Results for “computers” — Page 36

DOE OSTI2021

An Integrated High-performance Computing and Digital Real-time Simulation Testbed to Benchmark Closed-loop Load Shedding Algorithms in Power Systems

An integrated testbed using digital real-time simulator (DRTS) and a high-performance computing (HPC) cluster is presented here to compare speed and performance of computational schemes to mitigate time-critical issues in electric power systems. The first approach in this testbed validation is taken by running a set of closed-loop load shedding algorithms to compare and contrast two paradigms of arresting cascading failure propagation. Two algorithms involve solving DC and AC power flow model-based optimization problems to compute load shedding at different buses, while a model-based stochastic search using parallel computing provides a viable alternative. The algorithms are implemented in the DRTS-HPC testbed for the IEEE 14-bus benchmark transmission system. As a proof of the concept, simulation results are presented for implementation of closed-loop load-shedding algorithms for cascading failures in the DRTS-HPC testbed

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Coupled Microbial-Conversion and Computational-Fluid-Dynamics (CFD) Models for Butanediol Production in Micro-Aerated Reactors

Microbial conversion of substrates to macromolecules has been widely used in the synthesis of value-added products in pharmaceutical and biotechnology industries. These bioreactions are also being investigated in the production of low-value commodities such as biofuels [1]. Gas-liquid mass-transfer and transport-reaction coupling are important challenges when designing and scaling up these reactor systems. Experiments in wellmixed small-scale reactors have enabled characterization of microbial reactivity, while their coupling with macroscale transport remains relatively unexplored. In this work, we use a coupled metabolic-CFD model to study the action of a genetically engineered microbe Zymomonas mobilis [2] on sugars to produce 2,3-Butanediol (BDO). BDO is an important hydrocarbon intermediate that can be catalytically upgraded to several fuels and chemicals [3]. An important aspect to this particular microbial conversion is the need for micro-aerated environments as opposed to traditional aerobic fermentation. Slight variations in oxygen concentration can result in competing reaction pathways that disable BDO production. Hence, gas-liquid mass transfer and transport need to be optimized in large-scale reactors to maximize BDO production, for which CFD is a valuable tool. The aerobic-fermentation CFD model previously developed by the authors [5] for simulating bubble-column and airlift reactors at scale was used in this study. The Reynolds-averaged mass, momentum and energy transport equations for interpenetrating gas and liquid phase are solved in this model along with the transport and interphase mass transfer of oxygen. Our previous work used a phenomenological model for microbial oxygen uptake that neglected microbial growth and other reaction pathways. In this work, a detailed metabolic model enabled prediction of product formation and inhibition pathways. In order to manage computational cost, we used a subcycling technique [6] that takes advantage of the clear separation in transport (~ 200 sec) and reaction (~ 2-3 hours) timescales. The CFD model is first solved to steady state, after which the metabolic model is advanced at every cell in the computational domain using the local oxygen concentration. The CFD model is then run to achieve a new steady state that provides a new oxygen distribution for the metabolic model. This process, where reaction and fluid updates are interleaved together, is iterated until reactants are completely exhausted. This work will examine the performance of different reactor designs such as bubble column and airlift reactors at scale (250-500 m3). Oxygen mass-transfer coefficient and distribution are critically analyzed among reactors, and optimization studies pertaining to aeration is presented. Furthermore, it has been observed in experiments that high BDO production may be achieved by manipulating the aerobic environment over the course of reaction, such that oxygen concentration is high during the growth phase, and very low as sugar is depleted. This characteristic will be addressed by our simulations for which a timedependent scheduling strategy for aeration is presented that maximizes BDO production. [1] Humbird, D., Davis, R., and McMillan, J., Aeration costs in stirred-tank and bubble column bioreactors, Biochemical Engineering Journal, 127, 161—166, 2017 [2] Yang, S., Mohagheghi, A., Franden, M. A., Chou, Y.-C., Chen, X., Dowe, N., Himmel, M. E., and Zhang, M., Metabolic engineering of zymomonas mobilis for 2, 3-butanediol production from lignocellulosic biomass sugars. Biotechnology for biofuels, 9(1):189, 2016 [3] Kim, S. J., Sim, H. J., Kim, J. W., Lee, Y. G., Park, Y. C., and Seo, J. H., Enhanced production of 2,3-butanediol from xylose by combinatorial engineering of xylose metabolic pathway and cofactor regeneration in pyruvate decarboxylase-deficient Saccharomyces cerevisiae. Bioresource Technology, 245:1551–1557, 2017 [4] Weller, H., Tabor, G., Jasak, H. and Fureby, C., A tensorial approach to computational continuum mechanics using object-oriented techniques, Computers in physics, 12, 6, 620--631, 1998

