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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics

The Simulation EnviRonment for Geomorphology, Hydrodynamics, and Ecohydrology in Integrated form (SERGHEI) is a multi-dimensional, multi-domain, and multi-physics model framework for environmental and landscape simulation, designed with an outlook towards Earth system modelling. At the core of SERGHEI's innovation is its performance-portable high-performance parallel-computing (HPC) implementation, built from scratch on the Kokkos portability layer, allowing SERGHEI to be deployed, in a performance-portable fashion, in graphics processing unit (GPU)-based heterogeneous systems. In this work, we explore combinations of MPI and Kokkos using OpenMP and CUDA backends. In this contribution, we introduce the SERGHEI model framework and present with detail its first operational module for solving shallow-water equations (SERGHEI-SWE) and its HPC implementation. This module is designed to be applicable to hydrological and environmental problems including flooding and runoff generation, with an outlook towards Earth system modelling. Its applicability is demonstrated by testing several well-known benchmarks and large-scale problems, for which SERGHEI-SWE achieves excellent results for the different types of shallow-water problems. Finally, SERGHEI-SWE scalability and performance portability is demonstrated and evaluated on several TOP500 HPC systems, with very good scaling in the range of over 20 000 CPUs and up to 256 state-of-the art GPUs.

58 GEOSCIENCES↗
DOE OSTI2024

Pretraining Billion-Scale Geospatial Foundational Models on Frontier

As AI workloads increase in scope, generalization capability becomes challenging for small task-specific models and their demand for large amounts of labeled training samples increases. On the contrary, Foundation Models (FMs) are trained with internet-scale unlabeled data via self-supervised learning and have been shown to adapt to various tasks with minimal fine-tuning. Although large FMs have demonstrated significant impact in natural language processing and computer vision, efforts toward FMs for geospatial applications have been restricted to smaller size models, as pretraining larger models requires very large computing resources equipped with state-of-the-art hardware accelerators. Current satellite constellations collect 100+TBs of data a day, resulting in images that are billions of pixels and multimodal in nature. Such geospatial data poses unique challenges opening up new opportunities to develop FMs. We investigate billion scale FMs and HPC training profiles for geospatial applications by pretraining on publicly available data. We studied from end-to-end the performance and impact in the solution by scaling the model size. Our larger 3B parameter size model achieves up to 30% improvement in top1 scene classification accuracy when comparing a 100M parameter model. Moreover, we detail performance experiments on the Frontier supercomputer, America's first exascale system, where we study different model and data parallel approaches using PyTorch's Fully Sharded Data Parallel library. Specifically, we study variants of the Vision Transformer architecture (ViT), conducting performance analysis for ViT models with size up to 15B parameters. By discussing throughput and performance bottlenecks under different parallelism configurations, we offer insights on how to leverage such leadership-class HPC resources when developing large models for geospatial imagery applications.

Tsaris, Aristeidis (aris)↗
DOE OSTI2023

Proxy Applications for Converged Workloads: DMC LDRD Initiative

Modern scientific applications are complicated and require coordination of several components. Proxy application driven software-hardware co-design plays a vital role in driving innovation among the developments of applications, software infrastructure and hardware architecture. Proxy applications are self-contained and simplified codes that are intended to model the performance-critical computations within applications. Applications executing on modern High Performance Computing (HPC) systems are susceptible to network congestion, insufficient memory bandwidth within and across compute nodes, and inadvertent loss of performance due to bugs and unoptimized programming models. Modern numerical simulations and machine learning models play a critical role in studying physical phenomenon under myriad uncertainties. Such applications often exhibit irregular computation and memory accesses at specific regions of the application code, which can contribute to various performance bottlenecks at scale. To mitigate such issues and prepare the next generation hardware for a variety of computation and data movement contingencies, a well-known practice is to consider "proxy" applications as representative motifs for various classes of scientific applications. While there is disagreement in the HPC community on the mechanisms of construction of the proxy applications, there is a strong consensus on their positive impact in co-design. Proxy Applications for Converged Workloads (PACER) is about facilitating software-hardware co-design through proxy applications with the goal of improving the performance of converged science workflows on heterogeneous systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Data-Driven Approach to Nation-Scale Building Energy Modeling

