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DOE OSTI2025

Synthetic-domain computing and neural networks using lithium niobate integrated nonlinear phononics

Analogue computing uses the physical behaviours of devices to provide energy-efficient arithmetic operations. However, scaling up analogue computing platforms by simply increasing the number of devices leads to challenges such as device-to-device variation. Here, in this study, we report scalable analogue computing and neural networks in the synthetic frequency domain using an integrated nonlinear phononic platform on lithium niobate. This synthetic-domain computing is robust to device variations, as vectors and matrices are concurrently encoded at different frequencies within a single device, achieving a high throughput per area. Leveraging inherent nonlinearities, our device-aware neural network can perform a four-class classification task with an accuracy of 98.2%. The nonlinear phononic computing hardware also maintains consistent performance over a wide operational temperature range (characterized up to 192 °C). Our synthetic-domain computing combines single-device parallelism, inherent nonlinearity and environmental stability, and could be of use in edge computing applications in which power efficiency and environmental resilience are crucial.

Ji, Jun [Virginia Polytechnic Inst. and State Univ↗

DOE OSTI2026

Statistical inference of anomalous thermal transport with uncertainty quantification for interpretive 2D SOL models

The critical task of inferring anomalous cross-field transport coefficients is addressed in simulations of boundary plasmas with fluid models. A workflow for parameter inference in the UEDGE fluid code is developed using Bayesian optimization with parallelized sampling and integrated uncertainty quantification. In this workflow, transport coefficients are inferred by maximizing their posterior probability distribution, which is generally multidimensional and non-Gaussian. Uncertainty quantification is integrated throughout the optimization within the Bayesian framework that combines diagnostic uncertainties and model limitations. As a concrete example, we infer the anomalous electron thermal diffusivity $\chi_\perp$ from an interpretive 2D model describing electron heat transport in the conduction-limited region with radiative power loss. The workflow is first benchmarked against synthetic data and then tested on H-, L-, and I-mode discharges to match their midplane temperature and divertor heat flux profiles. We demonstrate that the workflow efficiently infers diffusivity and its associated uncertainty, generating 2D profiles that match 1D measurements. Future efforts will focus on incorporating more complicated fluid models and analyzing transport coefficients inferred from a large database of experimental results.

Bayesian optimization↗

DOE OSTI2021

WIRE: Resource-efficient Scaling with Online Prediction for DAG-based Workflows

This paper introduces WIRE that manages resources for the DAG-based workflows on IaaS clouds. WIRE predicts and plans resources over the MAPE (Monitor-Analyze-Plan-Execute) loops to: 1) Estimate task performance with online data, 2) Conduct simulations to predict the upcoming loads based on online estimates and workflow DAGs, 3) Apply a resource-steering policy to size cloud instance pools for the maximal parallelism that is consistent with low cost. We implement WIRE on Pegasus WMS/HTCondor and evaluate its performance on the ExoGENI network cloud. The results show that WIRE attains low resource cost with the performance that is typically within a factor of two of optimal.

Xie, Bing↗

DOE OSTI2015

Porting Ordinary Applications to Blue Gene/Q Supercomputers

Efficiently porting ordinary applications to Blue Gene/Q supercomputers is a significant challenge. Codes are often originally developed without considering advanced architectures and related tool chains. Science needs frequently lead users to want to run large numbers of relatively small jobs (often called many-task computing, an ensemble, or a workflow), which can conflict with supercomputer configurations. In this paper, we discuss techniques developed to execute ordinary applications over leadership class supercomputers. We use the high-performance Swift parallel scripting framework and build two workflow execution techniques-sub-jobs and main-wrap. The sub-jobs technique, built on top of the IBM Blue Gene/Q resource manager Cobalt's sub-block jobs, lets users submit multiple, independent, repeated smaller jobs within a single larger resource block. The main-wrap technique is a scheme that enables C/C++ programs to be defined as functions that are wrapped by a high-performance Swift wrapper and that are invoked as a Swift script. We discuss the needs, benefits, technicalities, and current limitations of these techniques. We further discuss the real-world science enabled by these techniques and the results obtained.

