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Title: Distributed heterogeneous compute infrastructure for the study of additive manufacturing systems

Abstract

In this work, we present the current status of a large-scale computing framework to address the need of the multidisciplinary effort to study chemical dynamics. Specifically, we are enabling scientists to process and store experimental data, run large-scale computationally expensive high-fidelity physical simulations, and analyze these results using the state-of-the-art data analytics tools, machine learning, and uncertainty quantification methods using heterogeneous computing resources, such as CPU and GPU cluster. The framework can integrate or abstract out multiple domains based on roles. In order to develop this framework, we have leveraged an existing framework coupled with in-house heterogeneous computing resources. We present the results of using this framework on a single metadata triggered workflow to accelerate an additive manufacturing use case.

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1605909
Alternate Identifier(s):
OSTI ID: 1657189
Report Number(s):
PNNL-SA-150139
Journal ID: ISSN 2059-8521
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
MRS Advances
Additional Journal Information:
Journal Volume: 5; Journal Issue: 29-30; Journal ID: ISSN 2059-8521
Publisher:
Materials Research Society (MRS)
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; additive manufacturing; infrastructure; data

Citation Formats

Thomas, Mathew, Schram, Malachi, Fox, Kevin, Strube, Jan, Oblath, Noah S., Rallo, Robert, Kennedy, Zachary C., Varga, Tamas, Battu, Anil K., and Barrett, Christopher A.. Distributed heterogeneous compute infrastructure for the study of additive manufacturing systems. United States: N. p., 2020. Web. https://doi.org/10.1557/adv.2020.103.
Thomas, Mathew, Schram, Malachi, Fox, Kevin, Strube, Jan, Oblath, Noah S., Rallo, Robert, Kennedy, Zachary C., Varga, Tamas, Battu, Anil K., & Barrett, Christopher A.. Distributed heterogeneous compute infrastructure for the study of additive manufacturing systems. United States. https://doi.org/10.1557/adv.2020.103
Thomas, Mathew, Schram, Malachi, Fox, Kevin, Strube, Jan, Oblath, Noah S., Rallo, Robert, Kennedy, Zachary C., Varga, Tamas, Battu, Anil K., and Barrett, Christopher A.. Fri . "Distributed heterogeneous compute infrastructure for the study of additive manufacturing systems". United States. https://doi.org/10.1557/adv.2020.103. https://www.osti.gov/servlets/purl/1605909.
@article{osti_1605909,
title = {Distributed heterogeneous compute infrastructure for the study of additive manufacturing systems},
author = {Thomas, Mathew and Schram, Malachi and Fox, Kevin and Strube, Jan and Oblath, Noah S. and Rallo, Robert and Kennedy, Zachary C. and Varga, Tamas and Battu, Anil K. and Barrett, Christopher A.},
abstractNote = {In this work, we present the current status of a large-scale computing framework to address the need of the multidisciplinary effort to study chemical dynamics. Specifically, we are enabling scientists to process and store experimental data, run large-scale computationally expensive high-fidelity physical simulations, and analyze these results using the state-of-the-art data analytics tools, machine learning, and uncertainty quantification methods using heterogeneous computing resources, such as CPU and GPU cluster. The framework can integrate or abstract out multiple domains based on roles. In order to develop this framework, we have leveraged an existing framework coupled with in-house heterogeneous computing resources. We present the results of using this framework on a single metadata triggered workflow to accelerate an additive manufacturing use case.},
doi = {10.1557/adv.2020.103},
journal = {MRS Advances},
number = 29-30,
volume = 5,
place = {United States},
year = {2020},
month = {2}
}

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Works referenced in this record:

Fiji: an open-source platform for biological-image analysis
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