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Hybrid prioritized resource allocation in thermally-or power-constrained computing devices

Various embodiments comprise prioritizing frequency allocations in thermally- or power-constrained computing devices. Computer elements may be assigned ‘weights’ based on their priorities. The computer elements with higher weights may receive higher frequency allocations to assure they receive priority in processing more quickly. The computer elements with lower weights may receive lower frequency allocations and suffer a slowdown in their processing. Elements with the same weight may be grouped together for the purpose of frequency allocation.

Al-Rawi, Asma↗

DOE OSTI2022

Towards an HPC Complementary Computing Facility

This Letter considers the design for computing facilities that are complementary to the leadership class High Performance Computing (HPC) facilities. This design envisions a future where funding agencies are allocating greater resources for leadership class facilities and these facilities will provide a significant part of the total compute cycles for HEP Experiments. While a leadership class facility (LCF) may provide cycles and advanced architectures, the facility does not necessarily provide all of the services needed to help HEP users make the best use of the HPC facility, as well as the services needed to provide computing for workflows that are not a good fit for the HPC facilities. This Letter outlines some of the necessary components of a facility designed to provide those services and capabilities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Toward designing effective exascale scientific computing workflows: experiences and best practices

Many fields within scientific computing have embraced advances in big-data analysis and machine learning, which often requires the deployment of large, distributed and complicated workflows that may combine training neural networks, performing simulations, running inference, and performing database queries and data analysis in asynchronous, parallel and pipelined execution frameworks. Such a shift has brought into focus the need for scalable, efficient workflow management solutions with reproducibility, error and provenance handling, traceability, and checkpoint-restart capabilities, among other needs. Here, we discuss challenges and best-practices for deploying exascale-generation computational science workflows on resources at the Oak Ridge Leadership Computing Facility (OLCF). We present our experiences with large-scale deployment of distributed workflows on the Summit supercomputer, including for bioinformatics and computational biophysics, materials science, and deep learning model optimization. We also present problems and solutions created by working within a Python-centric software base on traditional HPC systems, and discuss steps that will be required before the convergence of HPC, AI, and data science can be fully realized. Our results point to a wealth of exciting new possibilities for harnessing this convergence to tackle new scientific challenges.

Coletti, Mark↗

DOE OSTI2022

Atomic operations in a large scale distributed computing network

Techniques for executing an atomic command in a distributed computing network are provided. A core cluster, including a plurality of processing cores that do not natively issue atomic commands to the distributed computing network, is coupled to a translation unit. To issue an atomic command, a core requests a location in the translation unit to write an opcode and operands for the atomic command. The translation unit identifies a location (a “window”) that is not in use by another atomic command and indicates the location to the processing core. The processing core writes the opcode and operands into the window and indicates to the translation unit that the atomic command is ready. The translation generates an atomic command and issues the command to the distributed computing network for execution. After execution, the distributed computing network provides a response to the translation unit, which provides that response to the core.

Lackey, Jr., Stanley A.↗

DOE OSTI2023

Automated analysis of lattice structures using computed tomography

Systems, methods, and computer-readable media for evaluating a set of computed tomography data associated with a lattice structure. The lattice structure may be additively manufactured. The computed tomography data may be segmented using a filter for identifying blob-like structures to identify nodes present within the lattice structure. A three-dimensional path traversal is applied to volumetric data to identify a plurality of struts within the lattice structure that are compared to corresponding struts within a set if three-dimensional mesh data of the lattice structure to identify defective struts. Further, two-dimensional slices may be extracted from each of the computed tomography data and the mesh data and compared to identify one or more inconsistencies indicative of defects within the lattice structure.

Schiefelbein, Bryan E.↗

DOE OSTI2023

Virtual Infrastructure Twins: Software Testing Platforms for Computing-Instrument Ecosystems

Science ecosystems are being built by federating computing systems and instruments located at geographically distributed sites over wide-area networks. These computing-instrument ecosystems are expected to support complex workflows that incorporate remote, automated AI-driven science experiments. Their realization, however, requires various designs to be explored and software components to be developed, in order to support the orchestration of distributed computations and experiments. It is often too expensive, infeasible, or disruptive for the entire ecosystem to be available during the typically long software development and testing periods. We propose a Virtual Infrastructure Twin (VIT) of the ecosystem that emulates its network and computing components, and incorporates its instrument software simulators. It provides a software environment nearly identical to the ecosystem to support early development and testing, and design space exploration. We present a brief overview of previous digital infrastructure twins that culminated in the VIT concept, including (i) the virtual science network environment for developing software-defined networking solutions, and (ii) the virtual federated science instrument environment for testing the federation software stack and remote instrument control software. We briefly describe VITs for Nion microscope steering and access to GPU systems.