Maheshwari, Ketan C.↗

DOE OSTI2025

Alquimia v1.0: a generic interface to biogeochemical codes – a tool for interoperable development, prototyping and benchmarking for multiphysics simulators

Alquimia v1.0 is a generic interface to geochemical solvers that facilitates development of multiphysics simulators by enabling code coupling, prototyping and benchmarking. The interface enforces the function arguments and their types for setting up, solving, serving up output data and carrying out other common auxiliary tasks while providing a set of structures for data transfer between the multiphysics code driving the simulation and the geochemical solver. Alquimia relies on a single-cell approach that permits operator splitting coupling and parallel computation. We describe the implementation in Alquimia of two widely used open-source codes that perform geochemical calculations: PFLOTRAN and CrunchFlow. We then exemplify its use for the implementation and simulation of reactive transport in porous media by two open-source flow and transport simulators: Amanzi and ParFlow. We also demonstrate its use for the simulation of coupled processes in novel multiphysics applications including the effect of multiphase flow on reaction rates at the pore scale with OpenFOAM, the role of complex biogeochemical processes in land surface models such as the E3SM Land Model (ELM) and the impact of surface–subsurface hydrological interactions on hydrogeochemical export from watersheds with the Advanced Terrestrial Simulator (ATS). These applications make it apparent that the availability of a well-defined yet flexible interface has the potential to improve the software development workflow, freeing up resources to focus on advances in process models and mechanistic understanding of coupled problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1972

Development of programs for computing characteristics of ultraviolet radiation

Efficient programs were developed for computing all four characteristics of the radiation scattered by a plane-parallel, turbid, terrestrial atmospheric model. They were developed (FORTRAN 4) and tested on the IBM /360 computers with 2314 direct access storage facility. The storage requirement varies between 200K and 750K bytes depending upon the task. The scattering phase matrix (or function) is expanded in a Fourier series whose number of terms depend upon the zenith angles of the incident and scattered radiations, as well as on the nature of aerosols. A Gauss-Seidel procedure is used for obtaining the numerical solution of the transfer equation.

Dave, J. V.↗

NASA NTRS2004

Space Biotechnology and Commercial Applications University of Florida

The Space Biotechnology and Commercial Applications grant was funded by NASA's Kennedy Space Center in FY 2002 to provide dedicated biotechnology and agricultural research focused on the regeneration of space flight environments with direct parallels in Earth-based applications for solving problems in the environment, advances in agricultural science, and other human support issues amenable to targeted biotechnology solutions. This grant had three project areas, each with multiple tasks. They are: 1) Space Agriculture and Biotechnology Research and Education, 2) Integrated Smart Nanosensors for Space Biotechnology Applications, and 3) Commercial Applications. The Space Agriculture and Biotechnology Research and Education (SABRE) Center emphasized the fundamental biology of organisms involved in space flight applications, including those involved in advanced life support environments because of their critical role in the long-term exploration of space. The SABRE Center supports research at the University of Florida and at the Space Life Sciences Laboratory (SLSL) at the Kennedy Space Center. The Integrated Smart Nanosensors for Space Biotechnology Applications component focused on developing and applying sensor technologies to space environments and agricultural systems. The research activities in nanosensors were coordinated with the SABRE portions of this grant and with the research sponsored by the NASA Environmental Systems Commercial Space Technology Center located in the Department of Environmental Engineering Sciences. Initial sensor efforts have focused on air and water quality monitoring essential to humans for living and working permanently in space, an important goal identified in NASA's strategic plan. The closed environment of a spacecraft or planetary base accentuates cause and effect relationships and environmental impacts. The limited available air and water resources emphasize the need for reuse, recycling, and system monitoring. It is essential to collect real-time information from these systems to ensure crew safety. This new class of nanosensors will be critical to monitoring the space flight environment in future NASA space systems. The Commercial Applications component of this program pursued industry partnerships to develop products for terrestrial use of NASA sponsored technologies, and in turn to stimulate growth in the biotechnology industry. For technologies demonstrating near term commercial potential, the objective is to include industry partners on or about the time of proof of concept that will not only co-invest in the technology but also take the resultant technology to the commercial market.