In 2019, 125 million U.S. residential and commercial buildings consumed $412 billion in energy bills. These buildings currently consume 40% of the nation's primary energy, 73% of electricity, 80% of energy during peak electric grid use, and responsible for 39% of greenhouse gas emissions [14]. Urban-scale building energy modeling has grown significantly in the past decade, allowing individual campuses or communities of buildings to be modeled, simulated, and cost-effective solutions for intelligent management to be identified and implemented. While traditionally limited to individual counties and usually less than 2,000 buildings, the Automatic Building Energy Modeling (AutoBEM) soft-ware suite has been developed to process unconventional, nation-scale data sources to generate unique OpenStudio and EnergyPlus models of each building. Through the use of High Performance Computing (HPC) resources, every U.S. building has been simulated. This paper showcases the data layout, node partitioning, algorithmic approaches, and analytic results that were used to create, share, and analyze 124.4 million U.S. building models.

Berres, Andy↗
DOE OSTI2021

Analyzing Black Hole X-Rays with Simulations

We generate synthetic X-ray spectra of black holes from large-scale 3D GRMHD simulations performed on HPC machines. Here are a sampling of model spectra, characterized by the parameters indicated in the diagram below. The dashed red lines show power law fits to the parts of the spectrum observed by the X-ray telescope NuSTAR. How well these model spectra compare to real data helps us to characterize the physical realism of the underlying simulation techniques.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Job Management with mpi_jm

About this book: This book constitutes the refereed post-conference proceedings of 13 workshops held at the 33rd International ISC High Performance 2018 Conference, in Frankfurt, Germany, in June 2018: HPC I/O in the Data Center, HPC-IODC 2018; Workshop on Performance and Scalability of Storage Systems, WOPSSS 2018; 13th Workshop on Virtualization in High­-Performance Cloud Computing, VHPC 2018; Third International Workshop on In Situ Visualization, WOIV 2018; 4th International Workshop on Communication Architectures for HPC, Big Data, Deep Learning and Clouds at Extreme Scale, ExaComm 2018; International Workshop on OpenPOWER for HPC, IWOPH 2018; IXPUG Workshop: Many-Core Computing on Intel Processors; Workshop on Sustainable Ultrascale Computing Systems; Approximate and Transprecision Computing on Emerging Technologies, ATCET 2018; First Workshop on the Convergence of Large-Scale Simulation and Artificial Intelligence; Third Workshop for Open Source Supercomputing, OpenSuCo 2018; First Workshop on Interactive High-Performance Computing; Workshop on Performance Portable Programming Models for Accelerators, P^3MA 2018. The 53 full papers included in this volume were carefully reviewed and selected from 80 submissions. They cover all aspects of research, development, and application of large-scale, high performance experimental and commercial systems. Topics include HPC computer architecture and hardware; programming models, system software, and applications; solutions for heterogeneity, reliability, power efficiency of systems; virtualization and containerized environments; big data and cloud computing; and artificial intelligence.

Berkowitz, Evan↗
DOE OSTI2016

High Resolution Model Development to Quantify the Impact of Icebergs on the Stability of the Atlantic Meridional Overturning Circulation

