A Perspective on the Future of Engineering Design through Computation.
Abstract not provided.
Strack, Otto Eric↗
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FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing
Large-scale particle physics experiments face challenging demands for high-throughput computing resources both now and in the future. New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that potentially requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave by Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) ms with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600–700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
FPGAs as a Service to Accelerate Machine Learning Inference [PowerPoint]
Large-scale particle physics experiments face challenging demands for high-throughput computing resources both now and in the future. New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC, and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave b y Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) milliseconds with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600--700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Software Tools Ecosystem Project (STEP) Midyear Report CY2025
This document provides a technical project report for the first six months of 2025 for the Software Tools Ecosystem Project (STEP). The mission of STEP is to enable critical software tools to proactively adapt to emerging platform technologies (such as new accelerators, storage devices, network technologies, and smart devices) and emerging application use cases (such as advanced machine learning and workflow frameworks) so that they continue to meet the needs of scientific computing and provide a strong foundation for future Advanced Scientific Computing Research activities. Our challenges include the wide breadth of our stakeholders and rapidly evolving platform technology dependencies.
97 MATHEMATICS AND COMPUTING↗
Software Tools Ecosystem Project (STEP): CY2025 Annual Report
This document provides a technical project report for the Software Tools Ecosystem Project (STEP) during calendar year 2025. The mission of STEP is to enable critical software tools to proactively adapt to emerging platform technologies (such as new accelerators, storage devices, network technologies, and smart devices) and emerging application use cases (such as advanced machine learning and workflow frameworks) so that they continue to meet the needs of scientific computing and provide a strong foundation for future Advanced Scientific Computing Research activities.
97 MATHEMATICS AND COMPUTING↗
Quantum Computing for Theoretical Nuclear Physics, A White Paper prepared for the U.S. Department of Energy, Office of Science, Office of Nuclear Physics
Tremendous excitement exists among quantum computing enthusiasts as we witness the rapid evolution of quantum computing devices toward early practical implementation. Importantly, algorithm developments suggest that qubit devices have the potential to solve quantum many-body and quantum field theory problems of relevance to various research directions within the Department of Energy Office of Science and National Science Foundation. While exascale platforms represent the current horizon of excellence in computing, quantum computing provides the technology that lies beyond this horizon and opens the door to new vistas of theoretical endeavor. During a recent workshop at the Institute for Nuclear Theory, a group of 39 scientists from academia, government, national laboratories, and industry gathered to discuss the future of quantum computing for nuclear physics. Researchers described the basic scientific challenges in quantum chromodynamics and quantum many-body problems that could be addressed utilizing the power of quantum computing in the future. The group also discussed mechanisms for developing quantum-computing algorithms specifically for nuclear physics applications. This White Paper provides a synopsis of the workshop discussions, and suggests possible activities that might be supported as part of a vibrant pilot program of nuclear physics research in this area.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Quantum Realism
In this issue, we review quantum computing: what has been accomplished and what is its future. We introduce a collection of opinions about the realistic challenges facing the future of quantum computing.
97 MATHEMATICS AND COMPUTING↗
Integrating Quantum Computing with High-Performance Computing: A Streamlined Approach
In recent years, quantum computing has demon-strated the potential to revolutionize specific algorithms and applications by solving problems exponentially faster than classical computers. However, its widespread adoption for general computing remains a future prospect. This paper discusses the integration of quantum computing within High-Performance Computing (HPC) environments, focusing on a resource management framework designed to streamline quantum simulators' use and enhance runtime performance and efficiency. The proposed framework facilitates hybrid applications' transition from simulation backends to real quantum hardware, optimizing resource utilization and providing a flexible infrastructure for developing and testing quantum algorithms.
Shehata, Amir↗
Autoignition of Premixed Liquefied Petroleum Gas in a Rapid Compression Machine: Experimental Results and Chemical Kinetic Mechanism Reduction
Liquefied petroleum gas (LPG) has many properties that make it an attractive alternative fuel such as lower cost than conventional fuels and an established distribution infrastructure. The development of high efficiency, spark ignited LPG engines is currently limited by engine knock and misfire. The knock and misfire limits are further complicated by the wide range of chemical reactivity in LPG, particularly in international markets. In this study, a rapid compression machine (RCM) was used to characterize the effects of variation in LPG fuel reactivity, equivalence ratio, and exhaust gas recirculation (EGR) on autoignition of LPG/oxidizer/inert/EGR blends. Experiments were conducted with 100% propane (C3H8) and blends of propane with propene, ethane, isobutane, and n-butane. EGR was simulated with mixtures of Ar, CO2, CO, and NO at substitution percentages from 0 to 30 mass percent. Equivalence ratio was varied from 0.75 to 1.5. Ignition delay period under homogeneous autoignition conditions was measured at compressed pressures and temperatures of 23 to 25 bar and 701 to 921 K, respectively. Zero-dimensional simulations of the RCM experiments were performed using Chemkin with several detailed chemical kinetic mechanisms to determine their suitability at predicting ignition delay periods. Multiple reduced chemical kinetic mechanisms were created from the NUIGMech1.1 mechanism to determine the optimal balance between accuracy and computational efficiency for future three-dimensional, time-dependent spark-ignited engine computations.
