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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 sources NASA NTRS DOE OSTI

DOE OSTI2023

Is Knowledge About Running Applications Helping Improve Runtime Prediction of HPC Jobs?

High-performance computing systems rely upon scheduling algorithms to achieve high utilization. These schedulers rely upon user estimates of job resource requirements, such as runtime, to determine optimal scheduling of incoming jobs. These user estimates, however, are prone to error. To mitigate this error, significant research has been directed at providing better estimates of job runtime, usually employing machine learning techniques. These techniques are dependent upon the input features selected. Among the possible features is the primary application used by the job. In a survey of more than 20 papers directed at improving runtime prediction, only four included primary application as an input feature. We focus this investigation specifically on the value of adding primary application as an input feature, and find that it does improve model performance, especially for jobs with longer runtimes, though this improvement varies based on the application used. We recommend further research to determine the cause of this variability as well as an optimal strategy for employing a mixture of models both including and not including primary application as a feature.

feature selection↗

NASA NTRS1999

Information Technology and the Autonomous Control of a Mars In-Situ Propellant Production System

With the rapidly increasing performance of information technology, i.e., computer hardware and software systems, as well as networks and communication systems, a new capability is being developed that holds the clear promise of greatly increased exploration capability, along with dramatically reduced design, development, and operating costs. These new intelligent systems technologies, utilizing knowledge-based software and very high performance computer systems, will provide new design and development tools, scheduling mechanisms, and vehicle and system health monitoring capabilities. In addition, specific technologies such as neural nets will provide a degree of machine intelligence and associated autonomy which has previously been unavailable to the mission and spacecraft designer and to the system operator. One of the most promising applications of these new information technologies is to the area of in situ resource utilization. Useful resources such as oxygen, compressed carbon dioxide, water, methane, and buffer gases can be extracted and/or generated from planetary atmospheres, such as the Martian atmosphere. These products, when used for propulsion and life-support needs can provide significant savings in the launch mass and costs for both robotic and crewed missions. In the longer term the utilization of indigenous resources is an enabling technology that is vital to sustaining long duration human presence on Mars. This paper will present the concepts that are currently under investigation and development for mining the Martian atmosphere, such as temperature-swing adsorption, zirconia electrolysis etc., to create propellants and life-support materials. This description will be followed by an analysis of the information technology and control needs for the reliable and autonomous operation of such processing plants in a fault tolerant manner, as well as the approach being taken for the development of the controlling software. Finally, there will be a brief discussion of the verification and validation process so crucial to the implementation of mission-critical software.

Gross, Anthony R.↗

DOE OSTI2021

RSE-Ops

If DevOps is the intersection of "Developer" and "Operations," then how does this concept map to high performance computing? The answer is RSE-ops, or the intersection of Research Software Engineering and Operations. Research Software Engineers (RSEs) are the individuals writing code for scientific software, and supporting researchers to use codes on high performance computing systems. RSE-ops, then, generally refers to best practices for development and operations of scientific software, which typically happens in a high performance computing environment. A comparison can be made to Cloud Native Devops, which is a similar term that has emerged around Cloud Native Computing. It refers to the application of DevOps practices to the cloud.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

TAMM: Tensor algebra for many-body methods

Tensor algebra operations such as contractions in computational chemistry consume a significant fraction of the computing time on large-scale computing platforms. The widespread use of tensor contractions between large multi-dimensional tensors in describing electronic structure theory has motivated the development of multiple tensor algebra frameworks targeting heterogeneous computing platforms. In this paper, we present Tensor Algebra for Many-body Methods (TAMM), a framework for productive and performance-portable development of scalable computational chemistry methods. TAMM decouples the specification of the computation from the execution of these operations on available high-performance computing systems. With this design choice, the scientific application developers (domain scientists) can focus on the algorithmic requirements using the tensor algebra interface provided by TAMM, whereas high-performance computing developers can direct their attention to various optimizations on the underlying constructs, such as efficient data distribution, optimized scheduling algorithms, and efficient use of intra-node resources (e.g., graphics processing units). The modular structure of TAMM allows it to support different hardware architectures and incorporate new algorithmic advances. We describe the TAMM framework and our approach to the sustainable development of scalable ground- and excited-state electronic structure methods. We present case studies highlighting the ease of use, including the performance and productivity gains compared to other frameworks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1998

