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DOE OSTI2021

Fast and Scalable Sparse Triangular Solver for Multi-GPU Based HPC Architectures

Designing efficient and scalable sparse linear algebra kernels on modern multi-GPU based HPC systems is a daunting task due to significant irregular memory references and workload imbalance across the GPUs. This is particularly the case for \textit{Sparse Triangular Solver (SpTRSV)} which introduces additional two-dimensional computation dependencies among subsequent computation steps. Dependency information is exchanged and shared among GPUs, thus warrant for efficient memory allocation, data partitioning, and workload distribution as well as fine-grained communication and synchronization support. In this work, we demonstrate that directly adopting unified memory can adversely affect the performance of SpTRSV on multi-GPU architectures, despite linking via fast interconnect like NVLinks and NVSwitches. Alternatively, we employ the latest NVSHMEM technology based on Partitioned Global Address Space programming model to enable efficient fine-grained communication and drastic synchronization overhead reduction. Furthermore, to handle workload imbalance, we propose a malleable task-pool execution model which can further enhance the utilization of GPUs. By applying these techniques, our experiments on the NVIDIA multi-GPU supernode V100-DGX-1 and DGX-2 systems demonstrate that our design can achieve on average 3.53x (up to 9.86x) speedup on a DGX-1 system and 3.66x (up to 9.64x) speedup on a DGX-2 system with 4-GPUs over the Unified-Memory design. The comprehensive sensitivity and scalability studies also show that the proposed zero-copy SpTRSV is able to fully utilize the computing and communication resources of the multi-GPU system.

Xie, Chenhao↗

DOE OSTI2023

A Functional Reference Architecture and Assessment Thereof for the National Ignition Facility Industrial Controls Systems

The Industrial Control System (ICS) at the National Ignition Facility (NIF) has an effective, well-established architecture based off a conventional cyclical software paradigm focused on maintainability and the identification of issues. This architecture exhibits scalability in ensuring expansions of the system conform to the existing architecture, modularity enough to allow relatively easy integrations of such expansions and works as a successful tool to introduce control system engineers new to the NIF ICS to the structure of the system at each layer. This architecture, like most software architectures, is object-oriented, lending itself to ease of understanding by control systems engineers and software engineers familiar with an object-oriented perspective. There are occasions, however, where engineers of other disciplines require insight into the functionality and structure of the ICS for the purposes of understanding fundamentally how their own system is or will be governed by the ICS, without the need for the details of operation of the ICS or the object-oriented view. For this reason, a functional architecture of the ICS could be a potent tool for communicating this insight. Even more powerful, a generalization of this proposed functional ICS architecture in the form of a National Ignition Facility and Photon Science (NIF & PS) Industrial Controls Reference Architecture could communicate this insight not just to systems governed by the ICS in the NIF proper, but across entirety of the NIF & PS Principal Associate Directorate (PAD), anywhere an instance of the ICS architecture is present, such as the approximately 40 “small labs” distributed across the directorate. Such a tool will provide an alternative means of understanding the implementation of these control systems, conducive to a larger variety of engineering and scientific disciplines.

42 ENGINEERING↗

DOE OSTI2021

A-SST Initial Specification

The U.S. Army Research Office (ARO), in partnership with IARPA, are investigating innovative, efficient, and scalable computer architectures that are capable of executing next-generation large scale data-analytic applications. These applications are increasingly sparse, unstructured, non-local, and heterogeneous. Under the Advanced Graphic Intelligence Logical computing Environment (AGILE) program, Performer teams will be asked to design computer architectures to meet the future needs of the DoD and the Intelligence Community (IC). This design effort will require flexible, scalable, and detailed simulation to assess the performance, efficiency, and validity of their designs. To support AGILE, Sandia National Labs will be providing the AGILE-enhanced Structural Simulation Toolkit (A-SST). This toolkit is a computer architecture simulation framework designed to support fast, parallel, and multi-scale simulation of novel architectures. This document describes the A-SST framework, some of its library of simulation models, and how it may be used by AGILE Performers.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1991

High-performance multiprocessor architecture for a 3-D lattice gas model

The lattice gas method has recently emerged as a promising discrete particle simulation method in areas such as fluid dynamics. We present a very high-performance scalable multiprocessor architecture, called ALGE, proposed for the simulation of a realistic 3-D lattice gas model, Henon's 24-bit FCHC isometric model. Each of these VLSI processors is as powerful as a CRAY-2 for this application. ALGE is scalable in the sense that it achieves linear speedup for both fixed and increasing problem sizes with more processors. The core computation of a lattice gas model consists of many repetitions of two alternating phases: particle collision and propagation. Functional decomposition by symmetry group and virtual move are the respective keys to efficient implementation of collision and propagation.

