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Distributed heterogeneous compute infrastructure for the study of additive manufacturing systems

Published online by Cambridge University Press:  07 February 2020

Mathew Thomas ,
Malachi Schram ,
Kevin Fox ,
Jan Strube ,
Noah S. Oblath ,
Robert Rallo ,
Zachary C. Kennedy ,
Tamas Varga ,
Anil K. Battu  and
Christopher A. Barrett
Mathew Thomas*
Affiliation:
Pacific Northwest National Laboratory, Richland, Washington, U.S.A.
Malachi Schram
Affiliation:
Pacific Northwest National Laboratory, Richland, Washington, U.S.A.
Kevin Fox
Affiliation:
Pacific Northwest National Laboratory, Richland, Washington, U.S.A.
Jan Strube
Affiliation:
Pacific Northwest National Laboratory, Richland, Washington, U.S.A.
Noah S. Oblath
Affiliation:
Pacific Northwest National Laboratory, Richland, Washington, U.S.A.
Robert Rallo
Affiliation:
Pacific Northwest National Laboratory, Richland, Washington, U.S.A.
Zachary C. Kennedy
Affiliation:
Pacific Northwest National Laboratory, Richland, Washington, U.S.A.
Tamas Varga
Affiliation:
Pacific Northwest National Laboratory, Richland, Washington, U.S.A.
Anil K. Battu
Affiliation:
Pacific Northwest National Laboratory, Richland, Washington, U.S.A.
Christopher A. Barrett
Affiliation:
Pacific Northwest National Laboratory, Richland, Washington, U.S.A.
*
*(Email: [email protected])
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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.

Keywords

additive manufacturinginfrastructuredata
Type
Articles
Information
MRS Advances , Volume 5 , Issue 29-30: Materials Theory, Computation and Characterization , 2020 , pp. 1547 - 1555
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Copyright
Copyright © Materials Research Society 2020

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References

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