In the present-day North Atlantic Ocean, relatively warm and salty water moves northwards from the tropics to the high latitudes, sinks, and returns southward towards the equator as North Atlantic Deep Water, forming the so called Atlantic Meridional Overturning Circulation (AMOC). It has been found that the stability of the AMOC is non-linearly related to the freshwater budget of the North Atlantic. In this way, additional fresh water can be added to the ocean with little impact, until a tipping point is reached that causes the AMOC to suddenly weaken and the Northern Hemisphere to abruptly cool. A great deal of uncertainty still remains over the sensitivity of the AMOC to changes in freshwater discharge as a result of the unrealistic manner in which freshwater has historically been added to climate models. Frequently, freshwater is discharged in ocean models entirely as liquid water, but in reality a large fraction of freshwater entering the ocean is ice calving from marine glaciers (half for Antarctica and two-thirds for Greenland). To more accurately quantify AMOC sensitivity to past and future changes in freshwater input, this project developed a comprehensive iceberg model to more realistically simulate the interaction between the cryosphere and the oceans at high-latitudes. The iceberg model created is written in Fortran90 and designed to scale efficiently on High Performance Computing (HPC) clusters so that tens-of-thousands of icebergs can be simulated at any time. Experiments performed with our model showed that in the Pleistocene there would have been enormous floods of freshwater released into the North Atlantic that would have transported icebergs and meltwater along the entire east coast of the United States, as far south as Florida Keys. In addition, high-resolution, modern-day, model simulations showed that if the Greenland Ice Sheet continues to melt at its current rate then there will be a 6-fold increase in the number of icebergs drifting in the North Atlantic in the next 30 years, and that the strength of the AMOC could weaken by 3.5%.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

On Parallelizing Single Dynamic Simulation Using HPC Techniques and APIs of Commercial Software

Time-domain simulations are heavily used in today’s planning and operation practices to assess power system transient stability and post-transient voltage/frequency profiles following severe contingencies to comply with industry standards. Because of the increased modeling complexity, it is several times slower than real time for state-of-the-art commercial packages to complete a dynamic simulation for a large-scale model. With the growing stochastic behavior introduced by emerging technologies, power industry has seen a growing need for performing security assessment in real time. This paper presents a parallel implementation framework to speed up a single dynamic simulation by leveraging the existing stability model library in commercial tools through their application programming interfaces (APIs). Several high performance computing (HPC) techniques are explored such as parallelizing the calculation of generator current injection, identifying fast linear solvers for network solution, and parallelizing data outputs when interacting with APIs in the commercial package, TSAT. The proposed method has been tested on a WECC planning base case with detailed synchronous generator models and exhibits outstanding scalable performance with sufficient accuracy.

Diao, Ruisheng↗
DOE OSTI2019

Broadband (0–5 Hz) Fully Deterministic 3D Ground‐Motion Simulations of a Magnitude 7.0 Hayward Fault Earthquake: Comparison with Empirical Ground‐Motion Models and 3D Path and Site Effects from Source Normalized Intensities

We report on high‐performance computing (HPC) fully deterministic simulation of ground motions for a moment magnitude (⁠Mw⁠) 7.0 scenario earthquake on the Hayward fault resolved to 5 Hz using the SW4 finite‐difference code. We computed motions obeying physics‐based 3D wave propagation at a regional scale with an Mw 7.0 kinematic rupture model generated following Graves and Pitarka (2016). Both plane‐layered (1D) and 3D Earth models were considered, with 3D subsurface material properties and topography interpolated from a model of the U.S. Geological Survey (USGS). The resulting ground‐motion intensities cover a broader frequency range than typically considered in regional‐scale simulations, including higher frequencies relevant for engineering analysis of structures. Median intensities for sites across the domain are within the reported between‐event uncertainties (⁠τ⁠) of ground‐motion models (GMMs) across spectral periods 0.2–10 s (frequencies 0.1–5 Hz). The within‐event standard deviation ϕ of ground‐motion intensity measurement residuals range 0.2–0.5 natural log units with values consistently larger for the 3D model. Source‐normalized ratios of intensities (3D/1D) reveal patterns of path and site effects that are correlated with known geologic structure. These results demonstrate that earthquake simulations with fully deterministic wave propagation in 3D Earth models on HPC platforms produce broadband ground motions with median and within‐event aleatory variability consistent with empirical models. Systematic intensity variations for the 3D model caused by path and site effects suggest that these epistemic effects can be estimated and removed to reduce variation in site‐specific hazard estimates. This study motivates future work to evaluate the validity of the USGS 3D model and investigate the development of path and site corrections by running more scenarios.

