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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 487 records · Page 27

All sources NASA NTRS DOE OSTI

DOE OSTI2022

UPC++ v1.0 Specification, Revision 2023.3.0

UPC++ is a C++ library providing classes and functions that support Partitioned Global Address Space (PGAS) programming. The key communication facilities in UPC++ are one-sided Remote Memory Access (RMA) and Remote Procedure Call (RPC). All communication operations are syntactically explicit and default to non-blocking; asynchrony is managed through the use of futures, promises and continuation callbacks, enabling the programmer to construct a graph of operations to execute asynchronously as high-latency dependencies are satisfied. A global pointer abstraction provides system-wide addressability of shared memory, including host and accelerator memories. The parallelism model is primarily process-based, but the interface is thread-safe and designed to allow efficient and expressive use in multi-threaded applications. The interface is designed for extreme scalability throughout, and deliberately avoids design features that could inhibit scalability.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

UPC++ v1.0 Specification (Revision 2021.3.0)

UPC++ is a C++11 library providing classes and functions that support Partitioned Global Address Space (PGAS) programming. The key communication facilities in UPC++ are one-sided Remote Memory Access (RMA) and Remote Procedure Call (RPC). All communication operations are syntactically explicit and default to non-blocking; asynchrony is managed through the use of futures, promises and continuation callbacks, enabling the programmer to construct a graph of operations to execute asynchronously as high-latency dependencies are satisfied. A global pointer abstraction provides system-wide addressability of shared memory, including host and accelerator memories. The parallelism model is primarily process-based, but the interface is thread-safe and designed to allow efficient and expressive use in multi-threaded applications. The interface is designed for extreme scalability throughout, and deliberately avoids design features that could inhibit scalability.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

UPC++ v1.0 Specification, Revision 2022.9.0

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

UPC++ v1.0 Specification, Revision 2020.3.0

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Global Kinetic Modeling of the Intrabinary Shock in Spider Pulsars

Abstract Spider pulsars are compact binary systems composed of a millisecond pulsar and a low-mass companion. The relativistic magnetically dominated pulsar wind impacts onto the companion, ablating it and slowly consuming its atmosphere. The interaction forms an intrabinary shock, a proposed site of particle acceleration. We perform global fully kinetic particle-in-cell simulations of the intrabinary shock, assuming that the pulsar wind consists of plane-parallel stripes of alternating polarity and that the shock wraps around the companion. We find that particles are efficiently accelerated via shock-driven reconnection. We extract first-principles synchrotron spectra and light curves, which are in good agreement with X-ray observations: (1) the synchrotron spectrum is nearly flat, F ν ∝ const; (2) when the pulsar spin axis is nearly aligned with the orbital angular momentum, the light curve displays two peaks, just before and after the pulsar eclipse (pulsar superior conjunction), separated in phase by ∼0.8 rad; (3) the peak flux exceeds the one at the inferior conjunction by a factor of 10.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources

Applications which fuse machine learning and simulation are rarely best served by a single computing resource. Highly parallel simulation codes are best deployed on super- computers, while AI tasks used to decide which simulations to perform may be best suited to specialized accelerators. Here we present a Function-as-a-Service (FaaS) system for executing complex, distributed computational campaigns that achieves performance parity with conventional workflow systems without the complexities of secure network connections between compute providers. One innovation enabling high performance is a subsystem that directly moves task data between sites, separate from the cloud-hosted FaaS system used to distribute task instructions. We also introduce a flexible scheduling system that allows us access factor of 2 trade offs between the amount of resources required to solve a problem at each compute site. We anticipate that this system will upgrade multi-site applications from demonstration projects to routine practice in computational science.

Ward, Logan↗

DOE OSTI2022

The heat transfer coefficient associated with a moving packed bed of silica particles flowing through parallel plates

Concentrating Solar Power (CSP) with thermal energy storage has the potential to be a renewable energy technology with long duration, inexpensive energy storage. Higher temperature operation increases the conversion efficiency and reduces the cost of energy storage. Several emerging CSP designs utilize particles as the solar receiver due to their high temperature stability and low cost. In some designs, the particles are also used as the thermal energy storage (TES) media. In either configuration, an energy transfer is required between the hot particles and the working fluid in the power cycle in a Particle-to-Fluid Heat Exchanger (PtFHX). Understanding the heat transfer between a moving packed bed and a stationary surface is critical to the successful design of a PtFHX. In this paper, a test facility is described in which a moving packed bed of silica sand with particle size 100–600 μm is introduced into the channel formed by two parallel plates, one of which is heated. The effective static thermal conductivity of the particles used for the test are separately measured over the entire range of test temperatures. The inlet and outlet bulk temperatures of the particle flow are measured as are the surface temperatures at several axial locations along the centerline of the plate. The result is the measurement of heat transfer coefficient as a function of temperature for several velocities. The uncertainty of the measurements is presented and the results are compared to model results found in the literature.

