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At least 685 records · Page 38

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DOE OSTI2025

BULKI-Store v0.3.2

BULKI-Store is a distributed object storage system optimized for high-performance computing environments. Built with a Rust core and Python bindings, it efficiently manages scientific and machine learning datasets across HPC clusters. The system employs a client-server architecture with MPI integration, enabling seamless scaling on supercomputers like Perlmutter. BULKI-Store's object-oriented approach provides intuitive data organization with rich metadata support, contrasting with traditional file-based solutions. Key optimizations include selective checkpoint loading, unified checkpoint files, and object chunking for large data transfers. For machine learning workloads, BULKI-Store offers advantages through fine-grained access patterns, dynamic data sharing between training instances, and reduced memory pressure. Memory management features include strategic Python GC calls, minimized data copies, and batch processing capabilities. The system leverages Rayon's thread pool for asynchronous data prefetching and supports multiple CPU architectures (ARM64, x86, AMD, RISC-V). By combining performance optimizations with developer-friendly APIs, BULKI-Store addresses the complex data management challenges of modern HPC applications while maintaining compatibility across heterogeneous computing environments.

Zhang, Wei [Lawrence Berkeley National Laboratory ↗

DOE OSTI2025

Exact closed-form unitary transformations of fermionic operators

Unitary transformations play a fundamental role in many-body physics, and except for special cases, they are not expressible in closed form. We present closed-form expressions for unitary transformations generated by a single fermionic operator for Hermitian and anti-Hermitian generators. We demonstrate the usefulness of these expressions in formal analyses of unitary transformations and numerical applications to Hamiltonian downfolding in quantum computing and Heisenberg dynamics. Furthermore, this work paves the way for new analytical treatments of unitary transformations and numerical many-body methods for fermions.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2025

Fusion Model for Metagenomics

This work highlights the use of an embeddings approach that can encode multiple features and create efficient contextualization of profiled metagenomes derived from microbiome samples using computer vision models and image representations of the abundance profiles. The model's embeddings can be used to cluster existing samples based on multiple conditions and interpretations, and new embeddings can be quickly created for new samples and fitted to existing clusters to characterize them. This has practical applications for unknown, unlabeled microbiome samples. The model's embeddings can be used to cluster existing samples based on multiple conditions and interpretations, and new embeddings can be quickly created for new samples and fitted to existing clusters to characterize them. This has practical applications for unknown, unlabeled microbiome samples.

Valdes, CamiloA [Lawrence Livermore National Labor↗

NASA NTRS1974

NCUBE - A clustering algorithm based on a discretized data space

Cluster analysis involves the unsupervised grouping of data. The process provides an automatic procedure for generating known training samples for pattern classification. NCUBE, the clustering algorithm presented, is based upon the concept of imposing a gridwork on the data space. The NCUBE computer implementation of this concept provides an easily derived form of piecewise linear discrimination. This piecewise linear discrimination permits the separation of some types of data groups that are not linearly separable.

Eigen, D. J.↗

NASA NTRS1981

The evaluation of alternate methodologies for land cover classification in an urbanizing area

The usefulness of LANDSAT in classifying land cover and in identifying and classifying land use change was investigated using an urbanizing area as the study area. The question of what was the best technique for classification was the primary focus of the study. The many computer-assisted techniques available to analyze LANDSAT data were evaluated. Techniques of statistical training (polygons from CRT, unsupervised clustering, polygons from digitizer and binary masks) were tested with minimum distance to the mean, maximum likelihood and canonical analysis with minimum distance to the mean classifiers. The twelve output images were compared to photointerpreted samples, ground verified samples and a current land use data base. Results indicate that for a reconnaissance inventory, the unsupervised training with canonical analysis-minimum distance classifier is the most efficient. If more detailed ground truth and ground verification is available, the polygons from the digitizer training with the canonical analysis minimum distance is more accurate.

Smekofski, R. M.↗

NASA NTRS1992

Adaptive unstructured meshing for thermal stress analysis of built-up structures

An adaptive unstructured meshing technique for mechanical and thermal stress analysis of built-up structures has been developed. A triangular membrane finite element and a new plate bending element are evaluated on a panel with a circular cutout and a frame stiffened panel. The adaptive unstructured meshing technique, without a priori knowledge of the solution to the problem, generates clustered elements only where needed. An improved solution accuracy is obtained at a reduced problem size and analysis computational time as compared to the results produced by the standard finite element procedure.

