Performance characterization of irregular I/O at the extreme scale

Herbein, Stephen; McDaniel, Sean; Podhorszki, Norbert; Logan, Jeremy S.; Klasky, Scott A.; Taufer, Michela

doi:10.1016/j.parco.2015.10.009

Title: Performance characterization of irregular I/O at the extreme scale

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Abstract

Here, this paper reports our experience with irregular I/O and describes lessons learned when running applications with such I/O on supercomputers at the extreme scale. Specifically, we study how irregularities in I/O patterns (i.e., irregular amount of data written per process at each I/O step) in scientific simulations can cause increasing I/O times and substantial loss in scalability. To this end, we quantify the impact of irregular I/O patterns on the I/O performance of scientific applications at the extreme scale by statistically modeling the irregular I/O behavior of two scientific applications: the Monte Carlo application QMCPack and the adaptive mesh refinement application ENZO. For our testing, we feed our model into I/O kernels of two well-known I/O data models (i.e., ADIOS and HDF) to measure the performance of the two applications’ I/O under different I/O settings. Empirically, we show how the growing data sizes and the irregular I/O patterns in these applications are both relevant factors impacting performance.

Authors:

Herbein, Stephen ^[1]; McDaniel, Sean ^[1];

^[2]; Logan, Jeremy S. ^[2];

^[2];

^[1]

Univ. of Delaware, Newark, DE (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Publication Date:: Sat Oct 24 00:00:00 EDT 2015

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)

Sponsoring Org.:: USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF)

OSTI Identifier:: 1559754

Alternate Identifier(s):: OSTI ID: 1251771

Grant/Contract Number:: AC05-00OR22725; CCF 1318445; AAC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: Parallel Computing

Additional Journal Information:: Journal Volume: 51; Journal Issue: C; Journal ID: ISSN 0167-8191

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; Exascale; Irregular I/O; QMCPack; ENZO; ADIOS; HDF5

Citation Formats


                    Herbein, Stephen, McDaniel, Sean, Podhorszki, Norbert, Logan, Jeremy S., Klasky, Scott A., and Taufer, Michela. Performance characterization of irregular I/O at the extreme scale.  United States: N. p., 2015. 
Web.  doi:10.1016/j.parco.2015.10.009.

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                    Herbein, Stephen, McDaniel, Sean, Podhorszki, Norbert, Logan, Jeremy S., Klasky, Scott A., & Taufer, Michela. Performance characterization of irregular I/O at the extreme scale.  United States.  https://doi.org/10.1016/j.parco.2015.10.009

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                    Herbein, Stephen, McDaniel, Sean, Podhorszki, Norbert, Logan, Jeremy S., Klasky, Scott A., and Taufer, Michela. Sat .  
"Performance characterization of irregular I/O at the extreme scale".  United States.  https://doi.org/10.1016/j.parco.2015.10.009.  https://www.osti.gov/servlets/purl/1559754.

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@article{osti_1559754,

  title        = {Performance characterization of irregular I/O at the extreme scale},

  author       = {Herbein, Stephen and McDaniel, Sean and Podhorszki, Norbert and Logan, Jeremy S. and Klasky, Scott A. and Taufer, Michela},

  abstractNote = {Here, this paper reports our experience with irregular I/O and describes lessons learned when running applications with such I/O on supercomputers at the extreme scale. Specifically, we study how irregularities in I/O patterns (i.e., irregular amount of data written per process at each I/O step) in scientific simulations can cause increasing I/O times and substantial loss in scalability. To this end, we quantify the impact of irregular I/O patterns on the I/O performance of scientific applications at the extreme scale by statistically modeling the irregular I/O behavior of two scientific applications: the Monte Carlo application QMCPack and the adaptive mesh refinement application ENZO. For our testing, we feed our model into I/O kernels of two well-known I/O data models (i.e., ADIOS and HDF) to measure the performance of the two applications’ I/O under different I/O settings. Empirically, we show how the growing data sizes and the irregular I/O patterns in these applications are both relevant factors impacting performance.},

  doi          = {10.1016/j.parco.2015.10.009},

  journal      = {Parallel Computing},

  number       = C,

  volume       = 51,

  place        = {United States},

  year         = {Sat Oct 24 00:00:00 EDT 2015},

  month        = {Sat Oct 24 00:00:00 EDT 2015}

}

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