Search NASA⌕ Search

SEARCH · Search NASA

Results for “high performance and distributed computing”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

FISH: A THREE-DIMENSIONAL PARALLEL MAGNETOHYDRODYNAMICS CODE FOR ASTROPHYSICAL APPLICATIONS

FISH is a fast and simple ideal magnetohydrodynamics code that scales to {approx}10,000 processes for a Cartesian computational domain of {approx}1000{sup 3} cells. The simplicity of FISH has been achieved by the rigorous application of the operator splitting technique, while second-order accuracy is maintained by the symmetric ordering of the operators. Between directional sweeps, the three-dimensional data are rotated in memory so that the sweep is always performed in a cache-efficient way along the direction of contiguous memory. Hence, the code only requires a one-dimensional description of the conservation equations to be solved. This approach also enables an elegant novel parallelization of the code that is based on persistent communications with MPI for cubic domain decomposition on machines with distributed memory. This scheme is then combined with an additional OpenMP parallelization of different sweeps that can take advantage of clusters of shared memory. We document the detailed implementation of a second-order total variation diminishing advection scheme based on flux reconstruction. The magnetic fields are evolved by a constrained transport scheme. We show that the subtraction of a simple estimate of the hydrostatic gradient from the total gradients can significantly reduce the dissipation of the advection scheme in simulations of gravitationally bound hydrostatic objects. Through its simplicity and efficiency, FISH is as well suited for hydrodynamics classes as for large-scale astrophysical simulations on high-performance computer clusters. In preparation for the release of a public version, we demonstrate the performance of FISH in a suite of astrophysically orientated test cases.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

MICADO: Parallel implementation of a 2D-1D iterative algorithm for the 3D neutron transport problem in prismatic geometries

The large increase in computing power over the past few years now makes it possible to consider developing 3D full-core heterogeneous deterministic neutron transport solvers for reference calculations. Among all approaches presented in the literature, the method first introduced in [1] seems very promising. It consists in iterating over resolutions of 2D and ID MOC problems by taking advantage of prismatic geometries without introducing approximations of a low order operator such as diffusion. However, before developing a solver with all industrial options at EDF, several points needed to be clarified. In this work, we first prove the convergence of this iterative process, under some assumptions. We then present our high-performance, parallel implementation of this algorithm in the MICADO solver. Benchmarking the solver against the Takeda case shows that the 2D-1D coupling algorithm does not seem to affect the spatial convergence order of the MOC solver. As for performance issues, our study shows that even though the data distribution is suited to the 2D solver part, the efficiency of the ID part is sufficient to ensure a good parallel efficiency of the global algorithm. After this study, the main remaining difficulty implementation-wise is about the memory requirement of a vector used for initialization. An efficient acceleration operator will also need to be developed. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Performance and power modeling and prediction using MuMMI and 10 machine learning methods

Energy-efficient scientific applications require insight into how high performance computing system features impact the applications' power and performance. This insight can result from the development of performance and power models. Here, in this article, we use the modeling and prediction tool MuMMI (Multiple Metrics Modeling Infrastructure) and 10 machine learning methods to model and predict performance and power consumption and compare their prediction error rates. We use an algorithm-based fault-tolerant linear algebra code and a multilevel checkpointing fault-tolerant heat distribution code to conduct our modeling and prediction study on the Cray XC40 Theta and IBM BG/Q Mira at Argonne National Laboratory and the Intel Haswell cluster Shepard at Sandia National Laboratories. Our experimental results show that the prediction error rates in performance and power using MuMMI are less than 10% for most cases. By utilizing the models for runtime, node power, CPU power, and memory power, we identify the most significant performance counters for potential application optimizations, and we predict theoretical outcomes of the optimizations. Based on two collected datasets, we analyze and compare the prediction accuracy in performance and power consumption using MuMMI and 10 machine learning methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Learning likelihood ratios with neural network classifiers

The likelihood ratio is a crucial quantity for statistical inference in science that enables hypothesis testing, construction of confidence intervals, reweighting of distributions, and more. Many modern scientific applications, however, make use of data- or simulation-driven models for which computing the likelihood ratio can be very difficult or even impossible. By applying the so-called “likelihood ratio trick,” approximations of the likelihood ratio may be computed using clever parametrizations of neural network-based classifiers. A number of different neural network setups can be defined to satisfy this procedure, each with varying performance in approximating the likelihood ratio when using finite training data. We present a series of empirical studies detailing the performance of several common loss functionals and parametrizations of the classifier output in approximating the likelihood ratio of two univariate and multivariate Gaussian distributions as well as simulated high-energy particle physics datasets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to $α$-Ag 2 Se

