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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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At least 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

An Optimizing Compiler for Petascale I/O on Leadership Class Architectures

In high-performance computing systems, parallel I/O architectures usually have very complex hierarchies with multiple layers that collectively constitute an I/O stack, including high-level I/O libraries such as PnetCDF and HDF5, I/O middleware such as MPI-IO, and parallel file systems such as PVFS and Lustre. Our project explored automated instrumentation and compiler support for I/O intensive applications. Our project made significant progress towards understanding the complex I/O hierarchies of high-performance storage systems (including storage caches, HDDs, and SSDs), and designing and implementing state-of-the-art compiler/runtime system technology that targets I/O intensive HPC applications that target leadership class machine. This final report summarizes the major achievements of the project and also points out promising future directions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Parameter Determination of the Non-Local Granular Fluidity Model for Wood Chips by Comparison to Well-Defined Experimental Flow Systems

Unlike simple liquids and gases, the bulk flow and transport of granular materials remain poorly understood by physicists and pose many problems for engineers in designing and operating bulk-solids handling equipment. Discrete element methods (DEM) are currently considered the state-of-the-art for simulating the flow of granular materials, but DEM is limited in system size to about a few million particles due to high computational costs, even when run on current high-performance computing architectures. Continuum models are needed to simulate the flows of bulk solids in industrial-scale vessels and equipment, e.g., grain silos and coal-ash disposal piles. The focus of this work is the so-called nonlocal granular fluidity (NLGF) model that has been previously shown to reproduce observed nonlocal behaviors that arise when granular phenomena occur on length scales that are near the scale of the system geometry, e.g., the jamming of hopper outlets and the stop-height of piles on inclines [PNAS, 110(17), 6730-6735]. We have implemented the NLGF constitutive model in OpenFOAM CFD software, performed simulations of well-defined flow systems, and compared the results to experimental data. Experimental systems include a ring-shear tester, pile formation, flow on an incline, and discharge of a hopper. The tested material was loblolly pine wood chips that were milled and sieved to a size range of roughly 0.05-0.25 inches in diameter. The NLGF model parameters were tuned to achieve reasonable agreement between simulation and experimental results. Further, we evaluate the mapping between measurable material properties (bulk friction angle, particle-particle friction, compressibility, grain diameter, etc.) with NLGF model parameters, some of which have direct analogs (friction parameters and grain diameter) while others are constructs of the model (nonlocal amplitude and timescale of fluidity evolution).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Ten questions on urban building energy modeling

Buildings in cities consume up to 70% of all primary energy. To achieve cities’ energy and climate goals, it is necessary to reduce energy use and associated greenhouse gas emissions in buildings through energy conservation and efficiency improvements. Computational tools empowered with rich urban datasets can model performance of buildings at the urban scale to provide quantitative insights for stakeholders and inform their decision making on urban energy planning, as well as building energy retrofits at scale, to achieve efficiency, sustainability, and resilience of urban buildings. Designing and operating urban buildings as a group (from a city block to a district to an entire city) rather than as single individuals requires simulation and optimization to account for interactions among buildings and between buildings and their surrounding urban environment, and for district energy systems serving multiple buildings with diverse thermal loads across space and time. When hundreds or more buildings are involved in typical urban building energy modeling (UBEM) to estimate annual energy demand, evaluate design or retrofit options, and quantify impacts of extreme weather events or climate change, it is crucial to integrate urban datasets and UBEM tools in a seamless automatic workflow with cloud or high-performance computing for users including urban planners, designers and researchers. This paper presents ten questions that highlight significant UBEM research and applications. The proposed answers in this work aim to stimulate discussion and provide insights into the current and future research on UBEM, and more importantly, to inspire new and important questions from young researchers in the field.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