Lee, F.↗

DOE OSTI2021

A scalable algorithm for the optimization of neural network architectures

In this work, we propose a new scalable method to optimize the architecture of an artificial neural network. The proposed algorithm, called Greedy Search for Neural Network Architecture, aims to determine a neural network with minimal number of layers that is at least as performant as neural networks of the same structure identified by other hyperparameter search algorithms in terms of accuracy and computational cost. Numerical results performed on benchmark datasets show that, for these datasets, our method outperforms state-of-the-art hyperparameter optimization algorithms in terms of attainable predictive performance by the selected neural network architecture, and time-to-solution for the hyperparameter optimization to complete.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

A Unified Design Theory for Multi-Port Polyphase Transformers Enabling Scalable Power-Multiplexed EV Fleet Charging Systems

This paper presents a unified analytical design theory for multi-port polyphase transformers, targeting scalable and isolated high-power Electric Vehicle (EV) fleet charging systems with power multiplexing capability. As fleet electrification accelerates, conventional one-to-one charger architectures face significant challenges in infrastructure cost, peak power demand, and low utilization of installed power electronics. Power-multiplexed charging architectures, which dynamically distribute power from a shared pool of converter modules across multiple vehicles, have emerged as a promising solution. However, such architectures require scalable, isolated multi-port power interfaces capable of routing energy among multiple inputs and outputs, whose design remains complex and dependent on iterative modeling. To address this gap, the proposed theory provides closed-form expressions for self-inductance, leakage inductance, and mutual coupling terms for arbitrary multi-phase, multi-port transformer structures. The formulation enables direct synthesis of isolated multi-input and multi-output resonant converter systems without reliance on geometry-specific finite-element analysis or extensive parameter extraction. This capability is particularly critical for power-multiplexed systems, where modular converter structures must interface with multiple vehicles while maintaining galvanic isolation and flexible power allocation. The effectiveness of the proposed framework is demonstrated through the design of a 360 kW multi-phase system operating over a 700–900 VDC input and 400–1250 VDC output range. PLECS simulation results confirm accurate prediction of system behavior and validate the applicability of the approach to multi-port, power-multiplexed charging scenarios. The proposed method significantly reduces design complexity while enabling scalable, cost-effective, and fully utilized EV fleet charging infrastructure.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗

DOE OSTI2026

A Review of Software for Designing and Operating Quantum Networks

Quantum networks development is crucial to realizing a production-grade network that can support distributed sensing, secure communication, and utility-scale quantum computation. However, the transition from laboratory demonstration to deployable networks requires software implementations of architectures and protocols tailored to the unique constraints of quantum systems. This paper reviews the current state of software implementations for quantum networks, organized around a three-plane abstraction of infrastructure, logical, and control/service planes. We cover software for both designing quantum network protocols (e.g., SeQUeNCe, QuISP, and NetSquid) and operating testbeds, with a focus on essential control/service plane functions such as entanglement, topology, and resource management, in a proposed taxonomy. Our review highlights a persistent gap between theoretical architecture and protocol proposals and their realization in simulators or testbeds, particularly in dynamic topology and network management. We conclude by outlining open challenges and proposing a roadmap for developing scalable software architectures to enable hybrid, large-scale quantum networks.

Network Design↗

DOE OSTI2022

Probabilistic Neural Computing with Stochastic Devices

Abstract The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event‐driven, and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain's ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications. To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures. A co‐design vision is described by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks. Finally, a framework is presented to categorize increasingly advanced hardware‐based probabilistic computing technologies.

Misra, Shashank↗

NASA NTRS1995

CFD Research, Parallel Computation and Aerodynamic Optimization

During the last five years, CFD has matured substantially. Pure CFD research remains to be done, but much of the focus has shifted to integration of CFD into the design process. The work under these cooperative agreements reflects this trend. The recent work, and work which is planned, is designed to enhance the competitiveness of the US aerospace industry. CFD and optimization approaches are being developed and tested, so that the industry can better choose which methods to adopt in their design processes. The range of computer architectures has been dramatically broadened, as the assumption that only huge vector supercomputers could be useful has faded. Today, researchers and industry can trade off time, cost, and availability, choosing vector supercomputers, scalable parallel architectures, networked workstations, or heterogenous combinations of these to complete required computations efficiently.

