Toward Real-Time Analysis of Synchrotron Micro-Tomography Data: Accelerating Experimental Workflows with AI and HPC

McClure, James E.; Yin, Junqi; Armstrong, Ryan T.; Maheshwari, Ketan; Wilkinson, Sean R.; Vlcek, Lucas; Wang, Ying Da; Berrill, Mark; Rivers, Mark L.

doi:10.1007/978-3-030-63393-6_15

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…Analysis methods such as those just described can easily overwhelm instrument computers. Indeed, some analyses can consume tens or even hundreds of thousands of cores, 36 , 37 albeit typically in a bursty manner. Similarly, experiments can generate petabytes.…”

Section: Resultsmentioning

confidence: 99%

Linking scientific instruments and computation: Patterns, technologies, and experiences

Vescovi¹,

Chard²,

Saint³

et al. 2022

Patterns

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Section: Resultsmentioning

confidence: 99%

Linking scientific instruments and computation: Patterns, technologies, and experiences

Vescovi¹,

Chard²,

Saint³

et al. 2022

Patterns

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“…There are also architectures in which edge devices can be used to process data in near-real-time, while coupling with analyses executed on true HPC resources. For example, a scientist at a light source might run parts of a workflow locally while collecting observational data at the same time that HPC resources are crunching numbers to help steer the data collection process (McClure et al 2020).…”

Section: Use Cases For Federationmentioning

confidence: 99%

Integrating HPC, AI, and Workflows for Scientific Data Analysis: Report from Dagstuhl Seminar 23352

Badia,

Berti-Equille,

Ferreira Da Silva

et al. 2024

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“…High performance methods to enhance experimental data collection workflows are also broadly generalizable [13].…”

Section: Introductionmentioning

confidence: 99%

Digital Rocks Portal (Digital Porous Media): Connecting data, simulation and community

Prodanović

Esteva²,

McClure³

et al. 2023

E3S Web Conf.

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Digital Rocks Portal (DRP, https://www.digitalrocksportal.org) organizes and preserves imaged datasets and experimental measurements of porous materials in subsurface, and beyond, with the mission to connect them to simulation and analysis, as well as educate the research community. We have over 150 projects represented in more than 200 publications, and an active community that reuses the data, most recently in multiple machine learning applications for automating image analysis as well as the prediction of transport. Such automation is crucial for performing formation evaluation tasks in near-real time. We present benchmark datasets that have played a role in recent machine learning prediction successes in the field. We further discuss the vision for further research advances, educational materials, as well as growth and sustainability plan of this digital rock physics community resource. In particular, we are in the process of expanding into a broader repository of engineered porous materials, specifically those for energy storage and the portal will transition to Digital Porous Media (DPM) in near future.

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Toward Real-Time Analysis of Synchrotron Micro-Tomography Data: Accelerating Experimental Workflows with AI and HPC

Cited by 6 publications

References 35 publications

Linking scientific instruments and computation: Patterns, technologies, and experiences

Linking scientific instruments and computation: Patterns, technologies, and experiences

Integrating HPC, AI, and Workflows for Scientific Data Analysis: Report from Dagstuhl Seminar 23352

Digital Rocks Portal (Digital Porous Media): Connecting data, simulation and community

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