Distributed heterogeneous compute infrastructure for the study of additive manufacturing systems. (2020)

• Record Type: Journal Article
• Authors: Thomas, Mathew; Schram, Malachi; Fox, Kevin; Strube, Jan; Oblath, Noah S.; Rallo, Robert; Kennedy, Zachary C.; Varga, Tamas; Battu, Anil K.; Barrett, Christopher A.
• Is Part Of: MRS advances. Volume 5:Number 29/30(2020)
• Page Start: 1547
• Page End: 1555
• Publication Date: 2020
• Subjects: additive manufacturing, -- infrastructure, -- data; Electrical engineering -- Congresses; Physics -- Congresses; Materials -- Research -- Congresses; Materials science -- Congresses; 620.11
• DOI: 10.1557/adv.2020.103
• Languages: English
• ISSNs: 2059-8521

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.