Rodgers, Arthur J.↗
DOE OSTI2022

RDPM: An Extensible Tool for Resilience Design Patterns Modelling

Resilience to faults, errors, and failures in extreme-scale high-performance computing (HPC) systems is a critical challenge. Resilience design patterns offer a new, structured hardware and software design approach for improving resilience. While prior work focused on developing performance, reliability, and availability models for resilience design patterns, this paper extends it by providing a Resilience Design Patterns Modeling (RDPM) tool which allows (1) exploring performance, reliability, and availability of each resilience design pattern, (2) offering customization of parameters to optimize performance, reliability, and availability, and (3) allowing investigation of trade-off models for combining multiple patterns for practical resilience solutions.

Kumar, Mohit↗
NASA NTRSDate not supplied

Vision 2030 Aircraft Propulsion Grand Challenge Problem: Full-engine CFD Simulations with High Geometric Fidelity and Physics Accuracy

2014 NASA published the outcome of the 2030 CFD (Computational Fluid Dynamics) Vision study: “CFD Vision 2030: A path to Revolutionary Computational Aerosciences” . The study provided a comprehensive review of the state of the art of CFD in 2014 for aerospace applications including, but not limited to, numerical algorithms, physics models, MDAO (Multidisciplinary Design Analysis and Optimization) and HPC (High Performance Computing) hardware. The study also proposed four conceptual ideas of Grand Challenge problems that would build on and benefit from advances outlined in the roadmap. The proposed challenges were meant to foster more detailed descriptions of grand challenge problems for specific disciplines. One of the proposed challenges was in the gas turbine propulsion area, focusing on transient full engine simulations. The current paper addresses detailed technical aspects of that challenge, and proposes a plan to approach it in a gradual manner, which includes high fidelity modeling of components, component coupling, and targeted experimental campaigns relying on common research models.

turbine engine↗
DOE OSTI2016

A performance model for the communication in fast multipole methods on high-performance computing platforms

Exascale systems are predicted to have approximately 1 billion cores, assuming gigahertz cores. Limitations on affordable network topologies for distributed memory systems of such massive scale bring new challenges to the currently dominant parallel programing model. Currently, there are many efforts to evaluate the hardware and software bottlenecks of exascale designs. It is therefore of interest to model application performance and to understand what changes need to be made to ensure extrapolated scalability. The fast multipole method (FMM) was originally developed for accelerating N-body problems in astrophysics and molecular dynamics but has recently been extended to a wider range of problems. Its high arithmetic intensity combined with its linear complexity and asynchronous communication patterns make it a promising algorithm for exascale systems. In this paper, we discuss the challenges for FMM on current parallel computers and future exascale architectures, with a focus on internode communication. We focus on the communication part only; the efficiency of the computational kernels are beyond the scope of the present study. We develop a performance model that considers the communication patterns of the FMM and observe a good match between our model and the actual communication time on four high-performance computing (HPC) systems, when latency, bandwidth, network topology, and multicore penalties are all taken into account. To our knowledge, this is the first formal characterization of internode communication in FMM that validates the model against actual measurements of communication time. The ultimate communication model is predictive in an absolute sense; however, on complex systems, this objective is often out of reach or of a difficulty out of proportion to its benefit when there exists a simpler model that is inexpensive and sufficient to guide coding decisions leading to improved scaling. The current model provides such guidance.

