A Parallel Machine Learning Workflow for Neutron Scattering Data Analysis

Download PDF
Open Webpage

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

Published: 2023, Last Modified: 04 Nov 2025IPDPS Workshops 2023EveryoneRevisionsBibTeXCC BY-SA 4.0
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.