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At least 1,423 records · Page 79

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Evaluating Performance Portability with the CMS Heterogeneous Pixel Reconstruction code

In the past years the landscape of tools for expressing parallel algorithms in a portable way across various compute accelerators has continued to evolve significantly. There are many technologies on the market that provide portability between CPU, GPUs from several vendors, and in some cases even FPGAs. These technologies include C++ libraries such as Alpaka and Kokkos, compiler directives such as OpenMP, the SYCL open specification that can be implemented as a library or in a compiler, and standard C++ where the compiler is solely responsible for the offloading. Given this developing landscape, users have to choose the technology that best fits their applications and constraints. For example, in the CMS experiment the experience so far in heterogeneous reconstruction algorithms suggests that the full application contains a large number of relatively short computational kernels and memory transfer operations. In this work we use a stand-alone version of the CMS heterogeneous pixel reconstruction code as a realistic use case of HEP reconstruction software that is capable of leveraging GPUs effectively. We summarize the experience of porting this code base from CUDA to Alpaka, Kokkos, SYCL, std::par, and OpenMP offloading. We compare the event processing throughput achieved by each version on NVIDIA and AMD GPUs as well as on a CPU, and compare those to what a native version of the code achieves on each platform.

Andriotis, Nikolaos↗

DOE OSTI2025

Large-scale experimental validation of thermochemical water-splitting oxides discovered by defect graph neural networks

Thermochemical water-splitting (TCH) based on 2-step thermal redox cycles in metal oxides is a promising approach to generating H 2 , but state-of-the-art (SOTA) CeO 2 has several practical limitations, which has motivated continued materials discovery efforts in this field. Here, in this study, we improve upon a SOTA defect graph neural network (dGNN) surrogate model's oxygen vacancy predictions and combine them with materials project phase diagrams to down-select and discover structurally diverse, experimentally known metal oxides whose TCH performance was previously unknown. Amongst twelve candidates selected based on our high-throughput screening and down-selection criteria, we achieved ∼80% accuracy in identifying materials with stable redox cycling and hydrogen production in stagnation flow reactor water-splitting experiments. Closer to 100% accuracy can be achieved if higher-accuracy, hybrid DFT-predicted vacancy formation energies were computed and used in lieu of the most uncertain dGNN-based screening predictions, as they correct false positives to true negatives. Notably, two discovered candidates, Sr 3 PrMn 2 O 8 and Ba 2 Fe 2 O 5 , display hydrogen yields greater than CeO 2 under specific redox conditions. In conclusion, these results demonstrate our ability to computationally predict and experimentally validate promising candidate TCH materials that have the potential to compete with CeO 2 .

08 HYDROGEN↗

DOE OSTI2022

SRF Levitation and Trapping of Nanoparticles

A proposal has been conceived to levitate and trap mesoscopic particles using radio frequency (RF) fields in a superconducting RF(SRF) cavity. Exploiting the intrinsic characteristics of an SRF cavity, this proposal aims at overcoming a major limit faced by state-of-the-art laser trapping techniques. The goal of the proposal is to establish a foundation to enable observation of quantum phenomena of an isolated mechanical oscillator interacting with microwave fields. An experiment supported by LDRD funding at JLab has started to address R&D issues relevant to these new research directions using existing SRF facilities at JLab. The success of this experiment would establish its groundbreaking relevance to quantum information science and technology, which may lead to applications in precision force measurement sensors, quantum memories, and alternative quantum computing implementations with promises for superior coherence characteristics and scalability well beyond the start-of-the-art. In this contribution, we will introduce the proposal and basic consideration of the experiment.