Phillips, Winfred↗

DOE OSTI2015

Fencing data transfers in a parallel active messaging interface of a parallel computer

Fencing data transfers in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI including data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task; the compute nodes coupled for data communications through the PAMI and through data communications resources including at least one segment of shared random access memory; including initiating execution through the PAMI of an ordered sequence of active SEND instructions for SEND data transfers between two endpoints, effecting deterministic SEND data transfers through a segment of shared memory; and executing through the PAMI, with no FENCE accounting for SEND data transfers, an active FENCE instruction, the FENCE instruction completing execution only after completion of all SEND instructions initiated prior to execution of the FENCE instruction for SEND data transfers between the two endpoints.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Fencing data transfers in a parallel active messaging interface of a parallel computer

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Fencing data transfers in a parallel active messaging interface of a parallel computer

Fencing data transfers in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI including data communications endpoints, each endpoint comprising a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes coupled for data communications through the PAMI and through data communications resources including a deterministic data communications network, including initiating execution through the PAMI of an ordered sequence of active SEND instructions for SEND data transfers between two endpoints, effecting deterministic SEND data transfers; and executing through the PAMI, with no FENCE accounting for SEND data transfers, an active FENCE instruction, the FENCE instruction completing execution only after completion of all SEND instructions initiated prior to execution of the FENCE instruction for SEND data transfers between the two endpoints.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Fencing data transfers in a parallel active messaging interface of a parallel computer

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

A Scalable HPC Insider Threat Monitoring System

Cyber Equations, Inc. is reporting on the results of its DOE SBIR Phase I project for the development of a Scalable HPC Insider Threat Monitoring System. This final report to the Department of Energy for Task DE-FOA-0001618 covers the period from 02/21/2017 to 02/20/2018 and summarizes the larger research accomplishments, which can be divided in theoretical and laboratory experiments. One of the many challenges for the U.S. Department of Energy (DOE) is the support of the high-performance computing (HPC) centers while providing quality information services, strategic vision, leadership, technical expertise, and capabilities necessary to enable the DOE to deliver effective, efficient, economical, and secure HPC services. In order to realize a secure DOE laboratory enterprise, DOE facilities operations must counter a wide range of cyber insider threats, from unsophisticated adversaries up to advanced, nation state sponsored actors. The DOE laboratories also presents an enormous insider attack surface of an ever-growing number of computers, networks, and devices all running a myriad of software. In addition, the DOE laboratories are connected to a variety of external organizations which multiplies the possibility of insider threat. During this SBIR project Cyber Equations developed a strategic combination of supervised and unsupervised machine learning techniques to control scale and classify whether entities, such as hosts, actors, or documents, are showing suspicious insider behavior. We conducted experiments using real cyber data to show that changes in observable behavior (e.g., changes in outbound traffic patterns) are indicative of attacks executing (e.g., insiders exfiltrating data, bots calling home, or an attack spreading). The features and methods we use, have been used in the anomaly detection research and development community for a significant amount of time and robust implementations are openly available. K-Means clustering, Decision Trees, and Support Vector Machine based classifiers are readily available through the R language and open source H20 machine learning framework. The Cyber Equations approach is novel in its use of these classifiers combined with Hadoop based parallel processing technology. The five stages of processing used by CyberInsider improve scalability and reduce the high number of false positives normally associated with network anomaly detection. In summary, the Cyber Equations has successfully accomplished all of its Phase I tasks and demonstrated the feasibility of the Insider Threat Monitoring System.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