Ryan, James S.↗

DOE OSTI2020

Distributed Ledger Technology Preliminary Performance Assessment for Applications in Utilities Operational Technology

This paper provides descriptions of the key components of different distributed ledger technology platforms. Distributed ledger technology (DLT) allows for distribution of databases among different organizations and devices. The platforms use cryptographically linked “blocks” to store and verify transactional information between these organizations. DLT increases data security, data integrity, trust among its participants. Different organizations are looking to deploy this distributed and decentralized approach to avoid the single-point-of-failure vulnerabilities associated with centralized data repositories. In this study we examine twelve different DLT platforms. There is agreement within the community that of all the platforms considered here, Hyperledger and Ethereum are the most mature when it comes to privacy and permissions. These DLT platforms are being used for applications such as transactive energy, health care, and the food and goods supply chain. However, further development is required to realize the full promise of DLT. Our assessment includes a general description of each DLT and its key characteristics. Such characteristics include consensus protocol and cryptography used, public vs. private, and permissioned or permissionless. The selection and implementation of a DLT architecture depends heavily on the use case and performance requirements. During this research we found key parameters to measure performance and existing tools for assessment. Four different parameters were identified 1) consensus, 2) throughput, 3) latency, and 4) scalability. The architectures of Hyperledger Caliper and Blockbench are described as different performance assessment frameworks. From this preliminary study it is evident that there are dissimilarities on the performance assessments methods developers and users are characterizing DLT architectures. The purpose of this paper is to identify key parameters to test performance, tools that are being used and provide information on results from previous studies.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2023

Procedure Automation Rating Matrix

The National Aeronautics and Space Administration (NASA) Advanced Air Mobility (AAM) National Campaign (NC) is researching the means by which future Urban Air Mobility (UAM) aircraft will operate safely in an integrated and scalable airspace architecture. Consistent with this objective, the NASA NC Airspace Procedures team designed a matrix to evaluate UAM instrument flight procedure design, flyability and interoperability of candidate departure, enroute, and approach architectures in live flight or simulation. The Procedure Automation Rating Matrix (PARM) is a multi-dimensional rating scale designed to provide direct feedback from test pilots and operators to airspace procedure designers developing airspace constructs for the integration and scalability of AAM operations in the National Airspace System (NAS). The PARM is assessed using a hierarchical decision tree that guides the operator through a ten-point alpha-numeric rating scale initiated either with or without the use of automation.

National Campaign↗

DOE OSTI2024

Invariant Features for Accurate Predictions of Quantum Chemical UV-vis Spectra of Organic Molecules

Including invariance of global properties of a phys-ical system as an intrinsic feature in graph neural networks (GNNs) enhances the model's robustness and generalizability and reduces the amount of training data required to obtain a desired accuracy for predictions of these properties. Existing open source GNN libraries construct invariant features only for specific GNN architectures. This precludes the generalization of invariant features to arbitrary message passing neural network (MPNN) layers which, in turn, precludes the use of these libraries for new, user-specified predictive tasks. To address this limitation, we implement invariant MPNNs into the flexible and scalable HydraGNN architecture. HydraGNN enables a seamless switch between various MPNNs in a unified layer sequence and allows for a fair comparison between the predictive performance of different MPNNs. We trained this enhanced HydraGNN archi-tecture on the ultraviolet-visible (UV-vis) spectrum of GDB-9 molecules, a feature that describes the molecule's electronic exci-tation modes, computed with time-dependent density functional tight binding (TD-DFTB) and available open source through the GDB-9-Ex dataset. We assess the robustness (i.e., accuracy and generalizability) of the predictions obtained using different invariant MPNNs with respect to different values of the full width at half maximum (FWHM) for the Gaussian smearing of the theoretical peaks. Our numerical results show that incorporating invariance in the HydraGNN architecture significantly enhances both accuracy and generalizability in predicting UV-vis spectra of organic molecules.

