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The HPC/QC Requirements Document presents a comprehensive framework for integrating High Performance Computing (HPC) and Quantum Computing (QC). The framework proposed in this document supports a hybrid quantum-classical computing model that provides application flexibility, and resource abstraction and standardization.
The SOS26 workshop was organized by Oak Ridge National Laboratory (ORNL) and held March 11-14, 2024, at Cocoa Beach, Florida. The SOS is a workshop organized annually, with a focus on distributed high performance computing (HPC). The technical program of the workshop was developed jointly by Sandia National Laboratories (SNL), ORNL and the Swiss National Supercomputing Center (CSCS). The 2024 SOS26 workshop theme was "Versatile HPC for the evolving and expanding needs of science" and had seven technical sessions covering HPC, data and machine learning (ML) topics. Each session consisted of four or five presentations, followed by a panel discussion. This report documents the workshop proceedings covering all the technical sessions.
Large-scale computing has enabled numerous scientific discoveries, including ground-breaking achievements facilitated by the US Department of Energy (DOE) supercomputers and advances in applied mathematics and computer science. While important advances were made in energy efficiency to enable exascale computing, continued efforts are needed to dramatically improve the energy efficiency of the next generation of high-performance computing (HPC) systems and, more broadly, AI data centers. Without substantial improvements in energy efficiency, the energy consumption associated with computing could become a limiting factor for future scientific discovery, national security, and technological advancement.
The ASCR basic research needs for Neuromorphic Computing for Science workshop was held in September 2024. The workshop brochure and report aim to inform and draft a set of grand challenges for advancing the field of neuromorphic computing and developing proof of principle neuromorphic circuits applicable for High Performance Computer (HPC) acceleration for scientific discovery, and brainstorm ideas needed for a successful, robust, and world leading basic research program. The resulting priority research directions are: (1) Neuromorphic computing circuit primitives; (2) Connectivity, communication, and hardware integration; (3) Neuroscience-derived dynamics and algorithms; and (4) Ecosystem for scalable neuromorphic co-design. Breakthroughs in understanding, designing, and prototyping the circuitry and simulation capabilities for a truly neuromorphic computer are essential to enable progress in the field.
The ASCR basic research needs for Neuromorphic Computing for Science workshop was held in September 2024. The workshop brochure and report aim to inform and draft a set of grand challenges for advancing the field of neuromorphic computing and developing proof of principle neuromorphic circuits applicable for High Performance Computer (HPC) acceleration for scientific discovery, and brainstorm ideas needed for a successful, robust, and world leading basic research program. The resulting priority research directions are: (1) Neuromorphic computing circuit primitives; (2) Connectivity, communication, and hardware integration; (3) Neuroscience-derived dynamics and algorithms; and (4) Ecosystem for scalable neuromorphic co-design. Breakthroughs in understanding, designing, and prototyping the circuitry and simulation capabilities for a truly neuromorphic computer are essential to enable progress in the field.
High-performance computing (HPC) has been an indispensable research tool for accessing physical realms difficult, or impossible, achieve with experiment alone. For several decades, the Department of Energy’s (DOE’s) Office of Science has deployed sophisticated HPC systems for solving the nation’s most pressing grand challenge problems in energy, climate change, and human health. In addition, DOE’s National Nuclear Security Administration (NNSA) has adeptly applied HPC in support of key national security objectives, such as nuclear science and stockpile modernization and stewardship. Over time, HPC systems have become increasingly more complex and capable, and as each new machine has come online, scientists and engineers have taken advantage of vast increases in compute power to accelerate scientific discoveries and engineering innovation.
Celeritas is a new Monte Carlo (MC) code that helps satisfy the increasing demand for high energy physics (HEP) detector simulation, using Graphics Processing Unit (GPU) hardware on high performance computing (HPC) systems to model Large Hadron Collider (LHC) experiments and beyond. This report details the project’s progress midway through its SciDAC funding period, highlighting the first complete implementation of standard electromagnetic (EM) physics on GPUs, initial results for performance and scalability on Leadership Computing Facilities (LCFs), and preliminary integration into the CMS and ATLAS experiments. By integrating HEP domain knowledge with expertise in MC transport, Celeritas has catalyzed a shift in the HEP community’s perception of GPU platforms as the future for HPC simulations.
