Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 2341404

High Performance Computing Facility Operational Assessment 2022: Oak Ridge Leadership Computing Facility

Abraham, Subil·Abston, J. Paul·Adamson, Ryan M.·Atchley, Scott·Barlow, Aaron·Barker, Ashley·Beck, Tom·Bethea, Katie L.·Brim, Michael·Fuson, Christopher·Kuchar, Olga·Landfield, Ryan·Lange, Jack·Liu, Hong·Maxwell, Don·Melesse Vergara, Veronica G.·Messer II, Bronson·Miller, Ross·Moore, Sheila·Morgan, James·Oral, Sarp·Papatheodore, Thomas·Parete-Koon, Suzanne·Prout, Ryan·Renaud, William·Rogers II, James (Jim)·Sandoval, Michael·Shankar, Mallikarjun (Arjun)·Simmerman, Scott·Sonewald, Betsy·Suh, In-Saeng·Thach, Kevin G.·Tichenor, Suzy·Tourassi, Georgia·Wang, Feiyi·Ward, Spencer·Whitt, Justin L.·Zimmer, Christopher
View original source ↗Read source document ↗Export metadata ↓

Abstract

The Oak Ridge Leadership Computing Facility (OLCF) was established to accelerate scientific discovery by providing world-leading computational performance and advanced data infrastructure. As a US Department of Energy (DOE) Office of Science user facility, the OLCF has managed the successful deployment and operation of a succession of leadership-class resources dedicated to open science. In addition to these resources, the OLCF staff continually strive to develop innovative processes and technologies, improve security, and empower users through allocation management and comprehensive user support and training. These efforts support the advancement of science by the OLCF users and benefit high-performance computing (HPC) facilities around the world. In calendar year (CY) 2022, the OLCF supported 1,681 users and 570 projects and exceeded all targets for user satisfaction. The facility received an average satisfaction score of 4.6 out of 5 on the annual user survey, and 96% of respondents reported a high satisfaction rate with the OLCF overall. Of the 3,212 user tickets submitted in CY 2022, OLCF staff resolved 97% within 3 business days. The facility also introduced several new services for users this year, including weekly virtual office hours with subject matter experts from ORNL and vendor partners; new views in MyOLCF that allow users to analyze allocation and compute usage for a project; the ability to build and run containers on Summit; and improved data visualization support and training resources.

Explore related subjects

97 MATHEMATICS AND COMPUTING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Abraham, Subil, Abston, J. Paul, Adamson, Ryan M., Atchley, Scott, Barlow, Aaron, Barker, Ashley, Beck, Tom, Bethea, Katie L., Brim, Michael, Fuson, Christopher, Kuchar, Olga, Landfield, Ryan, Lange, Jack, Liu, Hong, Maxwell, Don, Melesse Vergara, Veronica G., Messer II, Bronson, Miller, Ross, Moore, Sheila, Morgan, James, Oral, Sarp, Papatheodore, Thomas, Parete-Koon, Suzanne, Prout, Ryan, Renaud, William, Rogers II, James (Jim), Sandoval, Michael, Shankar, Mallikarjun (Arjun), Simmerman, Scott, Sonewald, Betsy, Suh, In-Saeng, Thach, Kevin G., Tichenor, Suzy, Tourassi, Georgia, Wang, Feiyi, Ward, Spencer, Whitt, Justin L., Zimmer, Christopher. 2023-04-01. High Performance Computing Facility Operational Assessment 2022: Oak Ridge Leadership Computing Facility. https://doi.org/10.2172/2341404

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

TANTE: Time-adaptive operator learning via neural Taylor expansion

Operator learning for time-dependent partial differential equations (PDEs) has seen rapid progress in recent years, enabling efficient approximation of complex spatiotemporal dynamics. However, most existing methods rely on fixed time step sizes during rollout, which limits their ability to adapt to varying temporal complexity and often leads to error accumulation. In this work, we propose the Time-Adaptive Transformer with Neural Taylor Expansion (TANTE), a novel operator-learning framework that produces continuous-time predictions with adaptive step sizes. TANTE predicts future states by performing a Taylor expansion at the current state, where neural networks learn both the higher-order temporal derivatives and the local radius of convergence. This allows the model to dynamically adjust its rollout based on the local behavior of the solution, thereby reducing cumulative error and improving computational efficiency. We demonstrate the effectiveness of TANTE across a wide range of PDE benchmarks, achieving superior accuracy and adaptability compared to fixed-step baselines, delivering accuracy gains of 60-80 % and speed-ups of 30-40 % at inference time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Structured illumination for surface-resolved grazing-incidence X-ray scattering

Grazing-incidence (GI) scattering techniques are widely used to characterize thin films, offering high surface sensitivity and insight into morphology and structure. However, these approaches typically provide statistical averaged information due to elongated footprint or limited spatial resolution due to beam size. Here we introduce a method that combines structured illumination with GI X-ray scattering and leverages our computational imaging approach to resolve local structural details. We demonstrate that our method captures local features of an organic semiconductor thin film without the need for sample rotation as in tomography. The method expands GI techniques from statistical averaging to high-resolution imaging, thereby providing the capability for detailed analysis of local material properties, such as domain shape, orientation and polymorphism, which are critical for advancing material design towards more efficient and tailored materials.

97 MATHEMATICS AND COMPUTING↗