14 SOLAR ENERGY↗

DOE OSTI2025

HARD: A performance portable radiation hydrodynamics code based on FleCSI framework

Hydrodynamics And Radiation Diffusion (HARD) is an open-source application for high-performance simulations of compressible hydrodynamics with radiation-diffusion coupling. Built on the FleCSI (Bergen et al., 2021 [1]) (Flexible Computational Science Infrastructure) framework, HARD expresses its computational units as tasks whose execution can be orchestrated by multiple back-end runtimes, including Legion (Bauer et al., 2012 [2]), MPI (Forum, 1994 [3]), and HPX (Kaiser et al., 2020 [4]). Node-level parallelism is handled through Kokkos (Edwards et al., 2014 [5]), providing a single-source, portable code base that runs efficiently on laptops, small homogeneous clusters, and the largest heterogeneous supercomputers currently available. To ensure scientific reliability, HARD includes a regression test suite that automatically reproduces canonical verification problems such as the Sod and LeBlanc shock tubes, and the Sedov blast wave, comparing numerical solutions against known analytical results. The project is distributed under an OSI-approved license, hosted on GitHub, and accompanied by reproducible build scripts and continuous integration workflows. This combination of performance portability, verification infrastructure, and community-focused development makes HARD a sustainable platform for advancing radiation hydrodynamics research across multiple domains.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Optimizing the hypre solver for manycore and GPU architectures

The solution of large-scale combustion problems with codes such as Uintah on modern computer architectures requires the use of multithreading and GPUs to achieve performance. Uintah uses a low-Mach number approximation that requires iteratively solving a large system of linear equations. The Hypre iterative solver has solved such systems in a scalable way for Uintah, but the use of OpenMP with Hypre leads to at least slowdown due to OpenMP overheads. The proposed solution uses the MPI Endpoints within Hypre, where each team of threads acts as a different MPI rank. This approach minimizes OpenMP synchronization overhead and performs as fast or (up to 1.44) faster than Hypre's MPI-only version, and allows the rest of Uintah to be optimized using OpenMP. The profiling of the GPU version of Hypre shows the bottleneck to be the launch overhead of thousands of micro-kernels. The GPU performance was improved by fusing these micro-kernels and was further optimized by using Cuda-aware MPI, resulting in an overall speedup of 1.16—1.44 compared to the baseline GPU implementation. The above optimization strategies were published in the International Conference on Computational Science 2020 [1]. This work extends the previously published research by carrying out the second phase of communication-centered optimizations in Hypre to improve its scalability on large-scale supercomputers. Additionally, this includes an efficient non-blocking inter-thread communication scheme, communication-reducing patch assignment, and expression of logical communication parallelism to a new version of the MPICH library that utilizes the underlying network parallelism [2]. The above optimizations avoid communication bottlenecks previously observed during strong scaling and improve performance by up to 2 on 256 nodes of Intel Knight's Landing processor.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Evanescent Mode Photoemission

Photoemission of electrons from solid surfaces into vacuum is routinely used in two configurations: reflection mode, in which light is incident on the emitting surface, and transmission mode, in which light illuminates the photoemissive material from behind. Here, using silicon nitride waveguides integrated beneath a high quantum efficiency cesium antimonide thin film, we demonstrate a new photoemission regime─the evanescent mode photoemission─in which light travels parallel to the surface in the waveguide while evanescently coupling into the thin film to excite and emit photoelectrons. Furthermore, our experiments show that this configuration enables direct visualization of guided optical modes over an unprecedented range (∼100 nm to 1 mm) and allows electron beam shaping at the source with transverse features well below ∼600 nm, establishing evanescent mode photoemission as a new platform for nanoscale optical field mapping and precise generation of electron beams.

Electron Sources↗

DOE OSTI2022

Multifunctional nanopore electrode array method for characterizing and manipulating single entities in attoliter-volume enclosures

Structurally regular nanopore arrays fabricated to contain independently controllable annular electrodes represent a new kind of architecture capable of electrochemically addressing small collections of matter—down to the single entity (molecule, particle, and biological cell) level. Furthermore, these nanopore electrode arrays (NEAs) can also be interrogated optically to achieve single entity spectroelectrochemistry. Larger entities such as nanoparticles and single bacterial cells are investigated by dark-field scattering and potential-controlled single-cell luminescence experiments, respectively, while NEA-confined molecules are probed by single molecule luminescence. By carrying out these experiments in arrays of identically constructed nanopores, massively parallel collections of single entities can be investigated simultaneously. The multilayer metal–insulator design of the NEAs enables highly efficient redox cycling experiments with large increases in analytical sensitivity for chemical sensing applications. NEAs may also be augmented with an additional orthogonally designed nanopore layer, such as a structured block copolymer, to achieve hierarchically organized multilayer structures with multiple stimulus-responsive transport control mechanisms. Finally, NEAs constructed with a transparent bottom layer permit optical access to the interior of the nanopore, which can result in the cutoff of far-field mode propagation, effectively trapping radiation in an ultrasmall volume inside the nanopore. Furthermore, the bottom metal layer may be used as both a working electrode and an optical cladding layer, thus, producing bifunctional electrochemical zero-mode waveguide architectures capable of carrying out spectroelectrochemical investigations down to the single molecule level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Structure of active methyl-CoM reductase, Earth’s main methane producer