Parallelization of NAS Benchmarks for Shared Memory Multiprocessors

This paper presents our experiences of parallelizing the sequential implementation of NAS benchmarks using compiler directives on SGI Origin2000 distributed shared memory (DSM) system. Porting existing applications to new high performance parallel and distributed computing platforms is a challenging task. Ideally, a user develops a sequential version of the application, leaving the task of porting to new generations of high performance computing systems to parallelization tools and compilers. Due to the simplicity of programming shared-memory multiprocessors, compiler developers have provided various facilities to allow the users to exploit parallelism. Native compilers on SGI Origin2000 support multiprocessing directives to allow users to exploit loop-level parallelism in their programs. Additionally, supporting tools can accomplish this process automatically and present the results of parallelization to the users. We experimented with these compiler directives and supporting tools by parallelizing sequential implementation of NAS benchmarks. Results reported in this paper indicate that with minimal effort, the performance gain is comparable with the hand-parallelized, carefully optimized, message-passing implementations of the same benchmarks.

Waheed, Abdul↗

DOE OSTI2024

Assessment of Cloud-based Applications for Enabling a Scalable Riskinformed Predictive Maintenance Strategy

The current light-water reactor fleet uses time-based maintenance strategies to achieve high-capacity factors. But to make nuclear more competitive in the energy market, these reactors could utilize emerging artificial intelligence (AI) and cloud computing technologies to achieve a cost-effective, predictive-maintenance strategy. This paper presents discussion and results on the application of cloud computing in the nuclear industry. The technical viability of cloud computing was analyzed using data from a boiling-water reactor’s safety relief valve. The models were hosted on three different systems: a local personal computer, Idaho National Laboratory’s high-performance computer system, and Microsoft Azure. The data were loaded and processed, and two types of models were trained in an A/B fashion. Based on the speed at which these actions were completed, it was determined that cloud computing affords adequate computing resources. Additionally, the computing power can scale with the demanded load. To enable cloud computing in the existing fleet, additional sensors, networks, and other requirements must be implemented to ensure a smooth transition from current maintenance strategies. However, the benefit is that the plants no longer need to manage their own servers, software, cybersecurity, and information technology support staff for in-house data analytics purpose. Many of these features can be offloaded to the cloud provider for a potential cost savings. Demonstrating how AI can improve the maintenance and operation of non-safety-related systems seems the likely path forward for implementing AI and cloud computing resources inside nuclear power plants.

azure↗

DOE OSTI2024

Performance Analysis of an Optimization Algorithm for Metamaterial Design on the Integrated High-Performance Computing and Quantum Systems

Optimizing metamaterials with complex geometries is a big challenge. Although an active learning algorithm, combining machine learning (ML), quantum computing, and optical simulation, has emerged as an efficient optimization tool, it still faces difficulties in optimizing complex structures that have potentially high performance. In this work, we comprehensively analyze the performance of an optimization algorithm for metamaterial design on the integrated HPC and quantum systems. We demonstrate significant time advantages through message-passing interface (MPI) parallelization on the high-performance computing (HPC) system showing approximately 54% faster ML tasks and 67 times faster optical simulation against serial workloads. Furthermore, we analyze the performance of a quantum algorithm designed for optimization, which runs with various quantum simulators on a local computer or HPC-quantum system. Results showcase ~24 times speedup when executing the optimization algorithm on the HPC-quantum hybrid system. This study paves a way to optimize complex metamaterials using the integrated HPC-quantum system.

