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

Abstract

ABSTRACT We present the current status of a scalable 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 state-of-the-art data analytics, machine learning, and uncertainty quantification methods using heterogeneous computing resources. We present the results of this framework on a single metadata-driven workflow to accelerate an additive manufacturing use-case.

Authors:
; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1605909
Report Number(s):
[PNNL-SA-150139]
[Journal ID: ISSN 2059-8521]
Grant/Contract Number:  
[DE-AC05-76RL01830]
Resource Type:
Accepted Manuscript
Journal Name:
MRS Advances
Additional Journal Information:
[Journal Name: MRS Advances]; Journal ID: ISSN 2059-8521
Country of Publication:
United States
Language:
English

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. doi: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. doi: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. doi:10.1557/adv.2020.103.
@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 = {ABSTRACT We present the current status of a scalable 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 state-of-the-art data analytics, machine learning, and uncertainty quantification methods using heterogeneous computing resources. We present the results of this framework on a single metadata-driven workflow to accelerate an additive manufacturing use-case.},
doi = {10.1557/adv.2020.103},
journal = {MRS Advances},
number = ,
volume = ,
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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