First-principles molecular dynamics (FPMD) simulations are highly accurate, but due to their high calculation cost, the computational scale is often limited to hundreds of atoms and few picoseconds under specific temperature and pressure conditions. As such, we present here the guidelines for creating artificial neural network empirical interatomic potential (ANN potential) trained with such a limited FPMD data, which can perform long time scale MD simulations at least under the same conditions. The FPMD data for training are prepared on the basis of the convergence of radial distribution function [$g(r)$]. While training the ANN using total energy and atomic forces of the FPMD data, the error of pressure is also monitored and minimized. To create further robust potential, we add a small amount of FPMD data to reproduce the interaction between two atoms that are close to each other. ANN potentials for $α$-Ag 2 Se were created as an application example, and it has been confirmed that not only $g(r)$ and mean square displacements but also the specific heat requiring a long time scale simulation matched the FPMD and the experimental values. In addition, the MD simulation using the ANN potential achieved over 10 4 acceleration over the FPMD one. The guidelines proposed here mitigate the creation difficulty of the ANN potential, and a lot of FPMD data sleeping on the hard disk after the research may be put on the front stage again.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

CHARM-SYCL & IRIS: A Tool Chain for Performance Portability on Extremely Heterogeneous Systems

Performance portability is becoming crucial as high-performance computing systems become increasingly heterogeneous. We have many options for CPUs and accelerators (e.g., GPUs) but also for non-Von Neumann architectures such as field-programmable gate arrays. This paper presents the CHARM-SYCL unified programming environment for multiple accelerator types as a performance-portable programming environment. It uses the IRIS library developed at Oak Ridge National Laboratory as the back end accelerator runtime. IRIS has a high-performance scheduler to distribute tasks across accelerators. This design allows us to run an application from the same source on multiple systems with multiple configurations. We provide three types of portability with CHARM-SYCL: Portable Workflow, Compiler and Runtime Portability, and Application and Performance Portability. We implement a Monte Carlo simulation benchmark code on the CHARM-SYCL execution environment and demonstrate that our programming environment can accommodate extremely heterogeneous systems.

Fujita, Norihisa↗
DOE OSTI2018

System Administration Tools

High Performance Computing clusters and supercomputers are expensive and complex systems to manage. The software tools to manage these systems are also aging to the point where they are no longer actively developed and their methodology is antiquated. There has been a surge of new methodologies and software tools developed by the cloud providers to maximize availability of services and scalability of server deployment. The authors would like to utilize these new software tools to distribute our proof of concept, named Kraken, that will be responsible for booting and managing LANL's HPC systems in the future

Peltz, Paul↗
DOE OSTI2016

General Line Ampacity State Solver

INL’s General Line Ampacity State Solver (GLASS) is a high performance software platform that enables advanced grid system operations and planning efforts to be computed in real time. It enables the combination of advanced sensors and sensor networks, communication and data transfer channels, secure and reliable data management, and real-time processing through a flexible system architecture, with the ability to support and integrate with any utility enterprise system. GLASS is designed to help resolve the problems associated with thermally limited transmission and distribution networks, namely bare overhead power transmission lines. GLASS enables the methodology of Concurrent Cooling (Dynamic Line Rating) by reducing the need of using a cost-prohibited number of weather stations (reduce the number of weather stations per mile of overhead power line) to achieve the resolution necessary for individual electric transmission line span capacity calculations. GLASS achieves this by coupling real-time grid operations data and infrastructure architecture information with real-time field deployed weather station measurements and pre-computed Computational Fluid Dynamics (CFD)-based wind modeling interpolation parameters (look-up tables). GLASS uses mathematical models based on the IEEE 738 standard and builds off of the provided iterative solution using modern software paradigms and programming styles to provide a more robust and modular software system. GLASS also interfaces in real-time with utility companies using known transmission line spans and structures. GLASS provides real-time ampacity calculations based on recent weather station data as well as transmission line specifications. GLASS also allows the user to interact graphically with internal databases, which include information such as transmission line conductor types and thermal rating limits, weather station names and locations, specific transmission line span and structure locations, as well as recent ampacity calculations.