Scientific workflows are increasingly relying on machine learning (ML), simulation, and hybrid techniques to predict, understand, and optimize the behavior of complex experiments. High-performance computing has greatly improved researchers’ ability to acquire diverse data modalities in these workflows. Recent studies suggest that the performance of machine learning models can be improved by integrating data from various sources. Unfortunately, these workloads pose unprecedent pressure on the network storage to meet the demands associated with accessing these multimodal data. To mitigate the impact of intensive IO, we propose a solution that utilizes a multi-tier High-Performance Computing (HPC) distributed storage and data processing framework, placing computation where the data resides for better performance. By adopting this project, the scientific community will gain new opportunities to explore multimodal storage-driven possibilities, integrating multiple scientific data sources with advanced streaming frameworks. Additionally, our framework effectively utilizes computing resources and bridges the gaps identified by HPC experts. Our proposed approach tackles scalability and persistence challenges by leveraging native persistency, which has posed difficulties in traditional approaches. Furthermore, we seek to enhance fault-tolerance and load-balance of computations by leveraging real-time streaming in diverse scientific computing environments, thereby propelling advanced scientific computing research into the next generation.
To leverage the increasing heterogeneity in modern computing resources, Geant4 incorporates advanced software tools and a task-based framework (G4Tasking) that enables efficient parallelism at event, sub-event, and track levels. Ongoing R&D efforts focus on integrating GPUs into high-energy physics (HEP) simulations, including optical photon simulation with Opticks/NVIDIA OptiX, offloading electromagnetic particle transport using G4HepEM/AdePT and Celeritas, and employing advanced surface-based geometry models such as VecGeom2.0 and ORANGE. As Geant4 continues evolving toward high-performance computing (HPC) and heterogeneous architectures, it remains a key tool for large-scale simulations in HEP and beyond.
NovoNutrients’ fermentation technology uses energy from hydrogen to transform industrial CO2 emissions into premium animal feed ingredients and other valuable products. A single NovoNutrients’ commercial manufacturing plant will capture and convert over 200,000 tons/yr of CO2 into over 100,000 tons/yr of high-protein feed. Key to the rapid and widespread deployment of the technology is maximization of its productivity and energy efficiency. Robust, physically based computational models of the technology will significantly increase productivity and efficiency, accelerating NovoNutrients’ technology to manufacturing scale. NREL has unique capabilities for creating and running such computational models. NREL's existing aerobic bioreaction computational fluid dynamics (CFD) models will be adapted to NovoNutrients’ gas fermentation (CO2, H2, O2) technology. The multiphysics CFD simulations require thousands of high-performance computing (HPC) node hours to simulate the complex geometries and contents of NovoNutrients’ industrial bioreactors. The experimentally validated CFD models were used to identify optimally efficient and productive bioreactor designs and operating conditions.
Artificial intelligence (AI), machine learning (ML), and high-performance computing (HPC) are poised to transform biological research, spurring innovation in biotechnology and biosystems design. "is transformation will bring an explosion of new capabilities to control the expression of genomic information in living organisms and harness that information to invent new biobased technologies (Jinek et al. 2012; NASEM 2025).
The Oak Ridge Leadership Computing Facility (OLCF) was established to accelerate scientific discovery by providing world-leading computational performance and advanced data infrastructure to the US Department of Energy (DOE) computing community. As a 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 effective 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.
Benchmarking is essential for high-performance software development, particularly for monitoring performance across code iterations. This project focused on enhancing the benchmarking process for Lamellar, an asynchronous runtime for High-Performance Computing (HPC) systems developed at Pacific Northwest National Laboratory. Prior to this work, benchmark results were difficult to track and compare across code versions, presenting significant challenges in identifying performance regressions and long-term trends. The primary objective was to establish a systematic, reproducible approach for measuring performance and detecting regressions following code commits. Our methodology involved three key components: standardizing benchmark outputs, implementing data versioning, and developing analysis tools. We standardized the benchmark output format to JSON Line records containing specific fields (execution time, hardware specifications, and environmental variables). To address data management challenges, we evaluated several options and eventually chose a git repository dedicated to benchmark data. We developed a suite of Python tools that processed benchmark results, enriched them with metadata, and facilitated search in the repository. The resulting system enables more efficient filtering and comparison of performance metrics across commit histories, hardware configurations, and benchmark variants through a unified query interface. Our implementation reduces computational overhead by first checking for existing results through configuration matching before initiating new benchmark runs, thereby conserving resources. The system has been validated by Lamellar developers. It organizes results by benchmark type and build configurations for efficient retrieval. Future developments include a planned Large Language Model interface for predicting benchmark performance, incorporating the criterion package for statistical analysis, which will enable automated detection of statistically significant performance changes, and integration with continuous integration pipelines. Despite these enhancements being reserved for future work, this project has successfully provided the Lamellar development team with a framework for maintaining consistent performance standards and identifying optimization opportunities across workloads and hardware environments.