Geng, R. L.↗

DOE OSTI2022

SRF Levitation and Trapping of Nanoparticles

Geng, Rong-Li↗

DOE OSTI2025

ESnet-JLab FPGA Accelerated Transport (control plane) [EJFAT (udplbd2)] v2.0

The ESnet-JLab FPGA Accelerated Transport system is a solution for streaming high-speed scientific measurement data from Data Acquisition Systems (DAQs) to high-performance computing facilties. It is generally compatible with many science workflows, and makes no assumptions about the specifics of any particular experiment. This program (udplbd version 2) implements the control plane for the system. It is responsible for programming network forwarding rules into the data plane (implemented by the hardware designed named udplb, described separately). It also implements the control loop necessary to match up offered workload with available capacity on high-performance compute nodes.

Howard, Derek [Lawrence Berkeley National Laborato↗

DOE OSTI2022

Flexocaloric effect in superelastic materials

We present a combined theoretical–experimental study of flexocaloric effects in superelastic materials exhibiting structural transitions. We study a Ginzburg–Landau model combined with a vibrational model for a beam near a ferroelastic transition loaded with a three-point bending setup. We also perform experiments on a Cu–Al–Ni single crystal undergoing a martensitic transition. We measure bent beam profiles, vertical force vs vertical deflection during a slow isothermal process, time evolution of the bending and unbending amplitudes, and the evolution of temperature profiles. We also compute the evolution of heat source and heat sink profiles. Finally, we study the location of acoustic emission events during the bending/unbending experiment. Our observations are consistent with the model predictions and allow us to identify the main physical parameters relevant for flexocaloric applications.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Algebraic Algorithms for Betweenness and Percolation Centrality

Abstract In this paper, we explored different ways to write the algebraic version of betweenness centrality algorithm. Particularly, we focused on Brandes' algorithm. We aimed for algebraic betweenness centrality that can be parallelized easily. We proposed 3-tuple geodetic semiring as an extension to the usual geodetic semiring with 2-tuples. Using the 3-tuple geodetic semiring, Dijkstra's and Brandes' algorithm, we wrote more concise and general algebraic betweenness centrality (ABC) algorithm which is valid for weighted and directed graphs. We also proposed an alternative version of ABC using the usual geodetic semiring with 2-tuple where we used a simple way to construct shortest path tree after computing shortest path distances in the usual geodetic semiring. This allows us to avoid computational complexity of ABC implementation using 3-tuple geodetic semiring. We used numba to optimize and parallelize ABC. We evaluated the performance of ABC using 2-tuple geodetic semiring as compared to NetworkX, a common python package for graph algorithms. We did scalability experiments on parallel ABC and showed its total speedup. We also showed that with small modification, ABC can be adapted to algebraicly compute other centrality measures such as percolation centrality.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Integrated Experimental and Modeling Study of Geochemical Reactions of Simple Fracturing Fluids with Caney Shale

Interactions between rock minerals and hydraulic fluids directly impact the geochemical and geomechanical properties of shale formations. However, the mechanisms of geochemical reactions in shale unconventional reservoirs remain poorly understood. Here, to investigate the geochemical reactions between shale and hydraulic fracturing fluids, a series of batch reactor experiments were undertaken. Three rock samples with different mineralogical compositions and three fluid samples of different compositions (deionized water, deionized water + 2% potassium chloride (KCl), and deionized water + 0.5% choline chloride (C 5 H 14 ClNO) were used. Experiments were undertaken at reservoir temperature and atmospheric pressure. Elemental compositions of effluents after 1, 3, 7, 14, 28 days were analyzed using Inductively Couple Plasma Mass Spectrometry (ICP-MS). Medical Computed Tomography (CT) scan and X-ray Fluorescence (XRF) were conducted on the entire core run to help upscale results obtained from rock-fluid interaction experiments. Geochemical modeling using a reactive simulator, TOUGHREACT, was undertaken to corroborate experimental results. Results show that lower pH triggered high dissolution rates in the rock samples, especially the carbonate components. As pH increased, the rate of dissolution declined significantly, though for most cases dissolution still continued. Observed dissolved silica concentrations were much higher than the quartz solubility, suggesting that much of the silica originates from more soluble silica polymorphs and possibly desorption from clay mineral exchange sites. Concentration of most elemental species in solution increased but aluminium and magnesium concentrations declined rapidly following initial entry into solution. Geochemical modeling corroborated the conclusions regarding mineral dissolution and precipitation observed from experiments, notably; the dissolution of calcite and oxidation of pyrite in reacted shale samples, the likely presence of silica polymorphs such as opal, chalcedony or amorphous silica in these samples, and the reduction of Al and Mg concentrations in solution by precipitation of secondary aluminosilicate phases. The de-flocculation of clay minerals during reaction implies fines migration after hydraulic fracturing. This is detrimental to reservoir productivity as clay fines are displaces and lodged within the micro and nano-fractures created during fracturing. The immediate consumption of aluminium and magnesium also has implications on blockage of hydrocarbon pathways due to precipitation of new minerals in these locations.

