed Processing Symposium, International (2008)
Miami, FL, USA
Apr. 14, 2008 to Apr. 18, 2008
ISBN: 978-1-4244-1693-6
pp: 1-8
Chao Mei , University of Illinois at Urbana-Champaign, Department of Computer Science, USA
Laxmikant Kale , University of Illinois at Urbana-Champaign, Department of Computer Science, USA
Isaac Dooley , University of Illinois at Urbana-Champaign, Department of Computer Science, USA
ABSTRACT
This paper describes a new scalable stream mining algorithm called NOISEMINER that analyzes parallel application traces to detect computational noise, operating system interference, software interference, or other irregularities in a parallel application’s performance. The algorithm detects these occurrences of noise during real application runs, whereas standard techniques for detecting noise use carefully crafted test programs to detect the problems. This paper concludes by showing the output of NOISEMINER for a real-world case in which 6 ms delays, caused by a bug in an MPI implementation, significantly limited the performance of a molecular dynamics code on a new supercomputer.
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CITATION
Chao Mei, Laxmikant Kale, Isaac Dooley, "NOISEMINER: An algorithm for scalable automatic computational noise and software interference detection", Parallel and Distributed Processing Symposium, International, vol. 00, no. , pp. 1-8, 2008, doi:10.1109/IPDPS.2008.4536186
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