This collaboration between Lawrence Livermore National Security, LLC (LLNS) as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Chevron USA Inc., acting through its Chevron Technical Center division, aimed at developing next-generation computational methods for the Elastic Stochastic Full Waveform Inversion (eSFWI). Seismic imaging is heavily used in the oil and gas industry for identifying and operating subsurface reservoirs. Improved seismic imaging methods can improve productivity, lower costs, and improve operational and environmental safety. This CRADA demonstrated that new high-performance computing (HPC) architectures being rolled out over the next five years can enable unprecedented seismic imaging resolution when using eSFWI techniques to process active seismic data. An open-source computational mini-application was developed, capable of demonstrating near-peak performance for eSFWI algorithms on CPU and GPU enabled HPC platforms. Performance was demonstrated on LLNL HPC systems such as Lassen, as well as on Chevron systems. This project benefited Chevron USA Inc. by demonstrating the potential computational efficiency of their full waveform inversion capabilities used to characterize oil/gas reservoirs, which in turn benefits the public through potential increases in capabilities to perform analysis of leasing sites.
As the penetration level of power electronics-interfaced renewables such as photovoltaics (PV) and wind has surged in modern electric grids, new operational risks caused by the dynamics of those inverter-based resources (IBRs) are emerging in parallel. Lessons learned from various grid events include that the impact of IBRs on system-level grid stability will become prominent along with the increase of renewables and that the short-timescale dynamic impacts of IBRs on grid stability are not fully captured by current commercial dynamic simulation tools [1] [2]. For example, IBRs can be controlled to mitigate those destabilizing interactions, but conventional phasor-domain tools (e.g. PSS/E, PSLF) often cannot capture that; likewise, the existing electromagnetic transient (EMT) simulation tools (e.g. PSCAD, EMTP) can simulate detailed IBR controls, but for large power systems with many IBRs, slow simulation speeds severely impede the ability to study dynamic events [3] [4]. Massively paralleling simulations using high-performance computing (HPC) can help address this, especially now that cloud-based HPC capability is widely available, but today s EMT tools are not HPC-compatible, and parallelization of dynamic simulation solvers is not trivial because each region can dynamically affect the others. Thus, dynamic simulation of grids with very large numbers of IBRs potentially poses a barrier to the ongoing energy transition.
Steel industry is crucial to the national economy and security. Around 67% of crude steel in the U.S is produced in electric arc furnaces (EAF), which is energy intensive. Around 140 EAFs operate in the U.S., consuming about 8.6x10 7 MMBtu/year of electricity. One of major challenges for EAFs includes maximizing the efficiency of the electrical energy provided in the form of electric arcs to melt various scrap mixes. To address this issue, a computational fluid dynamics (CFD) methodology is chosen to analyze scrap melting using the electric arc. Due to complex furnace phenomena and the wide variety of potential scenarios, high performance computing (HPC) is essential to yield comprehensive and detailed CFD analyses and systematic parametric studies for optimized EAF operation. The objectives are to 1) simulate scrap melting using electric arc, 2) evaluate electrode/arc position for optimum scrap melting and 3) establish reduced order model for CFD data-base for fast model calculation.
This collaborative project integrated an improved version of the Artificial Intelligence/Deep Learning FRNN prediction and control software into the real-time DIII-D PCS (plasma control system). A key AI/DL software challenge is to build a modern high-performance computing (HPC) enabled “synthetic plasma simulator” capable of carrying out HPC-driven real-time plasma control applications. This involves development of a deep learning framework to train the surrogate model for a first-principles-based instability analysis simulator (“SGTC”) derived from the global gyrokinetic code GTC. The role of SGTC is to provide accurate and detailed plasma instability information from a real-time AI-based simulator capability to complement the deep learning prediction and control from experimentally-measured signals, such as ECE Imaging, supplemented by synthetic SGTC-ECEI.
This report summarizes insights from the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science, which convened more than 40 experts from national laboratories, academia, industry, and community organizations to chart a path toward more powerful, sustainable, and collaborative scientific software ecosystems. To address urgent challenges at the intersection of high-performance computing (HPC), AI, and scientific software, participants envisioned agile, robust ecosystems built through socio-technical co-design—the intentional integration of social and technical components as interdependent parts of a unified strategy. This approach combines advances in AI, HPC, and software with new models for cross-disciplinary collaboration, training, and workforce development. Key recommendations include building modular, trustworthy AI-enabled scientific software systems; enabling scientific teams to integrate AI systems into their workflows while preserving human creativity, trust, and scientific rigor; and creating innovative training pipelines that keep pace with rapid technological change. Pilot projects were identified as near-term catalysts, with initial priorities focused on hybrid AI/HPC infrastructure, cross-disciplinary collaboration and pedagogy, responsible AI guidelines, and prototyping of public-private partnerships. This report presents a vision of next-generation ecosystems for scientific computing where AI, software, hardware, and human expertise are interwoven to drive discovery, expand access, strengthen the workforce, and accelerate scientific progress.