CMS readiness for multi-core workload scheduling

In the present run of the LHC, CMS data reconstruction and simulation algorithms benefit greatly from being executed as multiple threads running on several processor cores. The complexity of the Run 2 events requires parallelization of the code to reduce the memory-per- core footprint constraining serial execution programs, thus optimizing the exploitation of present multi-core processor architectures. The allocation of computing resources for multi-core tasks, however, becomes a complex problem in itself. The CMS workload submission infrastructure employs multi-slot partitionable pilots, built on HTCondor and GlideinWMS native features, to enable scheduling of single and multi-core jobs simultaneously. This provides a solution for the scheduling problem in a uniform way across grid sites running a diversity of gateways to compute resources and batch system technologies. This paper presents this strategy and the tools on which it has been implemented. The experience of managing multi-core resources at the Tier-0 and Tier-1 sites during 2015, along with the deployment phase to Tier-2 sites during early 2016 is reported. The process of performance monitoring and optimization to achieve efficient and flexible use of the resources is also described.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2011

Granular Simulation of NEO Anchoring

NASA is interested in designing a spacecraft capable of visiting a Near Earth Object (NEO), performing experiments, and then returning safely. Certain periods of this mission will require the spacecraft to remain stationary relative to the NEO. Such situations require an anchoring mechanism that is compact, easy to deploy and upon mission completion, easily removed. The design philosophy used in the project relies on the simulation capability of a multibody dynamics physics engine. On Earth it is difficult to create low gravity conditions and testing in low gravity environments, whether artificial or in space is costly and therefore not feasible. Through simulation, gravity can be controlled with great accuracy, making it ideally suited to analyze the problem at hand. Using Chrono::Engine [1], a simulation package capable of utilizing massively parallel GPU hardware, several validation experiments will be performed. Once there is sufficient confidence, modeling of the NEO regolith interaction will begin after which the anchor tests will be performed and analyzed. The outcome of this task is a study with an analysis of several different anchor designs, along with a recommendation on which anchor is better suited to the task of anchoring. With the anchors tested against a range of parameters relating to soil, environment and anchor penetration angles/velocities on a NEO.

multibody dynamics↗

DOE OSTI2022

High Temperature Anode Recycle Blower for Solid Oxide Fuel Cell, Phase II (Final Report)

Broad commercialization of solid oxide fuel cells (SOFCs) requires anode offgas recycle blowers (ARCB) that are specifically designed for handling the challenging operating conditions presented by the SOFC process gases. Otherwise, they can be susceptible to frequent maintenance, low reliability, and short life. This report presents details of a DOE-funded Phase II effort conducted by Mohawk Innovative Technology, Inc. (MiTi®) for the development and testing of an oil-free, low cost, high-temperature centrifugal ARCB based on compliant foil bearing (CFB) technology for support of a 100 kW solid oxide fuel cell power plant. This Phase II builds on the results of a successful Phase I development project that resulted in the demonstration of a low TRL-6 prototype, shown in Figure 1. This Phase II final report presents design improvements over the Phase I prototype, presents the assembled test ARCBs, and discusses the advantages of this novel technology, particularly its long life and maintenance-free operation, which are derived from the use of CFBs. Also included are preliminary techno-economic analysis considerations for cost-effective deployment of the technology. The specific objectives of this project, as stated in the statement of program objectives (SOPO) were 1) to follow the methodology of design for manufacturing (or manufacturability) and assembly for implementing improvements identified as part of the Phase I effort with the purpose of reducing cost, enabling mass production, and facilitating the commercialization of a revised ARCB design, and 2) to fabricate four complete ARCB units based on the revised design and demonstrating their performance in both a laboratory setting and in an actual SOFC power plant. Execution of this SOPO would be supported by a number of technical tasks resulting in the fabrication and testing of the prototypes. To this end, MiTi continued the teaming relationship started during Phase I of the program with subcontracting partner FuelCell Energy, Inc. (FCE), which integrated (under a parallel effort funded by DOE Award DE-FE0026199) a modular 200 kWe SOFC power plant. MiTi and FCE coordinated to ultimately incorporate one of MiTi’s ARCB prototypes into one of the 100 kWe SOFC Modular Power Blocks (MPB) that constitute the core of FCE’s SOFC power plant for in-situ long-duration testing. Additionally, MiTi would explore the scalability and extendibility of the technology to other applications, as well as conduct a basic techno-economic analysis.