Baker, Justin↗

NASA NTRSDate not supplied

The International Space Station, Optical Communications, and Delay Tolerant Networking: Towards A Solar System Internet Architecture

As Delay Tolerant Networking (DTN) matures as a software product its use cases have extended to infrastructural and architectural studies, bringing DTN closer to a widespread, operational technology. While it is known that a DTN can be configured, given complete information about a system of nodes, to provision a system with a complete network capability, what remains is to tackle practical considerations such as scalability, networking best practices, and how to establish service providers. In this paper, we document progress towards a scalable DTN architecture that was tested across multiple organizational and project boundaries; our approach was tested across nodes flying on the International Space Station (ISS) connected to a ground network. The network consists of four main network areas: the ISS, the White Sands Complex (WSC), the Mission Cloud Platform (MCP), and NASA’s Huntsville Operations Support Center (HOSC). The connections on the ground (WSC, MCP, and HOSC) are straightforward technically, but represent administrative and non-technical challenges that had to be overcome. The connection from the ISS to WSC was realized through a hybrid optical/RF means. In particular, NASA’s Integrated Laser Communications Relay Demonstration (LCRD) can form a relay from lowEarth-orbit (LEO) to the Earth, and was used in conjunction with the LCRD LEO User Modem and Amplifier Terminal (ILLUMA-T) on the ISS to form a link to one of three geographically diverse ground stations. A major goal was to achieve communications while keeping the ISS nodes unaware of ground station choices and scheduling. These disparate links were networked using High-rate DTN (HDTN), which enabled the desired architecture. The network architecture, tests, HDTN performance metrics, and general observations made are all discussed in detail, including a discussion on the successful utilization of the DTN security standard known as DTN Bundle Protocol Security (BPSec). We conclude with suggestions for next steps, and in particular focus on extending this architecture to the Near Space Network (NSN) and the upcoming LunaNet.

Alan Hylton↗

NASA NTRS1997

The ASC Data Archive for the AXAF Ground Calibration

A data archive is near completion at the AXAF Science Center (ASC) to store and provide access to AXAF data. The archive is a distributed client/server system. It consists of a number of different servers which handle flat data files, relational data, replication across multiple sites, and the interface to the WWW. There is a 4GL client interface for each type of data server, C++ and Java APIs, and a number of standard clients to archive and retrieve data. The architecture is scalable and configurable in order to accommodate future data types and increasing data volumes. The first release of the system became available in August 1996 and has been successfully operated since then in support of the AXAF calibration at the George C. Marshall Space Flight Center, Huntsville, AL. This paper will present the overall archive architecture and the design of client and server components. It will also review the ground calibration data contents, the plan for flight data, and the access to the data by ASC internal users and processing pipelines.

Zografou, Panagoula↗

DOE OSTI2026

Analog-to-digital converter based on voltage-controlled superconducting devices

The increasing demand for cryogenic electronics in superconducting and quantum computing systems calls for ultra-energy-efficient data conversion architectures that remain functional at deep cryogenic temperatures. Here, in this work, we present the first design of a voltage-controlled superconducting flash analog-to-digital converter (ADC) based on a voltage-controlled quantum-enhanced Josephson junction field-effect transistor (JJFET). Exploiting its strong gate tunability and transistor-like behavior, the JJFET offers a scalable alternative to conventional current-controlled superconducting devices while aligning naturally with CMOS-style design methodologies. Building on our previously developed Verilog-A compact model calibrated to experimental data, we design and simulate a three-bit JJFET-based flash ADC targeted for integration within cryogenic control and readout circuitry in quantum computing. The core comparator block is realized through careful bias current selection and augmented with a three-terminal nanocryotron to precisely define reference voltages. Cascaded JJFET comparators ensure robust voltage gain, cascadability, and logic-level restoration across stages. Simulation results demonstrate accurate quantization behavior with ultra-low power dissipation, underscoring the feasibility of voltage-driven superconducting mixed-signal circuits. This work establishes a critical step toward unifying superconducting logic and data conversion, paving the way for scalable cryogenic architectures in quantum–classical co-processors, low-power artificial intelligence accelerators, and next-generation energy-constrained computing platforms.

Analog-to-digital converter↗

NASA NTRSDate not supplied

The Adaptable, Deployable, Entry and Placement Technology (ADEPT) Enabling Advanced Entry, Descent, and Landing Capabilities for SmallSat Missions