Kim, Seongmin↗

DOE OSTI2021

Application of Machine Learning Techniques to an Agent-Based Model of Pantoea

Agent-based modeling (ABM) is a powerful simulation technique which describes a complex dynamic system based on its interacting constituent entities. While the flexibility of ABM enables broad application, the complexity of real-world models demands intensive computing resources and computational time; however, a metamodel may be constructed to gain insight at less computational expense. Here, we developed a model in NetLogo to describe the growth of a microbial population consisting of Pantoea . We applied 13 parameters that defined the model and actively changed seven of the parameters to modulate the evolution of the population curve in response to these changes. We efficiently performed more than 3,000 simulations using a Python wrapper, NL4Py . Upon evaluation of the correlation between the active parameters and outputs by random forest regression, we found that the parameters which define the depth of medium and glucose concentration affect the population curves significantly. Subsequently, we constructed a metamodel, a dense neural network, to predict the simulation outputs from the active parameters and found that it achieves high prediction accuracy, reaching an R 2 coefficient of determination value up to 0.92. Our approach of using a combination of ABM with random forest regression and neural network reduces the number of required ABM simulations. The simplified and refined metamodels may provide insights into the complex dynamic system before their transition to more sophisticated models that run on high-performance computing systems. The ultimate goal is to build a bridge between simulation and experiment, allowing model validation by comparing the simulated data to experimental data in microbiology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

A Scaling Study for Incompressible Multispecies Solver in Vertex-CFD

Multispecies incompressible flows occur widely in engineering and environmental applications, such as chemical reactors, fuel cells, ocean mixing, and biomedical systems. However, accurately resolving the complex transport and mixing phenomena associated with multiple interacting species remains computationally challenging, especially for large-scale problems. In this study, we present a robust, high-performance computing--enabled multispecies incompressible Navier–Stokes solver integrated within the Vertex-CFD framework. Our solver employs a fully coupled, implicit, finite element--based formulation that accurately captures the advection, diffusion, and interaction of multiple species in incompressible flows by leveraging the Kokkos library for parallel computing to achieve high computational efficiency. For pressure coupling, the entropically damped artificial compressibility method is utilized. We validated the solver against canonical test cases, including multispecies advection, diffusion, and Bateman systems; the results demonstrate second- and third-order spatial accuracy and consistent convergence. Additionally, we demonstrated the strong and weak scaling study results obtained on the leadership-class high-performance computing system, Frontier at Oak Ridge National Laboratory.

Oz, Furkan [ORNL] (ORCID:0000000265831724)↗

NASA NTRS1991

Enhancing aeropropulsion research with high-speed interactive computing

NASA-Lewis has committed to a long range goal of creating a numerical test cell for aeropropulsion research and development. Efforts are underway to develop a first generation Numerical Propulsion System Simulation (NPSS). The NPSS will provide a unique capability to numerically simulate advanced propulsion systems from nose to tail. Two essential ingredients to the NPSS are: (1) experimentally validated Computational Fluid Dynamics (CFD) codes; and (2) high performing computing systems (hardware and software) that will permit those codes to be used efficiently. To this end, NASA-Lewis is using high speed, interactive computing as a means for achieving Integrated CFD and Experiments (ICE). The development is described of a prototype ICE system for multistage compressor flow physics research.