Rao, Nageswara↗

DOE OSTI2023

Scalable Graph Analytics and HPC Operational Enhancement: Parallel Computing and ML/DL Innovations

Parallel computing plays a pivotal role in the efficient processing of large-scale graphs. Complex network analysis stands as a capti- vating research frontier, holding promise across diverse scientific domains such as sociology, biology, online media, and recommenda- tion systems. In this era, Machine Learning (ML) and Deep Learning (DL) have emerged as indispensable tools, underpinning remarkable technological achievements. Within this dynamic landscape, my research revolves around advancing parallel algorithms tailored for large-scale graph operations. To achieve this, I harness the power of cutting-edge technologies including OpenMP, MPI, HIP, and CUDA, on the High-Performance Computing (HPC) platforms to unlock optimal performance. I also apply ML/DL techniques to HPC operational data, to streamline the monitoring and maintenance of supercomputers, alleviating the complexities associated with their upkeep and enhancing user support. My research echoes the syn- ergy between parallel computing, large-scale graph analysis, and ML/DL, improving computational efficiency and user experience.

Sattar, Naw Safrin↗

DOE OSTI2024

Mathematics Integrated with Computer Science through Scratch: Curricular Modules for the Middle Grades

A project funded by the National Science Foundation, Computer Science Integrated with Mathematics in Middle Schools (CSIMMS) (DRL-1640039), brought middle-school mathematics teachers together with university computer science (CS) faculty and STEM education faculty to design, develop, and test curriculum modules in which CS is integrated into instruction for middle-school general-mathematics courses. The project developed integrated math/CS curriculum modules (for grades 6, 7, and 8), complete with student tasks and teacher materials to guide classroom implementation. Across the project, sixteen teachers from four middle schools representing different school contexts (both urban and suburban, serving students from a range of demographic and socioeconomic backgrounds) participated as members of the design team and trial testers of the math/CS modules. All modules underwent multiple years of testing and refinement, with extensive input from the teachers who co-designed and implemented them. Approximately 50-100 middle-school students participated in the classes of those who taught each year. The modules in this volume feature a range of models for integrating mathematics and computer science at the 6th and 7th grade levels. All modules foreground the teaching of grade-level mathematics content, with computer science functioning to motivate and/or reinforce these ideas.

Andrews larson, Christine↗

DOE OSTI2024

Automated analysis of lattice structures using computed tomography

Systems, methods, and computer-readable media for evaluating a set of computed tomography data associated with a lattice structure. The lattice structure may be additively manufactured. The computed tomography data may be segmented using a filter for identifying blob-like structures to identify nodes present within the lattice structure. A three-dimensional path traversal is applied to volumetric data to identify a plurality of struts within the lattice structure that are compared to corresponding struts within a set if three-dimensional mesh data of the lattice structure to identify defective struts. Further, two-dimensional slices may be extracted from each of the computed tomography data and the mesh data and compared to identify one or more inconsistencies indicative of defects within the lattice structure.

Schiefelbein, Bryan E.↗

DOE OSTI2023

Exploration of Potential Superconducting Multi-Mode Cavity Architectures for Quantum Computing

This thesis describes the investigation of superconducting multi-mode cavity architecture for superconducting transmon-based quantum computing. The dissertation highlights useful features of radio-frequency cavities used for quantum computing, with a brief discussion on the advantages of superconducting cavities. In the subsequent section, the concept of transmon is introduced and the mechanism of coupling with a cavity is analyzed. Once the foundations of the topic are laid down, the thesis focuses on optimizing a multi-mode superconducting rf cavity design originally developed for high-energy physics applications. Such section articulates in two main parts, the first part concerning the "bare" cavity remake via finite-elements eigenmode simulations using a computer-aided design software called CST Studio Suite®. In the second part, a transmon qubit is physically inserted into the modified cavity to assess the qubit-cavity coupling of the new design. In evaluating said coupling, two distinct analysis methods are used, namely the black-box quantization method and the energy participation ratio method, both implemented using Ansys® High-Frequency Electromagnetic-Field Simulator, or HFSS™. Results from the two evaluations, compared together, show that the optimized design meets the requisites to be used for quantum computing purposes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Fusing Edge Computing with Transport Security by Leveraging the Controller Area Network Transport Security Tracking and Reporting (C-STAR) Unit

