Abstract
Science ecosystems with networked computing systems and physical instruments are increasingly being deployed with a goal to achieve the productivity promised by AI-supported remote automation. In support of these efforts, the virtual infrastructure twins (VITs) have been successfully utilized to develop the orchestration codes for these ecosystems without requiring physical access to expensive instruments, such as electron microscopes. Currently, the utility of such a VIT is severely limited by the computing capacity and capability of the computing system used as its host. Furthermore, codes developed on the VIT typically need to be transferred and refactored for production use, particularly, on high-performance systems with accelerators. In response, we develop a design-to-deployment continuum platform wherein a VIT runs natively on the ecosystem's own computing system, and thereby facilitates the continual in-situ testing and transition of codes for production use. We describe the development and testing of software for remote microscope steering and GPU-based image reconstruction using this platform on a multi-GPU computing system networked to Nion microscopes. We demonstrate a continual transition of steering and reconstruction codes developed under VIT platform to production ecosystem deployment.
| Original language | English |
|---|---|
| Pages (from-to) | 3645-3654 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Industrial Informatics |
| Volume | 21 |
| Issue number | 5 |
| DOIs | |
| State | Published - 2025 |
Funding
This work was supported in part by the INTERSECT Initiative as part of the Laboratory Directed Research and Development Program, in part by the RAMSES and Diaspora projects of Advanced Scientific Computing Research program, U.S. Department of Energy, and in part by the Office of Basic Energy Sciences, Division of Materials Sciences and Engineering, U.S. Department of Energy, and is performed at Oak Ridge National Laboratory managed by UT-Battelle, LLC for U.S. Department of Energy under Grant DE-AC05-00OR22725. Paper no. TII-23-5169. 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, world-wide 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. Received 20 December 2023; revised 17 May 2024, 26 June 2024, and 10 December 2024; accepted 23 December 2024. Date of publication 31 January 2025; date of current version 21 April 2025. This work was supported in part by the INTERSECT Initiative as part of the Laboratory Directed Research and Development Program, in part by the RAMSES and Diaspora projects of Advanced Scientific Computing Research program, U.S. Department of Energy, and in part by the Office of Basic Energy Sciences, Division of Materials Sciences and Engineering, U.S. Department of Energy, and is performed at Oak Ridge National Laboratory managed by UT-Battelle, LLC for U.S. Department of Energy under Grant DE-AC05-00OR22725. Paper no. TII-23-5169. (Corresponding author: Anees Al-Najjar.) Anees Al-Najjar, Nageswara S. V. Rao, Ramanan Sankaran, De-bangshu Mukherjee, and Kevin Roccapriore are with Oak Ridge National Laboratory, Oak Ridge, TN 37830 USA (e-mail: alnajjaram@ ornl.gov; [email protected]; [email protected]; mukherjeed@ornl. gov; [email protected]).
Keywords
- Design-to-deployment continuum
- GPU computations
- electron microscope
- instrument-computing ecosystem
- virtual infrastructure twin (VIT)