The Grainger College of Engineering
Parallel Programming Laboratory

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
Pulication Type: Paper
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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.

Research Areas

Text Ref

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

BibTex

@inproceedings{NoiseMinerHIPS08,
  author = "Isaac Dooley and Chao Mei and Laxmikant V. Kale",
  title = "NoiseMiner: An Algorithm for Scalable Automatic Computational Noise and Software Interference Detection",
  booktitle = "Proceedings of HIPS Workshop at IEEE International Parallel and Distributed Processing Symposium 2008",
  year = "2008",
}