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Title: A Global Climate Model Agent for High Spatial and Temporal Resolution Data

Abstract

Fine cell granularity in modern climate models can produce terabytes of data in each snapshot, causing significant I/O overhead. To address this issue, a method of reducing the I/O latency of high-resolution climate models by identifying and selectively outputting regions of interest is presented. Working with a Global Cloud Resolving Model and running with up to 10240 processors on a Cray XE6, this method provides significant I/O bandwidth reduction depending on the frequency of writes and size of the region of interest. The implementation challenges of determining global parameters in a strictly core-localized model and properly formatting output files that only contain subsections of the global grid are addressed, as well as the overall bandwidth impact and benefits of the method. The gains in I/O throughput provided by this method allow dual output rates for high-resolution climate models: a low-frequency global snapshot as well as a high-frequency regional snapshot when events of particular interest occur.

Authors:
; ; ; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1176822
Report Number(s):
PNNL-SA-94300
KJ0403000
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: International Journal of High Performance Computing Applications, 29(1):107-116
Country of Publication:
United States
Language:
English
Subject:
global cloud resolving model; software agent; Hoshen-Kopelman algorithm; parallel clustering

Citation Formats

Wood, Lynn S., Daily, Jeffrey A., Henry, Michael J., Palmer, Bruce J., Schuchardt, Karen L., Dazlich, Donald A., Heikes, Ross P., and Randall, David. A Global Climate Model Agent for High Spatial and Temporal Resolution Data. United States: N. p., 2015. Web. doi:10.1177/1094342013518808.
Wood, Lynn S., Daily, Jeffrey A., Henry, Michael J., Palmer, Bruce J., Schuchardt, Karen L., Dazlich, Donald A., Heikes, Ross P., & Randall, David. A Global Climate Model Agent for High Spatial and Temporal Resolution Data. United States. doi:10.1177/1094342013518808.
Wood, Lynn S., Daily, Jeffrey A., Henry, Michael J., Palmer, Bruce J., Schuchardt, Karen L., Dazlich, Donald A., Heikes, Ross P., and Randall, David. Sun . "A Global Climate Model Agent for High Spatial and Temporal Resolution Data". United States. doi:10.1177/1094342013518808.
@article{osti_1176822,
title = {A Global Climate Model Agent for High Spatial and Temporal Resolution Data},
author = {Wood, Lynn S. and Daily, Jeffrey A. and Henry, Michael J. and Palmer, Bruce J. and Schuchardt, Karen L. and Dazlich, Donald A. and Heikes, Ross P. and Randall, David},
abstractNote = {Fine cell granularity in modern climate models can produce terabytes of data in each snapshot, causing significant I/O overhead. To address this issue, a method of reducing the I/O latency of high-resolution climate models by identifying and selectively outputting regions of interest is presented. Working with a Global Cloud Resolving Model and running with up to 10240 processors on a Cray XE6, this method provides significant I/O bandwidth reduction depending on the frequency of writes and size of the region of interest. The implementation challenges of determining global parameters in a strictly core-localized model and properly formatting output files that only contain subsections of the global grid are addressed, as well as the overall bandwidth impact and benefits of the method. The gains in I/O throughput provided by this method allow dual output rates for high-resolution climate models: a low-frequency global snapshot as well as a high-frequency regional snapshot when events of particular interest occur.},
doi = {10.1177/1094342013518808},
journal = {International Journal of High Performance Computing Applications, 29(1):107-116},
number = ,
volume = ,
place = {United States},
year = {Sun Feb 01 00:00:00 EST 2015},
month = {Sun Feb 01 00:00:00 EST 2015}
}