Summary Our work reveals the structure of the active state of Methyl-Coenzyme M Reductase (MCR), the key and rate-limiting enzyme in biological methane formation. We find large differences between the active Ni(I) and inactive Ni(II) proteins and provide insight into how nature makes and breaks the C-H bond of methane. The Ni(II)-F430 center in inactive MCR contains four planar nitrogen ligands, a lower axial glutamine oxo, and an upper axial thiolate. The Ni(I)-enzyme replaces the axial ligands with a single water. The one-electron redox change results in movement of the Ni ion and upward swing of the β-lactam ring in the tetrapyrrole coupled to a domino-like protein quake through second sphere residues, inter-subunit interactions, a substrate tunnel, affecting even the dimensions of the unit cell. These structural changes lead Ni(I)-MCR to release a charge clamp that, in the Ni(II) state, locks down substrate Coenzyme B. Determining the Ni(I)-MCR structure required development of rigorous anaerobic crystallographic techniques. Validation of the MCR redox state was accomplished by in-line and parallel spectroscopic and unit cell analyses. This structure has large implications for developing technologies to limit methane emissions and efficiently produce biofuels. Methodology described here will enhance structural biology for other oxygen-sensitive enzymes.

09 BIOMASS FUELS↗

DOE OSTI2021

OpenMC

OpenMC is an open source, community-developed framework for performing Monte Carlo particle transport calculations.It is capable of simulating neutron and photon transport below ~50 MeV and also includes a model for bremsstrahlung production of photons by electrons/positrons. Fixed source, k-eigenvalue, and subcritical multiplication problems can be solved. Problem geometries can be modeled either using constructive solid geometry or a CAD representation. A flexible and efficient tally system enables a wide variety of physical quantities to be tallied and analyzed. OpenMC can run in parallel using a hybrid MPI and OpenMP programming model and has been extensively tested on leadership class supercomputers. A built-in Bateman equation solver enables the modeling of material composition changes due to irradiation. One of the unique features of OpenMC is its rich, extensible Python and C/C++ programming interfaces that enable programming pre- and post-processing, multigroup cross section generation, workflow automation, multiphysics coupling, and the visualization of geometry and tally results. In addition to the core Monte Carlo transport solver and associated APIs, OpenMC includes a Python-based nuclear data interface that enables power users to inspect, modify,and perform various types of nuclear data processing on ENDF, ACE, and OpenMC¿s native HDF5 files

ROMANO, PaulK.↗

DOE OSTI2021

ParChain: a framework for parallel hierarchical agglomerative clustering using nearest-neighbor chain

This paper studies the hierarchical clustering problem, where the goal is to produce a dendrogram that represents clusters at varying scales of a data set. We propose the ParChain framework for designing parallel hierarchical agglomerative clustering (HAC) algorithms, and using the framework we obtain novel parallel algorithms for the complete linkage, average linkage, and Ward's linkage criteria. Compared to most previous parallel HAC algorithms, which require quadratic memory, our new algorithms require only linear memory, and are scalable to large data sets. ParChain is based on our parallelization of the nearest-neighbor chain algorithm, and enables multiple clusters to be merged on every round. We introduce two key optimizations that are critical for efficiency: a range query optimization that reduces the number of distance computations required when finding nearest neighbors of clusters, and a caching optimization that stores a subset of previously computed distances, which are likely to be reused. Experimentally, we show that our highly-optimized implementations using 48 cores with two-way hyper-threading achieve 5.8--110.1x speedup over state-of-the-art parallel HAC algorithms and achieve 13.75--54.23x self-relative speedup. Compared to state-of-the-art algorithms, our algorithms require up to 237.3x less space. Our algorithms are able to scale to data set sizes with tens of millions of points, which existing algorithms are not able to handle.