Computer Science↗
DOE OSTI2025

Integrated GW Farm ABM

This Data Repository includes data used for the integrated groundwater- farm ABM model, raw model output from scenario ensemble, and processed outputs that isolate the groundwater storage depletion outcomes for the 35,000 farm cells. Model Inputs: Farm ABM Inputs: This folder contains the input data used by the integrated groundwater - farm ABM modelling script (Python file) used for the high performance computing (HPC) experiments. The sub-folder "data inputs" contains all of the farm attribute data, while the three files in the folder have the hydrogeological data lookup table (NLDAS Cost Curve Attributes.csv), a lookup table (Theis well function table.csv) for the groundwater cost curve function, and the farm indexes and corresponding NLDAS ids for all of the cells run in this experiment (nldas farms subset final.csv). NLDAS Cost curve hydrogeological data: Hydrogeological data aggregated to 1/8 degree resolution and aligned with the NLDAS grid. Parameters include: water depth below ground surface [meters], subsurface porosity [unitless], aquifer depth from ground surface to aquifer bottom [meters], annual average recharge (USGS: mm, Doll: meters), and three different hydraulic conductivity (K) values (meters/day). The three K values represent the mean value from Gleeson et al. (2018), one standard deviation above the mean from Gleeson et al. (2018), and the de Graaf et al. 2020 modifications to certain lithologies. Additional information about these datasets and their processing are documented in the supplement to Yoon et al. 2025 (in review). Output: Raw outputs: This folder contains a .zip file that has model outputs for the entire scenario ensemble. There is one csv for each farm id, using the format "farm farmid cases.csv". The relationship between the farm id and NLDAS id is defined by the "nldas farms subset final.csv" located in the Farm ABM Inputs folder. Each csv has 625 rows, corresponding to 625 combinations of different scenario parameter values. Each row (scenario) represents the outcome of a 100 year simulation. Columns define scenario settings and summary statistics for each scenario. The first four columns define the scenario settings: "hydro ratio," "econ ratio," "K scenario," and "gamma scenario." The hydro and econ ratios are values passed to the modeling script that influence multipliers for other model parameters, as documented in the supplement to Yoon et al. 2025 (in review). The gamma multiplier is a coefficient multiplier applied to the baseline gamma values (values below 1 represent lower unobserved costs compared to baseline, values above 1 represent higher costs). The K scenario names represent K values of: "low": 0.5 m/d, "int 1": 2.5 m/d, "int 2": 10 m/d, "high": 50 m/d, and "gleeson": mean Gleeson K value. "Perc vol depleted" is the fraction of groundwater depleted at the end of the 100 simulation. Processed Output: Derived depletion outcomes from raw outputs: All of the individual csv files from the Raw outputs were aggregated into a single file that has the scenario settings and fraction depletion "Perc vol depleted" for every farm cell, for every scenario. The other two files define relationships between the farm id, NLDAS id, and local and major aquifer units, used for aquifer-level depletion analysis.

Agent based modeling↗
DOE OSTI2019

The Institute for Sustained Performance, Energy, and Resilience

The Green Queue framework is a framework designed to automate the development and deployment of customized architecture- and application-aware power savings recipes for large-scale HPC applications achieving up to 21% and 32% energy savings on HPC production applications run at scale. Additional work focused on the memory sub-system involved our methodology that uses application and machine characterization information to build predictive machine learning models that can accurately quantify phase-level sensitivity to the reduced per core memory bandwidth resulting from changes in the memory bus frequency to reduce the power. We evaluated the predictive capability of the model on real applications and validated them at a fine grain level by looking at 43 individual computational phases or application hotspots as well as the whole application. For more than 91% of the application hotspots, the prediction error is less than 10% (15) . Building from these validated performance and power models collaborations among SUPER team members developed an automated end-to-end system to reduce the complexity of developing and deploying machine learning models for performance, power, and energy. The new framework Automatic Multi-objective Modeling with Machine Learning (AutoMOMML) enabled multi-objective optimizations (power and performance) for HPC workloads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

CMS use of allocation based HPC resources

The higher energy and luminosity from the LHC in Run2 has put increased pressure on CMS computing resources. Extrapolating to even higher luminosities (and thus higher event complexities and trigger rates) in Run3 and beyond, it becomes clear the current model of CMS computing alone will not scale accordingly. High Performance Computing (HPC) facilities, widely used in scientific computing outside of HEP, present a (at least so far) largely untapped computing resource for CMS. Even just being able use a small fraction of HPC facilities computing resources could significantly increase the overall computing available to CMS. Here we describe the CMS strategy for integrating HPC resources into CMS computing, the unique challenges these facilities present, and how we plan on overcoming these challenges. We also present the current status of ongoing CMS efforts at HPC sites such as NERSC (Cori cluster), SDSC (Comet cluster) and TACC (Stampede cluster).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Profiling the BLAST bioinformatics application for load balancing on high-performance computing clusters