Gentle, Jake↗
DOE OSTI2025

Validation of RANS Models for Predicting Bypass Flow in Prismatic VHTRs

In prismatic Very-High Temperature Reactors (VHTRs), gaps occur between neighboring fuel assemblies referred to as bypass gaps. This work aims to measure the flow distribution between the bypass gaps between neighboring assemblies and the coolant channels within the assemblies. Computational fluid dynamics (CFD) was done using Reynolds-Averaged Navier Stokes (RANS) modeling in STAR-CCM+ to assess the ability of RANS models to accurately capture this phenomena. The RANS results were compared to experimental worked performed by Seoul National University (SNU) and Korea Atomic Energy Research Institute (KAERI) for validation. The CFD results showed good comparison with the experimental data available from SNU and flow split was characterized for multiple configurations, inlet mass flow rates, and bypass gap sizes.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Simulations of Bypass Flow in Prismatic VHTRs

In prismatic Very-High Temperature Reactors (VHTRs), gaps occur between neighboring fuel assemblies referred to as bypass gaps. This work aims to measure the flow distribution between the bypass gaps between neighboring assemblies and the coolant channels within the assemblies. Computational fluid dynamics (CFD) was done using Reynolds-Averaged Navier Stokes (RANS) modeling in STAR-CCM+ to assess the ability of RANS models to accurately capture this phenomena. The RANS results were compared to experimental worked performed by Seoul National University (SNU) and Korea Atomic Energy Research Institute (KAERI) for validation. The CFD results showed good comparison with the experimental data available from SNU and flow split was characterized for multiple configurations, inlet mass flow rates, and bypass gap sizes.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

SU-F-BRD-14: Dose Weighted Linear Energy Transfer Analysis of Critical Structures in Proton Therapy of Pediatric Brain Tumor Patients

Purpose: Dose weighted Linear Energy Transfer (LETd) analysis of critical structures may be useful in understanding the side effects of the proton therapy. The objective is to analyze the differences between LETd and dose distributions in brain tumor patients receiving double scattering proton therapy, to quantify LETd variation in critical organs, and to identify beam arrangements contributing to high LETd in critical organs. Methods: Monte Carlo simulations of 9 pediatric brain tumor patients were performed. The treatment plans were reconstructed with the TOPAS Monte Carlo code to calculate LETd and dose. The beam data were reconstructed proximal to the aperture of the double scattering nozzle. The dose and LETd to target and critical organs including brain stem, optic chiasm, lens, optic nerve, pituitary gland, and hypothalamus were computed for each beam. Results: Greater variability in LETd compared to dose was observed in the brainstem for patients with a variety of tumor types including 5 patients with tumors located in the posterior fossa. Approximately 20%–44% brainstem volume received LETd of 5kev/µm or greater from beams within gantry angles 180°±30° for 5 patients treated with a 3 beam arrangement. Critical organs received higher LETd when located in the vicinity of the beam distal edge. Conclusion: This study presents a novel strategy in the evaluation of the proton treatment impact on critical organs. While the dose to critical organs is confined below the required limits, the LETd may have significant variation. Critical organs in the vicinity of beam distal edge receive higher LETd and depended on beam arrangement, e.g. in posterior fossa tumor treatment, brainstem receive higher LETd from posterior-anterior beams. This study shows importance of the LETd analysis of the radiation impact on the critical organs in proton therapy and may be used to explain clinical imaging observations after therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Star–Galaxy Image Separation with Computationally Efficient Gaussian Process Classification

Abstract We introduce a novel method for discerning optical telescope images of stars from those of galaxies using Gaussian processes (GPs). Although applications of GPs often struggle in high-dimensional data modalities such as optical image classification, we show that a low-dimensional embedding of images into a metric space defined by the principal components of the data suffices to produce high-quality predictions from real large-scale survey data. We develop a novel method of GP classification hyperparameter training that scales approximately linearly in the number of image observations, which allows for application of GP models to large-size Hyper Suprime-Cam Subaru Strategic Program data. In our experiments, we evaluate the performance of a principal component analysis embedded GP predictive model against other machine-learning algorithms, including a convolutional neural network and an image photometric morphology discriminator. Our analysis shows that our methods compare favorably with current methods in optical image classification while producing posterior distributions from the GP regression that can be used to quantify object classification uncertainty. We further describe how classification uncertainty can be used to efficiently parse large-scale survey imaging data to produce high-confidence object catalogs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Real-Time Regional PV Spinning Reserve Estimator with AGC Look-Ahead Windows