42 ENGINEERING↗
An FPGA-Based Neuromorphic Processor with All-to-All Connectivity
Neuromorphic computing is a promising paradigm for future energy-efficient computing. At present, however, it is in its nascent stages—most hardware implementations are research-grade, commercial products are not available, and the software tools are not production-ready. The lack of hardware and software tools makes neuromorphic computing inaccessible to researchers around the globe. To this extent, we intend to build a low-cost, open-source, FPGA-based digital neuromorphic processor that can be used by researchers worldwide. In this paper, we present a preliminary implementation of the processor on a Xilinx Artix-7 FPGA using SystemVerilog. Our implementation supports the integrate-and-fire neuron with two parameters each for neurons and synapses. It also features all-to-all connectivity among neurons on the hardware. We test our implementation on four cases: bars and stripes datasets, shortest path algorithm, logic gates, and 8-3 encoder. We also perform a scalability study to understand the resource utilization of the FPGA as the number of all-to-all connected neurons increases. With our implementation, the Artix- 7 supports 65 neurons with all-to-all connectivity. Moreover, all the test cases mentioned above achieve 100% accuracy.
Maheshwari, Disha↗
Computing Performance Task: Summary, Status, and Future Updates
Explore the source record for details and available documents.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Application of Quantum Computing to Biochemical Systems: A Look to the Future
Chemistry is considered as one of the more promising applications to science of near-term quantum computing. Recent work in transitioning classical algorithms to a quantum computer has led to great strides in improving quantum algorithms and illustrating their quantum advantage. Because of the limitations of near-term quantum computers, the most effective strategies split the work over classical and quantum computers. There is a proven set of methods in computational chemistry and materials physics that has used this same idea of splitting a complex physical system into parts that are treated at different levels of theory to obtain solutions for the complete physical system for which a brute force solution with a single method is not feasible. These methods are variously known as embedding, multi-scale, and fragment techniques and methods. We review these methods and then propose the embedding approach as a method for describing complex biochemical systems, with the parts not only treated with different levels of theory, but computed with hybrid classical and quantum algorithms. Such strategies are critical if one wants to expand the focus to biochemical molecules that contain active regions that cannot be properly explained with traditional algorithms on classical computers. While we do not solve this problem here, we provide an overview of where the field is going to enable such problems to be tackled in the future.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Experimentally Validated Numerical Models of Non-Isothermal Turbulent Mixing in High Temperature Reactors
In the Very High Temperature Reactor (VHTR), the non-uniform heat generation and flow maldistribution of coolant in the core generates conditions leading into the lower plenum with many jets at different temperatures and velocities. The hope is that the combined flow becomes fully mixed in a short distance so that the fluctuating temperatures in the mixing region do not cause unwanted thermal stresses in the supporting structures. These cyclic thermal stresses can cause degradation of the wall microstructure including crack initiation and growth, ultimately leading to the fatigue failure in the lower plenum, and can be accelerated by the high temperature environment in the VHTR. The focus of this project was to provide experimental data for future validation of computational efforts aimed at capturing the turbulent mixing in the lower plenum. Computational fluid dynamics (CFD) is also used as part of this effort to assess the requirements to model fundamental flows with application to the lower plenum as well at the applied flow conditions that mimic the experimental facilities. These efforts will lay the groundwork for future incorporation of stress analysis as well as fatigue and failure studies for multiple VHTR components. An additional emphasis is placed on reduced order models (ROM) using canonical flows, which will help to balance computational expense and accuracy and enable future work in uncertainty quantification. The flow in the lower plenum is extremely complex with hundreds of cooling jets entering and then being forced to change directions by 90° while navigating through an array of cylindrical support posts. Generating experimental data for such a complicated flow is difficult, not to mention that the computational requirements to generate an adequate temporal and spatial sampling is extremely expensive and time consuming. Therefore, the ultimate focus of this research is a “unit cell” which consists of six jets in the presence of a crossflow, and seven cylindrical support posts. It is scaled to achieve similar ranges of key dimensionless numbers. This serves to mimic flow conditions in different regions of the lower plenum by adjusting velocities, temperatures, and orientations, and represents a compromise between the complexity inside the full-scale application and the ideal conditions of a more fundamental study. In addition to the unit cell experiments and simulations, the complex flows in the VHTR lower plenum are broken down into a number of simplified (in some cases even fundamental) flows, two of which are turbulent jets and flow past cylinders. Each of these are studied, computationally and experimentally for isothermal and non-isothermal conditions. Flows where trusted experimental data is available in the literature (e.g., turbulent free jet), additional experiments are not needed. The result of these efforts is a database of experimental data and multiple computational studies, which provide an outlook to the requirements, needed to capture turbulent mixing flow physics The ROM results provide assessments for appropriate resource-light models that balance accuracy with reduced computational burdens.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Scalable quantum computational science: A perspective from block-encodings and polynomial transformations
Significant developments made in quantum hardware and error correction recently have been driving quantum computing toward practical utility. However, gaps remain between abstract quantum algorithmic development and practical applications in computational sciences. In this perspective article, we propose several properties that scalable quantum computational science methods should possess. We further discuss how block-encodings and polynomial transformations can potentially serve as a unified framework with the desired properties. Recent advancements on these topics are presented, including the construction and assembly of block-encodings, and various generalizations of quantum signal processing (QSP) algorithms to perform polynomial transformations. The scalability of QSP methods on parallel and distributed quantum architectures is also highlighted. Promising applications in simulation and observable estimation in chemistry, physics, and optimization problems are presented. We hope this perspective serves as a gentle introduction to state-of-the-art quantum algorithms for the computational science community and inspires future development of scalable quantum computational science methodologies that bridge theory and practice.
