NoiseMiner: An Algorithm for Scalable Automatic Computational Noise and Software Interference Detection
International Workshop on High-Level Parallel Programming Models and Supportive Environments at IPDPS (HIPS) 2008
Publication Type: Paper
Repository URL: 08_HIPS_NoiseMiner
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.
TextRef
Isaac Dooley, Chao Mei, Laxmikant V. Kale, NoiseMiner: An Algorithm
for Scalable Automatic Computational Noise and Software Interference
Detection, To appear in Proceedings of HIPS Workshop at IEEE
International Parallel and Distributed Processing Symposium 2008
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