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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