03 NATURAL GAS↗

DOE OSTI2018

Iterative Importance Sampling Algorithms for Parameter Estimation

In parameter estimation problems one computes a posterior distribution over uncertain parameters defined jointly by a prior distribution, a model, and noisy data. Markov chain Monte Carlo (MCMC) is often used for the numerical solution of such problems. An alternative to MCMC is importance sampling, which can exhibit near perfect scaling with the number of cores on high performance computing systems because samples are drawn independently. However, finding a suitable proposal distribution is a challenging task. Several sampling algorithms have been proposed over the past years that take an iterative approach to constructing a proposal distribution. We investigate the applicability of such algorithms by applying them to two realistic and challenging test problems, one in subsurface flow, and one in combustion modeling. More specifically, we implement importance sampling algorithms that iterate over the mean and covariance matrix of Gaussian or multivariate t-proposal distributions. Our implementation leverages massively parallel computers, and we present strategies to initialize the iterations using “coarse” MCMC runs or Gaussian mixture models.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1990

Parallel processors and nonlinear structural dynamics algorithms and software

Techniques are discussed for the implementation and improvement of vectorization and concurrency in nonlinear explicit structural finite element codes. In explicit integration methods, the computation of the element internal force vector consumes the bulk of the computer time. The program can be efficiently vectorized by subdividing the elements into blocks and executing all computations in vector mode. The structuring of elements into blocks also provides a convenient way to implement concurrency by creating tasks which can be assigned to available processors for evaluation. The techniques were implemented in a 3-D nonlinear program with one-point quadrature shell elements. Concurrency and vectorization were first implemented in a single time step version of the program. Techniques were developed to minimize processor idle time and to select the optimal vector length. A comparison of run times between the program executed in scalar, serial mode and the fully vectorized code executed concurrently using eight processors shows speed-ups of over 25. Conjugate gradient methods for solving nonlinear algebraic equations are also readily adapted to a parallel environment. A new technique for improving convergence properties of conjugate gradients in nonlinear problems is developed in conjunction with other techniques such as diagonal scaling. A significant reduction in the number of iterations required for convergence is shown for a statically loaded rigid bar suspended by three equally spaced springs.

Belytschko, Ted↗

DOE OSTI2019

Porting Adaptive Ensemble Molecular Dynamics Workflows to the Summit Supercomputer

Molecular dynamics (MD) simulations must take very small (femtosecond) integration steps in simulation-time to avoid numerical errors. Efficient use of parallel programming models and accelerators in state-of-the art MD programs now is pushing Moore’s limit for time-per-MD step. As a result, directly simulating timescales beyond milliseconds will not be attainable directly, even at exascale. However, concepts from statistical physics can be used to combine many parallel simulations to provide information about longer timescales and to adequately sample the simulation space, while preserving details about the dynamics of the system. Implementing such an approach requires a workflow program that allows adaptable steering of task assignments based on extensive statistical analysis of intermediate results. Here we report the implementation of such an adaptable workflow program to drive simulations on the Summit IBM Power System AC922, a pre-exascale supercomputer at the Oak Ridge Leadership Computing Facility (OLCF). We compare to experiences on Titan, Summit’s predecessor, report the performance of the workflow and its components, and describe the porting process. We find that using a workflow program managed by a Mongo database can provide the fault tolerance, scalable performance, task dispatch rate, and reconfigurability required for robust and portable implementation of ensemble simulations such as are used in enhanced-sampling molecular dynamics. This type of workflow generator can also be used to provide adaptive steering of ensemble simulations for other applications in addition to MD.

Ossyra, John R.↗