The Adaptable, Deployable Entry and Placement Technology (ADEPT) is a mechanically deployable low-ballistic coefficient aeroshell entry system which performs entry, descent, and landing (EDL) functions for a broad range of planetary destinations including Mars, Venus, Titan and Earth. The deployable system allows mission planners to develop an aeroshell design that is stowed like a folded umbrella, and yet prior to the EDL mission segment, transforms into a deployed, near-rigid low ballistic coefficient configuration. The ADEPT architecture is scalable from a sub-1m class to 6m diameter entry vehicles for Venus and and it has been studied at up to 15m – 20m scale for human exploration class missions. There has been growing interest in performing interplanetary missions with small spacecraft as secondary payloads and ADEPT offers small satellite mission designers the ability to consider Aeroassist technologies to expand mission applications. The ADEPT architecture introduces a new approach for entry vehicle design. It deploys a flexible 3D woven carbon fabric as the primary drag surface. It is capable of surviving high aerothermal heating during entry, while maintaining a robust impermeable membrane that provides high drag for vehicle deceleration. The ADEPT project team has advanced this decelerator technology via systems-level testing at the one meter diameter (nano-ADEPT) scale. A subsonic aeroloads test (May 2015) and a pathfinder arc-jet aeroheating test (Sept 2015) and a successful sounding rocket flight (SR-1) launch of a 0.7 meter deployed diameter ADEPT (September 2018). The SR-1 flight experiment demonstrated most of the primary end-to-end mission stages including: launch in a stowed configuration, separation and deployment in zero-g, exo-atmospheric conditions, and passive ballistic re-entry of a 70 degree half-angle cone geometry. The ADEPT SR-1 sounding rocket flight experiment had to address challenges such as stowing within the constrained payload envelope, fabric folding, and packaging concerns.

Paul Wercinski↗

DOE OSTI2025

Proton Selective Nanoporous Atomically Thin Graphene Membranes for Vanadium Redox Flow Batteries

Angstrom-scale proton-selective pores in atomically thin 2D materials present fundamentally new opportunities for advancing proton exchange membranes (PEMs). Vanadium Redox Flow Batteries (VRFBs) for grid-scale energy storage require PEMs with high areal proton conductance (>1 S cm −2 ) and minimal vanadium ion (VO 2+ ) crossover. However, state-of-the-art Nafion 212 membranes (N212 ≈50 µm thick), suffer from persistent VO 2+ crossover reducing performance and efficiency. Here, a layered PEM is demonstrated, comprising monolayer CVD graphene with Angstrom-scale proton-selective pores introduced via Ar plasma, integrated with an ultra-thin ≈300 nm polybenzimidazole (PBI) layer and sandwiched between two Nafion 211 (25 µm thick) layers. The layered architecture facilitates scalable membrane fabrication by mitigating defects while processing and facile stacking of graphene layers allows stochastic non-selective defect isolation enabling exceptionally low VO 2+ crossover (selectivity (H + areal conductance / VO 2+ permeability) ≈6709 × 10 6 S min cm −4 ), with proton conductance >8 S cm −2 . Systematic transport experiments supported by resistance-based transport modelling elucidate the role of defect size, defect isolation, and sealing, as well as layering/stacking, to enable orders of magnitude (>671× over N212) improvements in selectivity, along with areal proton conductance >8 S cm −2 . This work highlights the potential of atomic-scale proton-selective defect engineering in 2D materials, in conjunction with facile stacking and layering of materials as strategies for scalable, high-performance advances in PEMs for energy, electrochemical, and separation applications beyond VRFBs.

Chaturvedi, Pavan [Vanderbilt Univ., Nashville, TN↗

DOE OSTI2020

Fouling modeling and prediction approach for heat exchangers using deep learning

In this article, we develop a generalized and scalable statistical model for accurate prediction of fouling resistance using commonly measured parameters of industrial heat exchangers. This prediction model is based on deep learning where a scalable algorithmic architecture learns non-linear functional relationships between a set of target and predictor variables from large number of training samples. Here, the efficacy of this modeling approach is demonstrated for predicting fouling in an analytically modeled cross-flow heat exchanger, designed for waste heat recovery from flue-gas using room temperature water. The performance results of the trained models demonstrate that the mean absolute prediction errors are under 10 –4 KW –1 for flue-gas side, water side and overall fouling resistances. The coefficients of determination (R 2 ), which characterize the goodness of fit between the predictions and observed data, are over 99%. Even under varying levels of measurement noise in the inputs, we demonstrate that predictions over an ensemble of multiple neural networks achieves better accuracy and robustness to noise. We find that the proposed deep-learning fouling prediction framework learns to follow heat exchanger flow and heat transfer physics, which we confirm using locally interpretable model agnostic explanations around randomly selected operating points. Overall, we provide a robust algorithmic framework for fouling prediction that can be generalized and scaled to various types of industrial heat exchangers.

42 ENGINEERING↗