ChatMPI: LLM-Driven MPI Code Generation for HPC Workloads

The Message Passing Interface (MPI) standard plays a crucial role in enabling scientific applications for parallel computing and is an essential component in high-performance computing (HPC). However, implementing MPI code manually—especially applying a proper domain decomposition and communication pattern—is a challenging and error-prone task. We present ChatMPI, an AI assistant for MPI parallelization of sequential C codes. In our analysis, we focus on testing six essential HPC workloads, which are based on Basic Linear Algebra Subprograms levels 1, 2, and 3 as well as sparse, stencil, and iterative operations. We analyze the process of creating ChatMPI by using the ChatHPC library. This lightweight large language model (LLM)–based infrastructure enables HPC experts to efficiently create and supervise trustworthy AI capabilities for critical HPC software tasks. We study the data required for training (fine-tuning) ChatMPI to generate parallel codes that not only use MPI syntax correctly but also apply HPC techniques to reduce memory communication and maximize performance by using proper work decomposition. With a relatively small training dataset composed of a few dozen prompts and fewer than 15 minutes of fine-tuning on one node equipped with two NVIDIA H100 GPUs, ChatMPI elevates trustworthiness for MPI code generation of current LLMs (e.g., Code Llama, ChatGPT-4o and ChatGPT 5). Additionally, we evaluate the performance of the MPI codes generated by ChatMPI in comparison with the ones generated by ChatGPT-4o and ChatGPT-5. The codes generated by ChatMPI provide up to a 4 × boost in performance by using better problem decomposition, communication patterns, and HPC techniques (e.g., communication avoiding).

Valero Lara, Pedro [ORNL] (ORCID:0000000214794310)↗
DOE OSTI2024

SOMA: Observability, monitoring, and in situ analytics for exascale applications

With the rise of exascale systems and large, data-centric workflows, the need to observe and analyze high performance computing (HPC) applications during their execution is becoming increasingly important. HPC applications are typically not designed with online monitoring in mind, therefore, the observability challenge lies in being able to access and analyze interesting events with low overhead while seamlessly integrating such capabilities into existing and new applications. We explore how our service-based observation, monitoring, and analytics (SOMA) approach to collecting and aggregating both application-specific diagnostic data and performance data addresses these needs. Furthermore, we present our SOMA framework and demonstrate its viability with LULESH, a hydrodynamics proxy application. Then we focus on Astaroth, a multi-GPU library for stencil computations, highlighting the integration of the TAU and APEX performance tools and SOMA for application and performance data monitoring.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

Some Problems and Solutions in Transferring Ecosystem Simulation Codes to Supercomputers

Many computer codes for the simulation of ecological systems have been developed in the last twenty-five years. This development took place initially on main-frame computers, then mini-computers, and more recently, on micro-computers and workstations. Recent recognition of ecosystem science as a High Performance Computing and Communications Program Grand Challenge area emphasizes supercomputers (both parallel and distributed systems) as the next set of tools for ecological simulation. Transferring ecosystem simulation codes to such systems is not a matter of simply compiling and executing existing code on the supercomputer since there are significant differences in the system architectures of sequential, scalar computers and parallel and/or vector supercomputers. To more appropriately match the application to the architecture (necessary to achieve reasonable performance), the parallelism (if it exists) of the original application must be exploited. We discuss our work in transferring a general grassland simulation model (developed on a VAX in the FORTRAN computer programming language) to a Cray Y-MP. We show the Cray shared-memory vector-architecture, and discuss our rationale for selecting the Cray. We describe porting the model to the Cray and executing and verifying a baseline version, and we discuss the changes we made to exploit the parallelism in the application and to improve code execution. As a result, the Cray executed the model 30 times faster than the VAX 11/785 and 10 times faster than a Sun 4 workstation. We achieved an additional speed-up of approximately 30 percent over the original Cray run by using the compiler's vectorizing capabilities and the machine's ability to put subroutines and functions "in-line" in the code. With the modifications, the code still runs at only about 5% of the Cray's peak speed because it makes ineffective use of the vector processing capabilities of the Cray. We conclude with a discussion and future plans.