Curtailed PV generation is a zero-marginal cost spinning reserve that can be used for a number of active power control services. However, unlike the traditional spinning reserve providers, i.e., fossil-fueled generators, who have well-defined operating characteristics, e.g., available headroom or potential high limit (PHL), PV plants have by nature variable and uncertain operating characteristics. To ensure the effective coordination between PV plants and the system operator during an active power control event, accurate forecasts of the PV PHL are essential. A novel reference-control grouping based scaling method has been proposed by NREL to estimate the PV PHL in real-time. This work further enhances the methodology by: 1) improving the model accuracy through machine learning; 2) considering look-ahead windows introduced by the computation and communication latencies; 3) applying the method to regional spinning reserve estimation. A significant performance improvement, over 99% of estimation error reduction, has been observed based on real-world data collected by CAISO and PV plant operators.

potential high limit↗
DOE OSTI2023

Real-Time Regional PV Spinning Reserve Estimator with AGC Look-Ahead Windows: Preprint

Curtailed PV generation is a zero-marginal cost spinning reserve that can be used for a number of active power control services. However, unlike the traditional spinning reserve providers, i.e., fossil-fueled generators, who have well-defined operating characteristics, e.g., available headroom or potential high limit (PHL), PV plants have by nature variable and uncertain operating characteristics. To ensure the effective coordination between PV plants and the system operator during an active power control event, accurate forecasts of the PV PHL are essential. A novel reference-control grouping based scaling method has been proposed by NREL to estimate the PV PHL in real-time. This work further enhances the methodology by: 1) improving the model accuracy through machine learning; 2) considering look-ahead windows introduced by the computation and communication latencies; 3) applying the method to regional spinning reserve estimation. A significant performance improvement, over 1.6% of estimation error reduction, has been observed based on real-world data collected by CAISO and PV plant operators.

potential high limit↗
DOE OSTI2025

Accelerating high-order continuum kinetic plasma simulations using multiple GPUs

Kinetic plasma simulations solve the Vlasov-Poisson or Vlasov-Maxwell equations to evolve scalar-variable distribution functions in position-velocity phase space and vector-variable electromagnetic fields in configuration space. The immense computational cost of evolving high-dimensional variables, and their large number of degrees of freedom, often limits the utility of continuum kinetic simulations and presents a challenge when it comes to accurately simulating real-world physical phenomena. To address this challenge, we present techniques that accelerate and minimize the computational work required for a scalable Vlasov-Poisson solver. We show theoretical hardware compute and communication bounds for solving a fourth-order finite-volume Vlasov-Poisson system. These bounds are then used to inform and evaluate the design of performance portable algorithms for a multiple graphics processing unit (GPU) accelerated version of the Vlasov-Poisson solver VCK-CPU [1]. We demonstrate that the multi-GPU Vlasov solver implementation, VCK-GPU, simultaneously minimizes required inter-process data transfer while also being bounded by the machine network performance limits. This results in an overall strong scaling speedup per timestep of up to 40x in three-dimensional phase space (one position, two velocity coordinates) and 54x in four dimensional phase space (two position, two velocity coordinates) and a 341x increase in simulation throughput of the GPU accelerated code over the existing CPU code. The GPU code is also able to weak scale up to 256 compute nodes and 1024 GPUs. In conclusion, we demonstrate that the improved compute performance enables exploring configurations which were previously computationally infeasible, including resolving fine-scale distribution function filamentation and multi-species dynamics with realistic electron-proton mass ratios.

Continuum kinetics↗
DOE OSTI2022

High-resolution Compton spectroscopy using x-ray microcalorimeters

X-ray Compton spectroscopy is one of the few direct probes of the electron momentum distribution of bulk materials in ambient and operando environments. Herein, we report high-resolution inelastic x-ray scattering experiments with high momentum and energy transfer performed at a storage-ring-based high-energy x-ray light source facility using an x-ray transition-edge sensor (TES) microcalorimeter detector. The performance was compared with a silicon drift detector (SDD), an energy-resolving semiconductor detector, and Compton profiles were measured for lithium and cobalt oxide powders relevant to lithium-ion battery research. Spectroscopic analysis of the measured Compton profiles demonstrates the high-sensitivity to the low- Z elements and oxidation states. The line shape analysis of the measured Compton profiles in comparison with computed Hartree–Fock profiles is usually limited by the resolution of the semiconductor detector. We have characterized an x-ray TES microcalorimeter detector for high-resolution Compton scattering experiments using a bending magnet source at the Advanced Photon Source with a double crystal monochromator, providing monochromatic photon energies near 27.5 keV. The momentum resolution below 0.16 atomic units (a.u.) was measured, yielding an improvement of more than a factor of 7 over a state-of-the-art SDD for the same scattering geometry. Furthermore, the lineshapes of narrow valence and broad core electron profiles of sealed lithium metal were clearly resolved using an x-ray TES compared to smeared and broadened lineshapes observed when using the SDD. High-resolution Compton scattering using the energy-resolving area detector shown here presents new opportunities for spatial imaging of electron momentum distributions for a wide class of materials with applications ranging from electrochemistry to condensed matter physics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Accelerated coupled Monte Carlo-Thermal hydraulic calculations using a hybrid GTF-diffusion-based prediction block: first results

