A Parallel Machine Learning Workflow for Neutron Scattering Data Analysis

Tianle Wang; Sudip K. Seal; Ramakrishnan Kannan; Cristina Garcia-Cardona; Thomas Proffen; Shantenu Jha

doi:10.1109/IPDPSW59300.2023.00133

A Parallel Machine Learning Workflow for Neutron Scattering Data Analysis

Tianle Wang, Sudip K. Seal, Ramakrishnan Kannan, Cristina Garcia-Cardona, Thomas Proffen, Shantenu Jha

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

As part of a larger effort, this work-in-progress reports the possible advantages of modifying conventional workflows used to generate labelled training samples and train machine learning (ML) models on them. We compare results from three different workflows using neutron scattering data analysis as the motivating application and report about 20% improvement in speedup, with no appreciable loss of model accuracy, over a baseline workflow.

Original language	English
Title of host publication	2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	795-798
Number of pages	4
ISBN (Electronic)	9798350311990
DOIs	https://doi.org/10.1109/IPDPSW59300.2023.00133
State	Published - 2023
Event	2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023 - St. Petersburg, United States Duration: May 15 2023 → May 19 2023

Publication series

Name	2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023

Conference

Conference	2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023
Country/Territory	United States
City	St. Petersburg
Period	05/15/23 → 05/19/23

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan) A portion of this research used resources at the Spallation Neutron Source, a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory. A portion of this research used resources at the Argonne Leadership Computing Facility, a DOE Office of Science User Facility operated by the Argonne National Laboratory. A portion of this research used resources at the Brookhaven National Laboratory, a DOE Office of Science National Laboratory. This research was sponsored by ExaLearn, an Exascale Computing Project, DOE.

Funders	Funder number
U.S. Department of Energy

Keywords

Neutron Data Analysis
Parallel Machine Learning
Workflow
heterogeneous computing

Access to Document

10.1109/IPDPSW59300.2023.00133

Cite this

Wang, T., Seal, S. K., Kannan, R., Garcia-Cardona, C., Proffen, T., & Jha, S. (2023). A Parallel Machine Learning Workflow for Neutron Scattering Data Analysis. In 2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023 (pp. 795-798). (2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IPDPSW59300.2023.00133

Wang, Tianle ; Seal, Sudip K. ; Kannan, Ramakrishnan et al. / A Parallel Machine Learning Workflow for Neutron Scattering Data Analysis. 2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 795-798 (2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023).

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abstract = "As part of a larger effort, this work-in-progress reports the possible advantages of modifying conventional workflows used to generate labelled training samples and train machine learning (ML) models on them. We compare results from three different workflows using neutron scattering data analysis as the motivating application and report about 20% improvement in speedup, with no appreciable loss of model accuracy, over a baseline workflow.",

keywords = "Neutron Data Analysis, Parallel Machine Learning, Workflow, heterogeneous computing",

author = "Tianle Wang and Seal, {Sudip K.} and Ramakrishnan Kannan and Cristina Garcia-Cardona and Thomas Proffen and Shantenu Jha",

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Wang, T, Seal, SK , Kannan, R, Garcia-Cardona, C, Proffen, T & Jha, S 2023, A Parallel Machine Learning Workflow for Neutron Scattering Data Analysis. in 2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023. 2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023, Institute of Electrical and Electronics Engineers Inc., pp. 795-798, 2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023, St. Petersburg, United States, 05/15/23. https://doi.org/10.1109/IPDPSW59300.2023.00133

A Parallel Machine Learning Workflow for Neutron Scattering Data Analysis. / Wang, Tianle; Seal, Sudip K.; Kannan, Ramakrishnan et al.
2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 795-798 (2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Wang T, Seal SK , Kannan R, Garcia-Cardona C, Proffen T, Jha S. A Parallel Machine Learning Workflow for Neutron Scattering Data Analysis. In 2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 795-798. (2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023). doi: 10.1109/IPDPSW59300.2023.00133

A Parallel Machine Learning Workflow for Neutron Scattering Data Analysis

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