Skiles, J. W.↗
DOE OSTI2026

Energy-efficient scientific computing using chemical reservoirs

The rapid growth of computing demands driven by scientific computing, data analytics, and artificial intelligence (AI) advancements has exposed the limitations of traditional digital processing systems. These systems are nearing physical energy barriers, making significant gains in energy efficiency increasingly unattainable. As we advance toward post-exascale computing, disruptive approaches are critical to overcoming these limitations. Among emerging analog solutions, biochemical computing offers a transformative path for achieving orders-of-magnitude improvements in energy efficiency. By leveraging the natural optimization capabilities of chemical reaction networks (CRNs), biochemical systems have the potential to meet high-performance computing needs through natural scalability. However, numerous challenges remain, including theoretical limitations in mapping computational problems to CRNs and practical barriers in implementing biochemical computing devices. In this paper, we present a framework for chemical computation using biochemical systems and introduce key components of our approach for energy-efficient scientific computing. We showcase the feasibility of this framework by solving a system of ordinary differential equations by emulating a chemical reservoir device, demonstrating its potential for addressing modern computing challenges. This work lays a foundational step toward harnessing the computational power of chemistry to design energy-efficient, scalable, high-performance next-generation computing systems.

Johnson, Connah G. M. [Pacific Northwest National ↗
DOE OSTI2022

ExaTN: Scalable GPU-Accelerated High-Performance Processing of General Tensor Networks at Exascale

We present ExaTN (Exascale Tensor Networks), a scalable GPU-accelerated C++ library which can express and process tensor networks on shared- as well as distributed-memory high-performance computing platforms, including those equipped with GPU accelerators. Specifically, ExaTN provides the ability to build, transform, and numerically evaluate tensor networks with arbitrary graph structures and complexity. It also provides algorithmic primitives for the optimization of tensor factors inside a given tensor network in order to find an extremum of a chosen tensor network functional, which is one of the key numerical procedures in quantum many-body theory and quantum-inspired machine learning. Numerical primitives exposed by ExaTN provide the foundation for composing rather complex tensor network algorithms. We enumerate multiple application domains which can benefit from the capabilities of our library, including condensed matter physics, quantum chemistry, quantum circuit simulations, as well as quantum and classical machine learning, for some of which we provide preliminary demonstrations and performance benchmarks just to emphasize a broad utility of our library.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Extreme-scale workflows: A perspective from the JLESC international community

The Joint Laboratory for Extreme-Scale Computing (JLESC) focuses on software challenges in high-performance computing systems to meet the needs of today’s science campaigns, which often require large resources, consist of multiple tasks, and generate vast amounts of data. In this context, extreme-scale workflows have been the key factor in enabling scientific discoveries by helping scientists automate the dependencies and data exchanges between workflow tasks, instead of managing those manually. Here, in this paper, we present representative extreme-scale workflows and feature workflow systems developed by JLESC participating institutions. We present lessons learned while developing these tools, alongside with the open challenges and future research directions in the field of extreme-scale workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Density scaling approximation for Monte-Carlo simulations of radioactive plumes

The release of radioactive gas into the atmosphere can diffuse into large volumes of air downwind from the point of release. The extent of radioactivity can cover thousands of cubic meters of air. For such large volumes, the weather models used to predict the down-wind distribution of the plume and the radiation transport models used to predict the radiation reaching ground-level from the plume can take tens of hours of computer time on multi-node institutional High-Performance Computing facilities. In this paper we focus on the radiation transport aspect of plume modeling. Here, we describe a phenomenological method for approximating the amounts of radiation that reach ground level from large volumes of a static radioactive plume that can be calculated on a stand-alone personal computer in much shorter computation times than those usually needed for such large volume evaluations. We refer to this method as the Density Scaling Approximation (DSA). Its ability to approximate ground-level count rates of large plumes comes from using a small-plume volume with a scaled-up value of air density to simulate the same number of scatterings that occur during transport in larger plume volumes at normal air density. We demonstrate the DSA by using a 100 m-diameter air-filled hemispherical dome geometry with a uniform volumetric activity of 135 Xe gas throughout the air-filled volume. The DSA for a larger dome diameter is obtained by evaluating the 100 m dome with an air density scaled up by the linear ratio of the larger diameter to the 100 m diameter. We find that this approximation works well for dome diameters up to 1200 m – the largest diameter studied and a size more than sufficient for accounting for all the radiation from 135 Xe. Moreover, most of our DSA results can be calculated over 500 times faster than corresponding full-sized geometry with normal air density. To help evaluate the accuracy of the DSA and gain insight into how well it can reproduce different regions of the spectra, we use three, easily understood regions of interest to compare the DSA results to the full-sized geometry at normal air density results. These regions are the full-energy peak, the region of single-Compton scattering, and the region of multiple-Compton scattering. We show how the dominance of the Compton scattering mechanism determines this division and thus provides insight into how Compton scattering is manifested in spectra from photon scattering through air in general, and how well the DSA approximation works.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Experiments and Analyses of Data Transfers Over Wide-Area Dedicated Connections