Abstract Background The Basic Local Alignment Search Tool (BLAST) is a suite of commonly used algorithms for identifying matches between biological sequences. The user supplies a database file and query file of sequences for BLAST to find identical sequences between the two. The typical millions of database and query sequences make BLAST computationally challenging but also well suited for parallelization on high-performance computing clusters. The efficacy of parallelization depends on the data partitioning, where the optimal data partitioning relies on an accurate performance model. In previous studies, a BLAST job was sped up by 27 times by partitioning the database and query among thousands of processor nodes. However, the optimality of the partitioning method was not studied. Unlike BLAST performance models proposed in the literature that usually have problem size and hardware configuration as the only variables, the execution time of a BLAST job is a function of database size, query size, and hardware capability. In this work, the nucleotide BLAST application BLASTN was profiled using three methods: shell-level profiling with the Unix “time” command, code-level profiling with the built-in “profiler” module, and system-level profiling with the Unix “gprof” program. The runtimes were measured for six node types, using six different database files and 15 query files, on a heterogeneous HPC cluster with 500+ nodes. The empirical measurement data were fitted with quadratic functions to develop performance models that were used to guide the data parallelization for BLASTN jobs. Results Profiling results showed that BLASTN contains more than 34,500 different functions, but a single function, RunMTBySplitDB, takes 99.12% of the total runtime. Among its 53 child functions, five core functions were identified to make up 92.12% of the overall BLASTN runtime. Based on the performance models, static load balancing algorithms can be applied to the BLASTN input data to minimize the runtime of the longest job on an HPC cluster. Four test cases being run on homogeneous and heterogeneous clusters were tested. Experiment results showed that the runtime can be reduced by 81% on a homogeneous cluster and by 20% on a heterogeneous cluster by re-distributing the workload. Discussion Optimal data partitioning can improve BLASTN’s overall runtime 5.4-fold in comparison with dividing the database and query into the same number of fragments. The proposed methodology can be used in the other applications in the BLAST+ suite or any other application as long as source code is available.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions

We discuss the challenges and propose research directions for using AI to revolutionize the development of high-performance computing (HPC) software. AI technologies, in particular large language models, have transformed every aspect of software development. For its part, HPC software is recognized as a highly specialized scientific field of its own. We discuss the challenges associated with leveraging state-of-the-art AI technologies to develop such a unique and niche class of software and outline our research directions in the two US Department of Energy–funded projects for advancing HPC Software via AI: Ellora and Durban.

Teranishi, Keita [ORNL] (ORCID:0000000166472690)↗
DOE OSTI2021

Summarizing the interoperabilities between xSDK members

Rapid, efficient production of high-quality, sustainable extreme-scale scientific applications is best accomplished using a rich ecosystem of state-of-the art reusable libraries, tools, lightweight frameworks, and defined software methodologies, developed by a community of scientists who are striving to identify, adapt, and adopt best practices in software engineering. The vision of the xSDK is to provide infrastructure for and interoperability of a collection of related and complementary software elements — developed by diverse, independent teams throughout the high-performance computing (HPC) community — that provide the building blocks, tools, models, processes, and related artifacts for rapid and efficient development of high-quality applications. A primary component, and challenge, of the xSDK is to improve interoperability among software libraries and domain components. This document summarizes the current and planned interoperabilities of the twenty-three xSDK member packages as of March 2021. Additionally, the status of the xSDK example codes that demonstrate and test the interoperabilities within the xSDK is provided.

97 MATHEMATICS AND COMPUTING↗