@inproceedings{2b2e059cedd4451296f86bb57da1d120,
title = "Automatic characterization of hpc job parallel filesystem I/O patterns",
abstract = "As part of the NSF funded XMS project, we are actively researching automatic detection of poorly performing HPC jobs. To aid the analysis we have generated a taxonomy of the temporal I/O patterns for HPC jobs. In this paper we describe the design of temporal pattern characterization algorithms for HPC job I/O. We have implemented these algorithms in the Open XDMoD job analysis framework. These I/O classifications include periodic patterns and a variety of characteristic non-periodic patterns. We present an analysis of the I/O patterns observed on the /scratch filesystem on an academic HPC cluster. This type of analysis can be extended to other HPC usage data such as memory, CPU and interconnect usage. Ultimately this analysis will be used to improve HPC throughput and efficiency by, for example, automatically identifying anomalous HPC jobs.",
keywords = "I/O, Pattern analysis, Performance",
author = "White, \{Joseph P.\} and Martins Innus and Kofke, \{Alexander D.\} and Jones, \{Matthew D.\} and DeLeon, \{Robert L.\} and Furlani, \{Thomas R.\}",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 2018 Practice and Experience in Advanced Research Computing Conference: Seamless Creativity, PEARC 2018 ; Conference date: 22-07-2017 Through 26-07-2017",
year = "2018",
month = jul,
day = "22",
doi = "10.1145/3219104.3219121",
language = "English",
isbn = "9781450364461",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery ",
booktitle = "Practice and Experience in Advanced Research Computing 2018",
address = "United States",
}