Szuch, John R.↗

NASA NTRS1991

Enhancing aeropropulsion research with high-speed interactive computing

Szuch, John R.↗

DOE OSTI2024

Computing with a Chemical Reservoir

Contemporary computation is expensive, with large language models and artificial intelligence becoming more common in daily life. However, high-performance computing is reaching the limits in speed and energy expenditure, and domain science requires ever-increasing computational capacity, with simulations and data analysis pipelines ever-growing in complexity. As we progress towards post-exascale computation, with the associated high energy costs, new methods of energy-conscious computation are required. Novel analog and hybrid digital-analog systems can overcome these challenges, and chemical reactions offer a promising avenue. Computers based on chemistry can provide compact desktop devices with immense computational power. These devices are readily scalable by considering greater reaction systems or vessels, meeting the high-performance requirements for scientific workflows. In this article, we present ChemComp, a compilation pipeline for the conversion of ordinary differential equations into implementable chemical reactions. We then demonstrate the solving capabilities of ChemComp by emulating a potential chemical reservoir device. We leverage the multi-layer intermediate representation (MLIR) compiler framework to implement an expressive chemical reaction abstraction and propose a path for chemical reaction networks (CRNs) to represent mathematical problems effectively. Combined, we demonstrate a potential workflow that can harness chemistry’s computing power to create energy-efficient, high-performance computation systems for contemporary computing needs.

artificial intelligence↗

DOE OSTI2018

GPUs as boosters to analyze scalar and vector fields in quantum chemistry

We report that the analysis of scalar and vector fields in quantum chemistry is an essential task for the computational chemistry community, where such quantities must be evaluated rapidly to perform a particular study. For example, the atoms in molecules approach proposed by Bader has become popular; however, this method demands significant computational resources to compute the involved tasks in short times. In this article, we discuss the importance of graphics processing units (GPU) to analyze electron density, and related fields, implementing several scalar, and vector fields within the graphics processing units for atoms and molecules (GPUAM) code developed by a group of the Universidad Autónoma Metropolitana in México City. With this application, the quantum chemistry community can perform demanding computational tasks on a desktop, where CPUs and GPUs are used to their maximum capabilities. Lastly, the performance of GPUAM is tested in several systems and over different GPUs, where a GPU installed in a workstation converts it to a robust high-performance computing system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2001

Performance of the Wavelet Decomposition on Massively Parallel Architectures

Traditionally, Fourier Transforms have been utilized for performing signal analysis and representation. But although it is straightforward to reconstruct a signal from its Fourier transform, no local description of the signal is included in its Fourier representation. To alleviate this problem, Windowed Fourier transforms and then wavelet transforms have been introduced, and it has been proven that wavelets give a better localization than traditional Fourier transforms, as well as a better division of the time- or space-frequency plane than Windowed Fourier transforms. Because of these properties and after the development of several fast algorithms for computing the wavelet representation of any signal, in particular the Multi-Resolution Analysis (MRA) developed by Mallat, wavelet transforms have increasingly been applied to signal analysis problems, especially real-life problems, in which speed is critical. In this paper we present and compare efficient wavelet decomposition algorithms on different parallel architectures. We report and analyze experimental measurements, using NASA remotely sensed images. Results show that our algorithms achieve significant performance gains on current high performance parallel systems, and meet scientific applications and multimedia requirements. The extensive performance measurements collected over a number of high-performance computer systems have revealed important architectural characteristics of these systems, in relation to the processing demands of the wavelet decomposition of digital images.

El-Ghazawi, Tarek A.↗

DOE OSTI2018

Neural Networks and Graph Algorithms with Next-Generation Processors

The use of graphical processors for distributed computation revolutionized the field of high performance scientific computing. As the Moore's Law era of computing draws to a close, the development of non-Von Neumann systems: neuromorphic processing units, and quantum annealers; again are redefining new territory for computational methods. While these technologies are still in their nascent stages, we discuss their potential to advance computing in two domains: machine learning, and solving constraint satisfaction problems. Each of these processors utilize fundamentally different theoretical models of computation. This raises questions about how to best use them in the design and implementation of applications. While many processors are being developed with a specific domain target, the ubiquity of spin-glass models and neural networks provides an avenue for multi-functional applications. This provides hints at the future infrastructure needed to integrate many next-generation processing units into conventional high-performance computing systems.