Rapid advances in embedded system complexity and capability provides exciting opportunities for transportation security deployment. Manufacturers and developers of these embedded systems continue to provide lower cost and more powerful solutions that can be leveraged by researchers and engineers. Furthermore, deploying these devices at the “edge” of the Internet-of-Things (IoT) infrastructure provides opportunities for highly capable applications in transport security. In an edge computation architecture, the device is co-located at the source of the data in the larger IoT structure – this provides computational capability at the location directly where the data is collected. For shipment transport security, this provides a direct compute node for digestion of data and mitigation actions in real-time. In our application, the vehicle provides a significant amount of this data that can be processed in real-time via the Controller Area Network Transport Security Tracking and Reporting (C-STAR) edge device. Utilization of a computational node located on the vehicle, such as the C-STAR, capitalizes on previously discussed opportunities of edge architectures. In this paper, we will discuss this security solution’s usability, current deployments, and scalability to further applications in transport security. First, we will cover the supported vehicle platforms that can leverage the C-STAR technology. This will be particularly relevant to medium- and heavy-duty vehicles transporting high-risk shipments. Second, we will speak to current deployments of the C-STAR that are ongoing. Finally, we will discuss additional areas for expansion such as maturing the onboard algorithms through continuing collaborations.

Cook, Adian [ORNL] (ORCID:0000000160825395)↗

DOE OSTI2025

A Quantum Computational Determination of the Weak Mixing Angle in the Standard Model

The weak mixing angle $s_W$ is a fundamental constant in the Standard Model (SM) and measured at the $Z$ boson mass to be $\widehat{s}^2_W(m_Z) = 0.23129 \pm 0.00004$ in the $\overline{\rm MS}$ renormalization scheme, where $m_Z=91.2\ \text{GeV}$. On the other hand, non-stabilizerness - the magic - characterizes the computational advantage of a quantum system over classical computers. We consider the production of magic from stabilizer initial states, which carry zero magic, in the 2-to-2 scattering of charged leptons in the SM at the tree level, which is mediated by the photon and the $Z$ boson. Using the second order stabilizer Rényi entropy, and averaging over all 60 initial stabilizer states and the scattering angle, we compute and minimize the magic production as a function of $s^2_W$ in the Møller scattering $e^-e^-\to e^-e^-$, which is free of kinematic thresholds. At the centre-of-mass energy $\sqrt{s}=m_Z$, there is a unique minimum in magic production at $\mathbf{s}^{2}_W(m_Z)=0.2317$, which agrees with the measured $\widehat{s}^2_W(m_Z)$ at the sub-percent level. At higher energies, the magic-minimizing $\mathbf{s}^{2}_W$ continues to agree with the empirical value at the percent level or better, up to 10 TeV. The finding suggests the electroweak sector of the SM tends to generate minimal quantum resources from the computational viewpoint.

Liu, Qiaofeng [Northwestern U.]↗

DOE OSTI2026

Agentic Diagrammatica: Towards Autonomous Symbolic Computation in High Energy Physics

We present Diagrammatica, a symbolic computation extension to the HEPTAPOD agentic framework, which enables LLM agents to plan and execute multi-step theoretical calculations. Symbolic computation poses a distinctive reliability challenge for LLM agents, as correctness is governed by implicit mathematical conventions that are not encoded in a form that can be easily checked in the computational backend. We identify two complementary remedies, tool-constrained computation and targeted knowledge grounding, and pursue the first as the primary architecture. Concretely, we concentrate the agent's action distribution onto tool calls with convention-fixing semantics, in which the agent specifies a compact, human-auditable diagram specification and a trusted backend performs the symbolic or numerical manipulations exactly. The toolkit provides two complementary calculation paths consuming a shared diagram specification: Naive Dimensional Analysis (NDA) for order-of-magnitude rate estimates and Exact Diagrammatic Analysis (EDA) for tree-level symbolic calculations via automatic FeynCalc code generation, both supplemented by automatic Feynman diagram enumeration and a navigable theory knowledge base. The architecture is validated on two benchmarks: (1) an exhaustive catalog of all tree-level, single-vertex $1\to 2$ partial decay widths across scalar, fermion, and vector parents, with complete massless and threshold limits and Standard Model validation; and (2) an NDA sensitivity study of the muon decay multiplicity $μ^+ \to ν_μ\barν_e + n(e^+e^-) + e^-$, determining the maximum observable $n$ at current and planned muon experiments.

