MINNION: Non-Linear Machine Learning based Data Reduction Software for High-Performance Computing
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Current trends point to a future where large-scale scientific applications are tightly coupled high-performance computing/artificial intelligence (HPC/AI) hybrids. Hence, we urgently need to invest in creating a seamless, scalable framework where HPC and AI/machine learning can efficiently work together and adapt to novel hardware and vendor libraries without starting from scratch every few years. Finally, the current ecosystem and sparsely connected community are not sufficient to tackle these challenges, and we require a breakthrough catalyst for science similar to what PyTorch enabled for AI.
We are updating the appropriate use policy for INL HPC resources to align with requirements regarding proprietary and non-proprietary use of government equipment.
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While a large number of tools have been developed to support application portability, high performance application developers often prefer to use vendor-provided, non-portable programming interfaces. This phenomena indicates the mismatch between user priorities and tool capabilities. This paper summarizes the results of a user survey and a developer survey. The user survey has revealed the user priorities and resulted in three criteria for evaluating tool support for portability. The developer survey has resulted in the evaluation of portability support and indicated the possibilities and difficulties of improvements.
Artificial Intelligence and Machine Learning Software Training for Researchers
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As particle physics experiments push their limits on both the energy and the intensity frontiers, the amount and complexity of the produced data are also expected to increase accordingly. With such large data volumes, next-generation efforts like the HL-LHC and DUNE will rely even more on both high-throughput (HTC) and high-performance (HPC) computing clusters. Full utilization of HPC resources requires scalable and efficient data-handling and I/O. For the last few decades, ROOT has been used by most HEP experiments to store data. However, other storage technologies like HDF5 may perform better in HPC environments. Initial explorations with HDF5 have begun using ATLAS, CMS and DUNE data; the DUNE experiment has also adopted HDF5 for its data-acquisition system. This paper presents the future outlook of the HEP computing and the role of HPC, and a summary of ongoing and future works to use HDF5 as a possible data storage technology for the HEP experiments to use in HPC environments.
FY 2021 Annual Report including major accomplishments, utilization and user institution statistics, and project summary reports for FY-21.
This paper is concerned with the physics-based simulation of light tracked vehicles operating on rough deformable terrain. The focus is on small autonomous vehicles, which weigh less than 100 lb and move on deformable and rough terrain that is feature rich and no longer representable using a continuum approach. A scenario of interest is, for instance, the simulation of a reconnaissance mission for a high mobility lightweight robot where objects such as a boulder or a ditch that could otherwise be considered small for a truck or tank, become major obstacles that can impede the mobility of the light autonomous vehicle and negatively impact the success of its mission. Analyzing and gauging the mobility and performance of these light vehicles is accomplished through a modeling and simulation capability called Chrono::Engine. Chrono::Engine relies on parallel execution on Graphics Processing Unit (GPU) cards.
The electromagnetic modeling of packages and interconnects plays a very important role in the design of high-speed digital circuits, and is most efficiently performed by using computer-aided design algorithms. In recent years, packaging has become a critical area in the design of high-speed communication systems and fast computers, and the importance of the software support for their development has increased accordingly. Throughout this project, our efforts have focused on the development of modeling and simulation techniques and algorithms that permit the fast computation of the electrical parameters of interconnects and the efficient simulation of their electrical performance.
In FY21, INL HPC capabilities were utilized by a diverse set of computing and applied researchers, for a wide range of research and engineering activities. This report focuses on current INL HPC systems and utilization from October 1, 2020 through September 30, 2021.
INL HPC team provided updates related to the INL HPC program and HPC systems for HPC User Group Quarterly Review - September 11, 2024
INL HPC team provided updates related to the INL HPC program and HPC systems for HPC User Group Quarterly Review - June 12, 2024
INL HPC team provided updates related to the INL HPC program and HPC systems for HPC User Group Quarterly Review - June 12, 2024
INL HPC capabilities were utilized by individuals at universities, industry, and government laboratories for a wide range of research and engineering activities between October 1, 2020 and September 30, 2021. The HPC systems themselves experienced minimal downtime over the course of the fiscal year. Most downtime was planned to apply critical security patches to system software. The most serious of the unplanned downtime events occurred due to maintenance on the Kohler backup generator in July. Users were kept notified of all events, both planned and unplanned, via 36 total email notifications in FY-21. The INL HPC systems continue to provide high availability for collaboration and innovation in nuclear energy systems research.
INL HPC team provided updates related to the INL HPC program and HPC systems for HPC User Group Quarterly Review - March 5, 2025
INL HPC team provided updates related to the INL HPC program and HPC systems for HPC User Group Quarterly Review - September 10, 2025