Accurate predictions of spatial power and temperature distributions require the coupling of a neutron transport solver with a thermal-hydraulic (TH) feedback. Nowadays, Monte Carlo (MC) codes are widely coupled to TH solvers, typically via a Picard iteration (PI) method, due to the higher fidelity that such frameworks can produce. To speed up a PI, a prediction step can produce an improved initial guess for a source distribution and feed it to the MC code. Recent work investigated a prediction step that uses generalized transfer functions (GTFs) to predict the macroscopic cross sections' variations following a perturbation in TH properties, such as coolant density. The previous method also relied on first order perturbation (FOP) theory to predict perturbed power profiles, rather than using an expensive MC iterate. The implemented FOP method relied on generating a fission matrix from which the forward and adjoint Eigenmodes were extracted and later used to for power calculations. The generation of the fission matrix can introduce a significant computational overhead, therefore undermining the performance of the proposed hybrid technique when applied to high-dimensional problems, e.g., full core calculations. This work attempts to improve the GTF-FOP prediction step by replacing the FOP solver with a nodal diffusion solver, thus eliminating the need to calculate a fission matrix. The GTF-diffusion step was tested for various moderator density perturbations. In each case, the predicted power distribution showed good agreement with the reference case. The latter is attributed to the generally good prediction of most spatially distributed macroscopic cross sections, except the transport cross section, which will become the focus of future work. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Impact of Time Dependent Reactor and Sensor Physics on Core Power Synthesis

Online synthesis of the power distribution is critical in the operation and control of nuclear power reactors to ensure that the core is operating within safety margins, and to provide essential knowledge associated with the burnup of the fuel. In light water reactors (LWRs), power synthesis is achieved by using some a priori knowledge of the state of the reactor core and updating based on the signals coming from in-core sensors—namely, self-powered neutron detectors (SPNDs). This report aims to study the effects of fuel burnup and sensor degradation on the ability to accurately synthesize the power distribution in a LWR. Several modeling tools were used to simulate power synthesis based on the responses of SPNDs, with emitters made out of Rh or V. A representative pressurized water reactor low-enriched uranium (LEU) core was modeled using the Polaris/Purdue Advanced Reactor Core Simulator (PARCS) approach. The Monte Carlo N-Particle Transport 6 (MCNP6) code was used, as well, to calculate response functions between different segments of fuel to individual SPNDs; this is a crucial parameter for power synthesis. The Oak Ridge Isotope GENeration (ORIGEN) package in the Standardized Computer Analyses for Licensing Evaluation (SCALE) code was used to model the time-dependent isotopic transmutation in the SPND emitters. All these data were fed into a custom code that enacted the point-based iterative (PBI) method to simulate power synthesis. Developmental work was also performed on high-fidelity SPND models in the GEometry ANd Tracking 4 (Geant4) code, which enables higher-accuracy modeling of the current responses from SPNDs. In this work, five sets of time-dependent power synthesis test cases were conducted. In these test cases, systematic changes in the input conditions enabled an analysis of the effect of (1) slightly inaccurate a priori power distribution assumptions with respect to fuel burnup, (2) highly inaccurate a priori assumptions with respect to fuel burnup (such that burnup is not included in the a priori assumed distribution), and (3) differences between Rh and V SPNDs in terms of downstream consequences of the transmutation in the emitters. The authors discovered that one may permissibly have slightly inaccurate a priori assumptions of the fuel burnup (such that the level of burnup may be slightly under- or over-approximated by the accumulated burnup in approximately 9.3 full power days), but to not account for burnup at all in the a priori assumption leads to severe levels of error, approaching 25% at maximum. The authors also discovered that V SPNDs are extraordinarily robust in the low-enriched uranium fuel cycle considered in this modeling work, whereas Rh SPNDs undergo significant transmutation that can result in large errors in the synthesized power distribution.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