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Title: Python-basedin situanalysis and visualization

Abstract

© 2018 Association for Computing Machinery. This work focuses on enabling the use of Python-based methods for the purpose of performing in situ analysis and visualization. This approach facilitates access to and use of a rapidly growing collection of Python-based, third-party libraries for analysis and visualization, as well as lowering the barrier to entry for user-written Python analysis codes. Beginning with a simulation code that is instrumented to use the SENSEI in situ interface, we present how to couple it with a Python-based data consumer, which may be run in situ, and in parallel at the same concurrency as the simulation. We present two examples that demonstrate the new capability. One is an analysis of the reaction rate in a proxy simulation of a chemical reaction on a 2D substrate, while the other is a coupling of an AMR simulation to Yt, a parallel visualization and analysis library written in Python. In the examples, both the simulation and Python in situ method run in parallel on a large-scale HPC platform.

Authors:
 [1];  [1];  [1];  [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1602814
DOE Contract Number:  
AC02-05CH11231
Resource Type:
Conference
Resource Relation:
Conference: Proceedings of the Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization - ISAV '18
Country of Publication:
United States
Language:
English

Citation Formats

Loring, Burlen, Myers, Andrew, Camp, David, and Bethel, E Wes. Python-basedin situanalysis and visualization. United States: N. p., 2018. Web. doi:10.1145/3281464.3281465.
Loring, Burlen, Myers, Andrew, Camp, David, & Bethel, E Wes. Python-basedin situanalysis and visualization. United States. doi:10.1145/3281464.3281465.
Loring, Burlen, Myers, Andrew, Camp, David, and Bethel, E Wes. Mon . "Python-basedin situanalysis and visualization". United States. doi:10.1145/3281464.3281465. https://www.osti.gov/servlets/purl/1602814.
@article{osti_1602814,
title = {Python-basedin situanalysis and visualization},
author = {Loring, Burlen and Myers, Andrew and Camp, David and Bethel, E Wes},
abstractNote = {© 2018 Association for Computing Machinery. This work focuses on enabling the use of Python-based methods for the purpose of performing in situ analysis and visualization. This approach facilitates access to and use of a rapidly growing collection of Python-based, third-party libraries for analysis and visualization, as well as lowering the barrier to entry for user-written Python analysis codes. Beginning with a simulation code that is instrumented to use the SENSEI in situ interface, we present how to couple it with a Python-based data consumer, which may be run in situ, and in parallel at the same concurrency as the simulation. We present two examples that demonstrate the new capability. One is an analysis of the reaction rate in a proxy simulation of a chemical reaction on a 2D substrate, while the other is a coupling of an AMR simulation to Yt, a parallel visualization and analysis library written in Python. In the examples, both the simulation and Python in situ method run in parallel on a large-scale HPC platform.},
doi = {10.1145/3281464.3281465},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}

Conference:
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Works referenced in this record:

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conference, January 2017

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