04 OIL SHALES AND TAR SANDS↗

DOE OSTI2023

Using temperature and flow fields to detect gas leakage from canisters containing spent nuclear fuel: Applications to RAMM-TM

The multi-physics STAR-CCM+ code has been used to simulate the temperature and flow fields in canister gas leakage experiments conducted by using a 1/4.5-scale model storage cask. The simulations were conducted at Argonne National Laboratory’s Laboratory Computing Resource Center, utilizing high-performance computing resources. Development of STAR-CCM+ simulation models for the 1/4.5-scale model cask is described herein, followed by validation of the simulation results against experimental data. Canister depressurization and thermal response during gas leakage are discussed, along with analyses of the leakage path and allowable leakage rate. The insights gained from the STAR-CCM+ simulations and leakage analyses will help guide future experiments and actual industry applications of Argonne’s Remote Area Modular Monitoring system for canister surface temperature measurement (RAMM-TM) to enable effective aging management of spent fuel during extended dry storage, as well as help reduce risks to public safety and health and protect the environment.

Canister gas leakage↗

DOE OSTI2024

METHOD FOR EXPOSING IRRADIATED FUEL CLADDING TO RAPID THERMAL TRANSIENTS

Reactor core materials exposed to rapid thermal transients during accidents can experience significant changes in mechanical properties as irradiation hardness is recovered. Understanding material performance during transients using computer modeling requires accurate descriptions of mechanical property evolution with time and temperature. A simple experimental capability was developed to expose irradiated Zircaloy to a simulated thermal transient using immersion in molten tin (at 675°C) to rapidly heat and hold before rapidly cooling with a water quench. Multiple heating-cooling cycles using a nonirradiated tensile specimen showed simplicity and viability. Based on preliminary test results, a robust system was established for routine evaluation of simulated thermal accident scenarios in the Low Activation Materials Design and Analysis (LAMDA) laboratory at Oak Ridge National Laboratory (ORNL). This paper summarizes system development and nonirradiated Zircaloy commissioning test results. Future experiments will conduct tensile tests on irradiated Zircaloy after various rapid temperature profiles.

Byun, TS↗

DOE OSTI2021

Computational general relativistic force-free electrodynamics: II. Characterization of numerical diffusivity

Scientific codes are an indispensable link between theory and experiment; in (astro-)plasma physics, such numerical tools are one window into the universe’s most extreme flows of energy. The discretization of Maxwell’s equations – needed to make highly magnetized (astro)physical plasma amenable to its numerical modeling – introduces numerical diffusion. It acts as a source of dissipation independent of the system’s physical constituents. Understanding the numerical diffusion of scientific codes is the key to classifying their reliability. It gives specific limits in which the results of numerical experiments are physical. We aim at quantifying and characterizing the numerical diffusion properties of our recently developed numerical tool for the simulation of general relativistic force-free electrodynamics by calibrating and comparing it with other strategies found in the literature. Our code correctly models smooth waves of highly magnetized plasma. We evaluate the limits of general relativistic force-free electrodynamics in the context of current sheets and tearing mode instabilities. We identify that the current parallel to the magnetic field (j ∥ ), in combination with the breakdown of general relativistic force-free electrodynamics across current sheets, impairs the physical modeling of resistive instabilities. We find that at least eight numerical cells per characteristic size of interest (e.g., the wavelength in plasma waves or the transverse width of a current sheet) are needed to find consistency between resistivity of numerical and of physical origins. High-order discretization of the force-free current allows us to provide almost ideal orders of convergence for (smooth) plasma wave dynamics. The physical modeling of resistive layers requires suitable current prescriptions or a sub-grid modeling for the evolution of j ∥ .