Hamilton, Kathleen↗

NASA NTRSDate not supplied

The Influence of Computer Architecture on Performance and Scaling for Hypersonic Flow Simulations

It is critical to understand how hypersonic simulation tools perform on a range of computational platforms. This information will aid in the acquisition of appropriate hardware and the potential refactoring of hypersonic codes to run on different systems. In this paper, we consider two representative high-speed reacting flow cases: a model Mach 8 hypersonic waverider glide vehicle and a model hydrocarbon-fueled hypersonic ramjet propulsion system. In both scenarios, the flow fields are in chemical non-equilibrium and are modeled by the multi-species reacting Navier-Stokes equations. For these simulations we use several hypersonic simulation tools, including US3D, Kestrel, FUN3D, and the JENRER© flow solver. We explore several high performance computing systems containing IntelR© XeonR© Platinum processors, AMD EPYCTM7702 processors, and NVIDIAR© Tesla V100 devices. We compare performance and strong scaling between the different systems.

CPU↗

DOE OSTI2025

Openpronghorn

OpenPronghorn is a simulation tool specifically tailored for modeling thermal-hydraulic phenomena in advanced nuclear reactors. It is built on the Multiphysics Object-Oriented Simulation Environment (MOOSE), an open-source platform that facilitates the development of high-performance scientific computing applications. OpenPronghorn solves the Navier-Stokes equations, which describe the conservation of mass, momentum, and energy in fluid flows, using the finite volume numerical method. The code supports a wide range of fluid flow conditions that are applicable to nuclear reactors, including incompressible and weakly compressible flows, as well as single-phase and multiphase flows. It is capable of modeling diverse flow regimes, including laminar and turbulent flows, using various turbulence models such as the standard k-epsilon models, the v2f model, and the mixing length model. For multiphase flows, OpenPronghorn employs a mixture a Eulerian modeling approach with mixture, drift-flux, and full Eulerian models, and includes open-sourced interfacial transfer correlations for drag, exchange, and heat transfer coming from the scientific literature. OpenPronghorn's modular design allows it to handle multiscale simulations, ranging from detailed Reynolds-Averaged Navier Stokes (RANS) simulations to coarse-mesh and lumped parameter models. This flexibility enables users to perform high-fidelity simulations of specific reactor components as well as system-level analyses of entire reactor circuits. The code can be coupled with other MOOSE-based tools using the MultiApp system, allowing for the transfer of coupling quantities such as mass flow rates, heat fluxes, and boundary conditions between different simulation scales. One of the main features of OpenPronghorn is the it includes built-in validation cases from the open-source scientific literature and supports the implementation of user-defined models and correlations through MOOSE's FunctorMaterial system. OpenPronghorn is designed to be computationally efficient, leveraging the SIMPLE projection method for large-scale problems, and can be run on high-performance computing systems to handle the extensive computational demands of detailed reactor simulations. Overall, OpenPronghorn is a versatile and robust tool that provides critical insights into the thermal-hydraulic behavior of advanced nuclear reactors, supporting the design, safety, and optimization of next-generation nuclear energy systems.

Retamales, Mauricio Eduardo Tano [Idaho National L↗

DOE OSTI2018

Large-scale GW calculations on pre-exascale HPC systems

The ab initio GW approach is a rigorous Green’s-function-based framework that can be employed to compute electronic excitation properties of a wide variety of materials such as extended systems, molecules, as well as confined and nanostructured materials with a very satisfactory accuracy. However, GW calculations on complex systems are often hindered by the high computational cost associated with the method. Here, in this study, we demonstrate how to significantly speedup GW calculations with a novel algorithm for the computationally intense kernel based on a non-blocking chunked cyclic communication scheme that minimizes latency in MPI messages, allows for overlapping of communication and computation, and improves cache usage in matrix multiplication operations. The optimized version of the code, implemented in the BerkeleyGW software package, is capable of scaling well to the full Cori computer at NERSC (Cray XC40) and achieves over 11 Peta FLOP/s of sustained performance. We showcase our work by performing large-scale GW calculations of defect structures on silicon, which require simulation cells containing over 1700 atoms, and which can now be efficiently executed in just a few minutes on large pre-exascale high-performance computing systems.

97 MATHEMATICS AND COMPUTING↗