Dechaumphai, Pramote↗

NASA NTRS1992

Adaptive unstructured meshing for thermal stress analysis of built-up structures

Dechaumphai, Pramote↗

NASA NTRS1992

Model reduction for Space Station Freedom

Model reduction is an important practical problem in the control of flexible spacecraft, and a considerable amount of work has been carried out on this topic. Two of the best known methods developed are modal truncation and internal balancing. Modal truncation is simple to implement but can give poor results when the structure possesses clustered natural frequencies, as often occurs in practice. Balancing avoids this problem but has the disadvantages of high computational cost, possible numerical sensitivity problems, and no physical interpretation for the resulting balanced 'modes'. The purpose of this work is to examine the performance of the subsystem balancing technique developed by the investigator when tested on a realistic flexible space structure, in this case a model of the Permanently Manned Configuration (PMC) of Space Station Freedom. This method retains the desirable properties of standard balancing while overcoming the three difficulties listed above. It achieves this by first decomposing the structural model into subsystems of highly correlated modes. Each subsystem is approximately uncorrelated from all others, so balancing them separately and then combining yields comparable results to balancing the entire structure directly. The operation count reduction obtained by the new technique is considerable: a factor of roughly r(exp 2) if the system decomposes into r equal subsystems. Numerical accuracy is also improved significantly, as the matrices being operated on are of reduced dimension, and the modes of the reduced-order model now have a clear physical interpretation; they are, to first order, linear combinations of repeated-frequency modes.

Williams, Trevor↗

NASA NTRS2003

Hierarchical Image Segmentation of Remotely Sensed Data using Massively Parallel GNU-LINUX Software

A hierarchical set of image segmentations is a set of several image segmentations of the same image at different levels of detail in which the segmentations at coarser levels of detail can be produced from simple merges of regions at finer levels of detail. In [1], Tilton, et a1 describes an approach for producing hierarchical segmentations (called HSEG) and gave a progress report on exploiting these hierarchical segmentations for image information mining. The HSEG algorithm is a hybrid of region growing and constrained spectral clustering that produces a hierarchical set of image segmentations based on detected convergence points. In the main, HSEG employs the hierarchical stepwise optimization (HSWO) approach to region growing, which was described as early as 1989 by Beaulieu and Goldberg. The HSWO approach seeks to produce segmentations that are more optimized than those produced by more classic approaches to region growing (e.g. Horowitz and T. Pavlidis, [3]). In addition, HSEG optionally interjects between HSWO region growing iterations, merges between spatially non-adjacent regions (i.e., spectrally based merging or clustering) constrained by a threshold derived from the previous HSWO region growing iteration. While the addition of constrained spectral clustering improves the utility of the segmentation results, especially for larger images, it also significantly increases HSEG s computational requirements. To counteract this, a computationally efficient recursive, divide-and-conquer, implementation of HSEG (RHSEG) was devised, which includes special code to avoid processing artifacts caused by RHSEG s recursive subdivision of the image data. The recursive nature of RHSEG makes for a straightforward parallel implementation. This paper describes the HSEG algorithm, its recursive formulation (referred to as RHSEG), and the implementation of RHSEG using massively parallel GNU-LINUX software. Results with Landsat TM data are included comparing RHSEG with classic region growing.

Tilton, James C.↗

NASA NTRS2011

Fast Image Texture Classification Using Decision Trees

Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

Thompson, David R.↗

NASA NTRS2016

K-Shell Photoabsorption and Photoionisation of Trace Elements I. Isoelectronic Sequences With Electron Number 3< or = N < or = 11

Context. With the recent launching of the Hitomi X-ray space observatory, K lines and edges of chemical elements with low cosmic abundances, namely F, Na, P, Cl, K, Sc, Ti, V, Cr, Mn, Co, Cu and Zn, can be resolved and used to determine important properties of supernova remnants, galaxy clusters and accreting black holes and neutron stars.Aims. The second stage of the present ongoing project involves the computation of the accurate photoabsorption and photoionisation cross sections required to interpret the X-ray spectra of such trace elements.Methods. Depending on target complexity and computer tractability, ground-state cross sections are computed either with the close-coupling Breit-Pauli R-matrix method or with the autostructure atomic structure code in the isolated-resonance approximation. The intermediate-coupling scheme is used whenever possible. In order to determine a realistic K-edge behaviour for each species, both radiative and Auger dampings are taken into account, the latter being included in the R-matrix formalism by means of an optical potential.Results. Photoabsorption and total and partial photoionisation cross sections are reported for isoelectronic sequences with electron numbers 3< or = N< or = 11. The Na sequence (N=11) is used to estimate the contributions from configurations with a 2s hole (i.e. [2s]) and those containing 3d orbitals, which will be crucial when considering sequences with N 11.Conclusions. It is found that the [2s/u] configurations must be included in the target representations of species with N> 11 as they contribute significantly to the monotonic background of the cross section between the L and K edges. Configurations with 3d orbitals are important in rendering an accurate L edge, but they can be practically neglected in the K-edge region.

atomic data / X-rays: general↗

NASA NTRS2014

The Roots of Beowulf

The first Beowulf Linux commodity cluster was constructed at NASA's Goddard Space Flight Center in 1994 and its origins are a part of the folklore of high-end computing. In fact, the conditions within Goddard that brought the idea into being were shaped by rich historical roots, strategic pressures brought on by the ramp up of the Federal High-Performance Computing and Communications Program, growth of the open software movement, microprocessor performance trends, and the vision of key technologists. This multifaceted story is told here for the first time from the point of view of NASA project management.