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Phonon Mean Free Path Spectroscopy: Theory and Experiments

Chapter 6 reviews the current status of resolving the phonon mean free path distribution in real materials from both the computational and experimental perspectives, which is essential for engineering materials at the nanoscale to achieve desirable thermal transport properties.

Hua, Chengyun↗

DOE OSTI2022

Machine learning on FPGA for event selection

Real-time data processing is a frontier field in experimental particle physics. The application of FPGAs at the trigger level is used by many current and planned experiments (CMS, LHCb, Belle2, PANDA). Usually they use conventional processing algorithms. LHCb has implemented Machine Learning (ML) elements for real-time data processing with a triggered readout system that runs most of the ML algorithms on a computer farm. The work described in this article aims to test the ML-FPGA algorithms for streaming data acquisition. Herein, there are many experiments working in this area and they have a lot in common, but there are many specific solutions for detector and accelerator parameters that are worth exploring further. This report describes the purpose of the work and progress in evaluating the ML-FPGA application.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Adaptive Computing and Multi-Fidelity Strategies for Control, Design and Scale-Up of Renewable Energy Applications

We describe our ongoing research in adaptive computing and multi-fidelity modeling strategies. Our goal is to use a combination of low- and high-fidelity simulation models to enable computationally efficient optimization and uncertainty quantification. We develop optimization formulations that take into account the compute resources currently available, which act as a constraint with regards to the fidelity level simulation we can run while maximizing information gain. These strategies are being implemented into a software framework with a generalized API allowing its application to a broad range of applications, from power grid stability and buildings control to material synthesis and biofuels processing. We will discuss a few examples from these applications that can benefit from this approach, especially when considering challenges arising in scaling up experiments and simulations.

adaptive computing↗

DOE OSTI2025

MoRE-Brain: Routed Mixture of Experts for Interpretable and Generalizable Cross-Subject fMRI Visual Decoding

Decoding visual experiences from fMRI offers a powerful avenue to understand human perception and develop advanced brain-computer interfaces. However, current progress often prioritizes maximizing reconstruction fidelity while overlooking interpretability, an essential aspect for deriving neuroscientific insight. To address this gap, we propose MoRE-Brain, a neuro-inspired framework designed for high-fidelity, adaptable, and interpretable visual reconstruction. MoRE-Brain uniquely employs a hierarchical Mixture-of-Experts architecture where distinct experts process fMRI signals from functionally related voxel groups, mimicking specialized brain networks. The experts are first trained to encode fMRI into the frozen CLIP space. A finetuned diffusion model then synthesizes images, guided by expert outputs through a novel dual-stage routing mechanism that dynamically weighs expert contributions across the diffusion process. MoRE-Brain offers three main advancements: First, it introduces a novel Mixture-of-Experts architecture grounded in brain network principles for neuro-decoding. Second, it achieves efficient cross-subject generalization by sharing core expert networks while adapting only subject-specific routers. Third, it provides enhanced mechanistic insight, as the explicit routing reveals precisely how different modeled brain regions shape the semantic and spatial attributes of the reconstructed image. Extensive experiments validate MoRE-Brain’s high reconstruction fidelity, with bottleneck analyses further demonstrating its effective utilization of fMRI signals, distinguishing genuine neural decoding from over-reliance on generative priors. Consequently, MoRE-Brain marks a substantial advance towards more generalizable and interpretable fMRI-based visual decoding.