Computer Science↗

DOE OSTI2022

DFT-FE 1.0: A massively parallel hybrid CPU-GPU density functional theory code using finite-element discretization

In this work, we present DFT-FE 1.0, building on DFT-FE 0.6 [Comput. Phys. Commun. 246, 106853 (2020)], to conduct fast and accurate large-scale density functional theory (DFT) calculations (reaching ~ 100,000 electrons) on both many-core CPU and hybrid CPU-GPU computing architectures. This work involves improvements in the real-space formulation—via an improved treatment of the electrostatic interactions that substantially enhances the computational efficiency—as well high-performance computing aspects, including the GPU acceleration of all the key compute kernels in DFT-FE. We demonstrate the accuracy by comparing the ground-state energies, ionic forces and cell stresses on a wide-range of benchmark systems against those obtained from widely used DFT codes. Further, we demonstrate the numerical efficiency of our implementation, which yields ~ 20× CPU-GPU speed-up by using GPU acceleration on hybrid CPU-GPU nodes. Notably, owing to the parallel-scaling of the GPU implementation, we obtain wall-times of 80–140 seconds for full ground-state calculations, with stringent accuracy, on benchmark systems containing ~ 6, 000 – 15,000 electrons.

pseudopotential↗

DOE OSTI2025

HPDR: High-Performance Portable Scientific Data Reduction Framework

The rapid growth in scientific data generation is outpacing advancements in computing systems necessary for efficient storage, transfer, and analysis, particularly in the context of exascale computing. With the deployment of first-generation exascale computing systems and next-generation experimental facilities, this gap is widening and necessitates effective data reduction techniques to manage enormous data volumes. Over the past decade, various data reduction methods, including lossless compression, error-controlled lossy compression, and data refactoring, have been developed to accelerate I/O in scientific workflows. Despite significant reductions in data volume, these methods introduce considerable computational overhead, which can become the new bottleneck in data processing. To mitigate this, GPU-accelerated data reduction algorithms have been introduced. However, challenges remain in their integration into exascale workflows, including limited portability across different GPU architectures, substantial memory transfer overhead, and reduced scalability on dense multi-GPU systems. To address these challenges, we propose HPDR, a high-performance and portable data reduction framework. HPDR is designed to enable the execution of state-of-the-art reduction algorithms across diverse processor architectures while reducing memory transfer overhead to 2.3 % of the original, resulting in up to 3.5× faster throughput compared to existing solutions. It also achieves up to 96% of the theoretical speedup in multi-GPU settings. In addition, evaluations on accelerating I/O operations at scale up to 1,024 nodes of the Frontier supercomputer demonstrate that HPDR can achieve up to 103 TB/s reduction throughput, providing up to 4× acceleration in parallel I/O performance compared to existing data reduction routines. This work highlights the potential of HPDR to significantly enhance data reduction efficiency in exascale computing environments.

Chen, Jieyang [University of Oregon]↗

DOE OSTI2022

Transience of the Retinal Output Is Determined by a Great Variety of Circuit Elements

Retinal ganglion cells (RGCs) encrypt stimulus features of the visual scene in action potentials and convey them toward higher visual centers in the brain. Although there are many visual features to encode, our recent understanding is that the ~46 different functional subtypes of RGCs in the retina share this task. In this scheme, each RGC subtype establishes a separate, parallel signaling route for a specific visual feature (e.g., contrast, the direction of motion, luminosity), through which information is conveyed. The efficiency of encoding depends on several factors, including signal strength, adaptational levels, and the actual efficacy of the underlying retinal microcircuits. Upon collecting inputs across their respective receptive field, RGCs perform further analysis (e.g., summation, subtraction, weighting) before they generate the final output spike train, which itself is characterized by multiple different features, such as the number of spikes, the inter-spike intervals, response delay, and the rundown time (transience) of the response. These specific kinetic features are essential for target postsynaptic neurons in the brain in order to effectively decode and interpret signals, thereby forming visual perception. We review recent knowledge regarding circuit elements of the mammalian retina that participate in shaping RGC response transience for optimal visual signaling.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Performance Analysis and Optimal Node-aware Communication for Enlarged Conjugate Gradient Methods

Krylov methods are a key way of solving large sparse linear systems of equations but suffer from poor strong scalability on distributed memory machines. Furthermore, this is due to high synchronization costs from large numbers of collective communication calls alongside a low computational workload. Enlarged Krylov methods address this issue by decreasing the total iterations to convergence, an artifact of splitting the initial residual and resulting in operations on block vectors. In this article, we present a performance study of an enlarged Krylov method, Enlarged Conjugate Gradients (ECG), noting the impact of block vectors on parallel performance at scale. Most notably, we observe the increased overhead of point-to-point communication as a result of denser messages in the sparse matrix-block vector multiplication kernel. Additionally, we present models to analyze expected performance of ECG, as well as motivate design decisions. Most importantly, we introduce a new point-to-point communication approach based on node-aware communication techniques that increases efficiency of the method at scale.

97 MATHEMATICS AND COMPUTING↗