Dedicated wide-area network connections are increasingly employed in high-performance computing and big data scenarios. One might expect the performance and dynamics of data transfers over such connections to be easy to analyze due to the lack of competing traffic. However, non-linear transport dynamics and end-system complexities (e.g., multi-core hosts and distributed filesystems) can in fact make analysis surprisingly challenging. We present extensive measurements of memory-to-memory and disk-to-disk file transfers over 10 Gbps physical and emulated connections with 0–366 ms round trip times (RTTs). For memory-to-memory transfers, profiles of both TCP and UDT throughput as a function of RTT show concave and convex regions; large buffer sizes and more parallel flows lead to wider concave regions, which are highly desirable. TCP and UDT both also display complex throughput dynamics, as indicated by their Poincare maps and Lyapunov exponents. For disk-to-disk transfers, we determine that high throughput can be achieved via a combination of parallel I/O threads, parallel network threads, and direct I/O mode. Our measurements also show that Lustre filesystems can be mounted over long-haul connections using LNet routers, although challenges remain in jointly optimizing file I/O and transport method parameters to achieve peak throughput.

Rao, Nageswara S.↗
DOE OSTI2023

TOWARDS RAPID RESPONSE UPDATES OF POPULATIONS AT RISK

Understanding population at risks has been a focus of the LandScan program through its development of population estimates. With advancements in computer vision, deep learning technologies and access to High Performance Computing (HPC) and high resolution imagery, population estimates are now modeled at the building level. However, when those patterns are disrupted, rapid updates to population distribution estimates are needed to support humanitarian aid and response. Oak Ridge National Laboratory (ORNL) recently adapted an existing deep learning building footprint extraction model in development of a scalable approach to Building Damage Assessments (BDA). This new opportunity opens the possibility of automating BDA to support rapid population distribution estimate updates for geographic areas involved in geopolitical conflicts or natural events for humanitarian aid and response or where to focus recovery efforts. In addition, incorporate social surveys to further model human behavior under conflict or other scenarios that disrupt normal patterns of life.

Urban, Marie↗
DOE OSTI2020

RLScheduler: An Automated HPC Batch Job Scheduler Using Reinforcement Learning

Today’s high-performance computing (HPC) platforms are still dominated by batch jobs. Accordingly, effective batch job scheduling is crucial to obtain high system efficiency. Existing HPC batch job schedulers typically leverage heuristic priority functions to prioritize and schedule jobs. But, once configured and deployed by the experts, such priority functions can hardly adapt to the changes of job loads, optimization goals, or system settings, potentially leading to degraded system efficiency when changes occur. To address this fundamental issue, we present RLScheduler, an automated HPC batch job scheduler built on reinforcement learning. RLScheduler relies on minimal manual interventions or expert knowledge, but can learn high-quality scheduling policies via its own continuous ‘trial and error’. We introduce a new kernel-based neural network structure and trajectory filtering mechanism in RLScheduler to improve and stabilize the learning process. Through extensive evaluations, we confirm that RLScheduler can learn high-quality scheduling policies towards various workloads and various optimization goals with relatively low computation cost. Moreover, we show that the learned models perform stably even when applied to unseen workloads, making them practical for production use.