Bayesian inference↗
Virtual Neuron: A Neuromorphic Approach for Encoding Numbers
Neuromorphic computers perform computations by emulating the human brain and are expected to be indispensable for energy-efficient computing in the future. They are primarily used in spiking neural network-based machine learning applications. However, neuromorphic computers are unable to preprocess data for these applications. Currently, data is preprocessed on a CPU or a GPU-this incurs a significant cost of transferring data from the CPU/GPU to the neuromorphic processor and vice versa. This cost can be avoided if preprocessing is done on the neuromorphic processor. To efficiently preprocess data on a neuromorphic processor, we first need an efficient mechanism for encoding data that can lend itself to all general-purpose preprocessing operations. Current encoding approaches have limited applicability and may not be suitable for all preprocessing operations. In this paper, we present the virtual neuron as a mechanism for encoding integers and rational numbers on neuromorphic processors. We evaluate the performance of the virtual neuron on physical and simulated neuromorphic hardware and show that it can perform an addition operation using 23 nJ of energy on average using a mixed-signal, memristor-based neuromorphic processor. The virtual neuron encoding approach is the first step in preprocessing data on a neuromorphic processor.
Date, Prasanna↗
Neural Algorithms for Low Power Implementation of Partial Differential Equations
The rise of low-power neuromorphic hardware has the potential to change high-performance computing; however much of the focus on brain-inspired hardware has been on machine learning applications. A low-power solution for solving partial differential equations could radically change how we approach large-scale computing in the future. The random walk is a fundamental stochastic process that underlies many numerical tasks in scientific computing applications. We consider here two neural algorithms that can be used to efficiently implement random walks on spiking neuromorphic hardware. The first method tracks the positions of individual walkers independently by using a modular code inspired by grid cells in the brain. The second method tracks the densities of random walkers at each spatial location directly. We present the scaling complexity of each of these methods and illustrate their ability to model random walkers under different probabilistic conditions. Finally, we present implementations of these algorithms on neuromorphic hardware.
97 MATHEMATICS AND COMPUTING↗
HPC resources for CMS offline computing: An integration and scalability challenge for the Submission Infrastructure
The computing resource needs of LHC experiments are expected to continue growing significantly during the Run 3 and into the HL-LHC era. The landscape of available resources will also evolve, as High Performance Computing (HPC) and Cloud resources will provide a comparable, or even dominant, fraction of the total compute capacity. The future years present a challenge for the experiments’ resource provisioning models, both in terms of scalability and increasing complexity. The CMS Submission Infrastructure (SI) provisions computing resources for CMS workflows. This infrastructure is built on a set of federated HTCondor pools, currently aggregating 400k CPU cores distributed worldwide and supporting the simultaneous execution of over 200k computing tasks. Incorporating HPC resources into CMS computing represents firstly an integration challenge, as HPC centers are much more diverse compared to Grid sites. Secondly, evolving the present SI, dimensioned to harness the current CMS computing capacity, to reach the resource scales required for the HLLHC phase, while maintaining global flexibility and efficiency, will represent an additional challenge for the SI. To preventively address future potential scalability limits, the SI team regularly runs tests to explore the maximum reach of our infrastructure. In this note, the integration of HPC resources into CMS offline computing is summarized, the potential concerns for the SI derived from the increased scale of operations are described, and the most recent results of scalability test on the CMS SI are reported.
Pérez-Calero Yzquierdo, Antonio↗
Envisioning Science in 2050
To explore future-focused questions that could impact the future of DOE, a Community of Interest (COI) on Future Scientific Methodologies, sponsored by the Office of Advanced Scientific Computing Research (ASCR), was held over three non-consecutive days in November 2020. The COI’s charge was to create a vision for how future computational fabrics might shape, and be shaped by, scientific and technological advances over the next 10 to 30 years.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