Wei, Yuxiang [Georgia Institute of Technology]↗

DOE OSTI2025

Comparison of a Full-Scale and a 1:10 Scale Low-Speed Two-Stroke Marine Engine Using Computational Fluid Dynamics

International marine shipping is a growing component of international trade; a vast majority of all the world’s goods are being transported on large ocean-going vessels. The International Maritime Organization (IMO) introduced the Energy Efficiency Design Index in 2013, a regulatory framework of associated metrics for reducing emissions of CO 2 per tonne-mile from shipping by approximately 10% each decade. Therefore, decarbonizing the maritime sector requires the development of new fuel sources. Because of the extremely large physical size of the internal combustion engines present in shipping vessels, experimental iterative development of the engine and fuel system is cost-prohibitive. Thus, the ability to perform combustion system development in a scaled platform that can be more easily operated and modeled computationally is of interest. To that end, scaling relationships are needed to translate the results from a smaller engine to a larger counterpart. Scaling studies to date have been restricted to low scaling ratios, four-stroke light-duty engines, and under-resolved computational fluid dynamic simulations that likely do not accurately capture the physics of scaling. In this work, computational models of a 1:10 scale and a full-scale two-stroke crosshead low-speed marine engine were created and validated against experiments obtained in a real 1:10 scale engine installed at Oak Ridge National Laboratory. Further, due to the large size of the full-scale engine, the model required large high-performance computing resources to be evaluated. The availability of high-performance computing resources at the Department of Energy’s Leadership Computing Facilities is an enabler of the current work. The results of the small- and large-scale engine simulations were compared to analyze the effectiveness of the appropriate scaling laws under these extreme scaling ratio conditions.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Complexity-theoretic foundations of BosonSampling with a linear number of modes

BosonSampling is the leading candidate for demonstrating quantum computational advantage in photonic systems. While we have recently seen many impressive experimental demonstrations, there is still a formidable distance between the complexity-theoretic hardness arguments and current experiments. One of the largest gaps involves the ratio of {particles} to modes -- all current hardness evidence assumes a dilute regime in which the number of linear optical modes scales at least quadratically in the number of particles. By contrast, current experiments operate in a saturated regime with a linear number of modes. In this paper we bridge this gap, bringing the hardness evidence for experiments in the saturated regime to the same level as had been previously established for the dilute regime. This involves proving a new worst-to-average-case reduction for computing the Permanent which is robust to both large numbers of row repetitions and also to distributions over matrices with correlated entries. We also apply similar arguments to give evidence for hardness of Gaussian BosonSampling in the saturated regime.

Computational Complexity (cs.CC)↗

DOE OSTI2020

Scalable Knowledge Graph Analytics at 136 Petaflop/s

We are motivated by newly proposed methods for data mining large-scale corpora of scholarly publications, such as the full biomedical literature, which may consist of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover how concepts relate to one another. They construct graph representations from annotated text databases and then formulate the relationship-mining problem as one of computing all-pairs shortest paths (APSP), which becomes a significant bottleneck. In this context, we present a new high-performance algorithm and implementation of the Floyd-Warshall algorithm for distributed-memory parallel computers accelerated by GPUs, which we call DSNAPSHOT (Distributed Accelerated Semiring All-Pairs Shortest Path). For our largest experiments, we ran DSNAPSHOT on a connected input graph with millions of vertices using 4, 096nodes (24,576GPUs) of the Oak Ridge National Laboratory's Summit supercomputer system. We find DSNAPSHOT achieves a sustained performance of 136×1015 floating-point operations per second (136petaflop/s) at a parallel efficiency of 90% under weak scaling and, in absolute speed, 70% of the best possible performance given our computation (in the single-precision tropical semiring or “min-plus” algebra). Looking forward, we believe this novel capability will enable the mining of scholarly knowledge corpora when embedded and integrated into artificial intelligence-driven natural language processing workflows at scale.

Kannan, Ramakrishnan {ramki}↗