Zhang, Di↗
DOE OSTI2020

Phase I Final Technical Report on Energy-Efficient Reconfigurable Universal Accelerator Interconnect

It is well known that application specific computing systems, optimally designed and configured for a given workload, offer much higher energy-efficiency and throughput than general purpose systems. In modern computing systems, heterogeneous computing systems have emerged that exploit the energy and performance benefits of combining various different domain-specific processor architectures. Application domains such as high-performance computing and machine learning now process terabyte-sized data sets, requiring enormous processing and memory resources. These applications have very high-power consumption due to bottlenecks in the electrical interconnection between processing units. This project aims to reduce both communication energy and latency by integrating universally available accelerators with silicon photonics. It also aims to increase system throughput by exploiting emerging technologies in silicon photonic reconfigurable interconnects; this will allow the system to balance itself in real time to accommodate changes in workloads and data flows.

2.5D/3D integration↗
DOE OSTI2025

AI-Driven Accelerated Inclusion Analysis for Energy Efficient Steelmaking (Final CRADA Report)

This was a collaborative effort between Lawrence Livermore National Security, LLC (LLNS) as manager and operator of Lawrence Livermore National Laboratory (LLNL) and ArcelorMittal USA Research LLC (“ArcelorMittal” as the Participant), to use scanning electron microscopy (SEM) images, computer vision and machine learning methods, and high-performance computing to accelerate the inclusion analysis process of liquid steel so that new methods can be used for near-real time process control on the shop floor.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Properties of Electronic Materials

This final technical report summarizes the research conducted under DOE Grant DE-SC0002623, "Properties of Electronic Materials," led by Principal Investigator Shengbai Zhang at Rensselaer Polytechnic Institute. Over the 16-year period, the project employed first-principles computational methods to investigate the structural, electronic, and dynamic properties of a wide range of electronic materials, with applications in energy technologies, optoelectronics, and data storage. Key areas included topological insulators, phase-change materials, graphene and two-dimensional systems, perovskites for photovoltaics, defect engineering in semiconductors, kagome lattices, and ultrafast carrier dynamics. The research resulted in 115 peer-reviewed publications, advancing fundamental understanding of material behaviors at the atomic scale and contributing to innovations in renewable energy, memory devices, and quantum materials. Findings have implications for improving energy efficiency, developing lead-free solar cells, and enabling high-speed data processing. The work has trained numerous graduate students and postdocs, fostering the next generation of computational materials scientists. The original goals were to develop theoretical models and computational tools to predict and optimize electronic properties of materials for energy applications. All objectives were accomplished, with no major departures from planned methodologies. Challenges in computational scaling were addressed through access to high-performance computing resources.

36 MATERIALS SCIENCE↗
NASA NTRS1988

NASKERN Program Tests Computers

Computer program, NASKERN developed to aid in evaluation of performances of supercomputers. Consists of seven test-kernel programs performing computations typical of supercomputer calculations. Written in FORTRAN 77.

Bailey, David H.↗
NASA NTRS1997

Global Magnetohydrodynamic Modeling of the Solar Corona

Under this contract, we have continued our investigations of the large scale structure of the solar corona and inner heliosphere using global magnetohydrodynamic (MHD) simulations. These computations have also formed the basis for studies of coronal mass ejections (CMES) using realistic coronal configurations. We have developed a technique for computing realistic magnetohydrodynamic (MHD) computations of the solar corona and inner heliosphere. To perform computations that can be compared with specific observations, it is necessary to incorporate solar observations into the boundary conditions. We have used the Wilcox Solar Observatory synoptic maps (collected during a solar rotation by daily measurements of the line-of-sight magnetic field at central meridian) to specify the radial magnetic field (B,) at the photosphere. For the initial condition, we use a potential magnetic field consistent with the specified distribution of B, at the lower boundary, and a wind solution consistent with the specified plasma density and temperature at the solar surface. Together this initial condition forms a (non-equilibrium) approximation of the state of the solar corona for the time-dependent MHD computation. The MHD equations are then integrated in time to steady state. Here we describe solutions relevant to a recent solar eclipse, as well as Ulysses observations. We have also developed a model configuration of solar minimum, useful for studying CME initiation and propagation.

Linker, Jon A.↗