Menzo, Tony [Alabama U.; Fermilab] (ORCID:00000002↗

DOE OSTI2025

A Quantum Computational Determination of the Weak Mixing Angle in the Standard Model

The weak mixing angle $s_W$ is a fundamental constant in the Standard Model (SM) and measured at the $Z$ boson mass to be $\widehat{s}^2_W(m_Z) = 0.23129 \pm 0.00004$ in the $\overline{\rm MS}$ renormalization scheme, where $m_Z=91.2\ \text{GeV}$. On the other hand, non-stabilizerness - the magic - characterizes the computational advantage of a quantum system over classical computers. We consider the production of magic from stabilizer initial states, which carry zero magic, in the 2-to-2 scattering of charged leptons in the SM at the tree level, which is mediated by the photon and the $Z$ boson. Using the second order stabilizer Rényi entropy, and averaging over all 60 initial stabilizer states and the scattering angle, we compute and minimize the magic production as a function of $s^2_W$ in the Møller scattering $e^-e^-\to e^-e^-$, which is free of kinematic thresholds. At the centre-of-mass energy $\sqrt{s}=m_Z$, there is a unique minimum in magic production at $\mathbf{s}^{2}_W(m_Z)=0.2317$, which agrees with the measured $\widehat{s}^2_W(m_Z)$ at the sub-percent level. At higher energies, the magic-minimizing $\mathbf{s}^{2}_W$ continues to agree with the empirical value at the percent level or better, up to 10 TeV. The finding suggests the electroweak sector of the SM tends to generate minimal quantum resources from the computational viewpoint.

Liu, Qiaofeng [Northwestern U.]↗

DOE OSTI2025

A Quantum Computational Determination of the Weak Mixing Angle in the Standard Model

The weak mixing angle $s_W$ is a fundamental constant in the Standard Model (SM) and measured at the $Z$ boson mass to be $\widehat{s}^2_W(m_Z) = 0.23129 \pm 0.00004$ in the $\overline{\rm MS}$ renormalization scheme, where $m_Z=91.2\ \text{GeV}$. On the other hand, non-stabilizerness - the magic - characterizes the computational advantage of a quantum system over classical computers. We consider the production of magic from stabilizer initial states, which carry zero magic, in the 2-to-2 scattering of charged leptons in the SM at the tree level, which is mediated by the photon and the $Z$ boson. Using the second order stabilizer Rényi entropy, and averaging over all 60 initial stabilizer states and the scattering angle, we compute and minimize the magic production as a function of $s^2_W$ in the Møller scattering $e^-e^-\to e^-e^-$, which is free of kinematic thresholds. At the centre-of-mass energy $\sqrt{s}=m_Z$, there is a unique minimum in magic production at $\mathbf{s}^{2}_W(m_Z)=0.2317$, which agrees with the measured $\widehat{s}^2_W(m_Z)$ at the sub-percent level. At higher energies, the magic-minimizing $\mathbf{s}^{2}_W$ continues to agree with the empirical value at the percent level or better, up to 10 TeV. The finding suggests the electroweak sector of the SM tends to generate minimal quantum resources from the computational viewpoint.

Liu, Qiaofeng [Northwestern U.]↗

DOE OSTI2023

Compute in‐Memory with Non‐Volatile Elements for Neural Networks: A Review from a Co‐Design Perspective

Abstract Deep learning has become ubiquitous, touching daily lives across the globe. Today, traditional computer architectures are stressed to their limits in efficiently executing the growing complexity of data and models. Compute‐in‐memory (CIM) can potentially play an important role in developing efficient hardware solutions that reduce data movement from compute‐unit to memory, known as the von Neumann bottleneck. At its heart is a cross‐bar architecture with nodal non‐volatile‐memory elements that performs an analog multiply‐and‐accumulate operation, enabling the matrix‐vector‐multiplications repeatedly used in all neural network workloads. The memory materials can significantly influence final system‐level characteristics and chip performance, including speed, power, and classification accuracy. With an over‐arching co‐design viewpoint, this review assesses the use of cross‐bar based CIM for neural networks, connecting the material properties and the associated design constraints and demands to application, architecture, and performance. Both digital and analog memory are considered, assessing the status for training and inference, and providing metrics for the collective set of properties non‐volatile memory materials will need to demonstrate for a successful CIM technology.

36 MATERIALS SCIENCE↗