Beowulf↗

NASA NTRS1994

Performance Measurement, Visualization and Modeling of Parallel and Distributed Programs

This paper presents a methodology for debugging the performance of message-passing programs on both tightly coupled and loosely coupled distributed-memory machines. The AIMS (Automated Instrumentation and Monitoring System) toolkit, a suite of software tools for measurement and analysis of performance, is introduced and its application illustrated using several benchmark programs drawn from the field of computational fluid dynamics. AIMS includes (i) Xinstrument, a powerful source-code instrumentor, which supports both Fortran77 and C as well as a number of different message-passing libraries including Intel's NX Thinking Machines' CMMD, and PVM; (ii) Monitor, a library of timestamping and trace -collection routines that run on supercomputers (such as Intel's iPSC/860, Delta, and Paragon and Thinking Machines' CM5) as well as on networks of workstations (including Convex Cluster and SparcStations connected by a LAN); (iii) Visualization Kernel, a trace-animation facility that supports source-code clickback, simultaneous visualization of computation and communication patterns, as well as analysis of data movements; (iv) Statistics Kernel, an advanced profiling facility, that associates a variety of performance data with various syntactic components of a parallel program; (v) Index Kernel, a diagnostic tool that helps pinpoint performance bottlenecks through the use of abstract indices; (vi) Modeling Kernel, a facility for automated modeling of message-passing programs that supports both simulation -based and analytical approaches to performance prediction and scalability analysis; (vii) Intrusion Compensator, a utility for recovering true performance from observed performance by removing the overheads of monitoring and their effects on the communication pattern of the program; and (viii) Compatibility Tools, that convert AIMS-generated traces into formats used by other performance-visualization tools, such as ParaGraph, Pablo, and certain AVS/Explorer modules.

Yan, Jerry C.↗

NASA NTRS1980

Computer classified Landsat data used as a forest stratifier

Results of two forest stratification projects are presented to show that Landsat data, when combined with guided clustering techniques, can provide detailed stratification of forest environment. It is shown that conifer species groupings, vegetative cover classes, and three size classes can be discriminated with accuracies ranging from 83 to 91%.

Fox, L., III↗

NASA NTRS2011

Base Heating Sensitivity Study for a 4-Cluster Rocket Motor Configuration in Supersonic Freestream

In support of launch vehicle base heating and pressure prediction efforts using the Loci-CHEM Navier-Stokes computational fluid dynamics solver, 35 numerical simulations of the NASA TND-1093 wind tunnel test have been modeled and analyzed. This test article is composed of four JP-4/LOX 500 lbf rocket motors exhausting into a Mach 2 - 3.5 wind tunnel at various ambient pressure conditions. These water-cooled motors are attached to a base plate of a standard missile forebody. We explore the base heating profiles for fully coupled finite-rate chemistry simulations, one-way coupled RAMP (Reacting And Multiphase Program using Method of Characteristics)-BLIMPJ (Boundary Layer Integral Matrix Program - Jet Version) derived solutions and variable and constant specific heat ratio frozen flow simulations. Variations in turbulence models, temperature boundary conditions and thermodynamic properties of the plume have been investigated at two ambient pressure conditions: 255 lb/sq ft (simulated low altitude) and 35 lb/sq ft (simulated high altitude). It is observed that the convective base heat flux and base temperature are most sensitive to the nozzle inner wall thermal boundary layer profile which is dependent on the wall temperature, boundary layer s specific energy and chemical reactions. Recovery shock dynamics and afterburning significantly influences convective base heating. Turbulence models and external nozzle wall thermal boundary layer profiles show less sensitivity to base heating characteristics. Base heating rates are validated for the highest fidelity solutions which show an agreement within +/-10% with respect to test data.

Mehta, Manish↗

NASA NTRSDate not supplied

Optical Time Transfer for Bistatic SAR Spacecraft

A spacecraft-to-spacecraft optical time-transfer simulation has been developed as a tool for informing NASA’s Surface Deformation and Change (SDC) mission architecture. The SDC mission will combine radar images from multiple spacecraft to improve understanding of the Earth’s sea-level and landscape changes. Spacecraft must be precisely synchronized in order to create sharp radar images. Simulation of multiple spacecraft time-synchronizing via laser communication can inform technology choices of a mission by providing a picosecond-precision level estimate of clock error. This timing and ranging simulation has been combined with a radar system performance analysis pipeline. The simulated timing errors are used to predict performance of bistatic SAR systems in the presence of oscillator noise and time synchronization in accuracy. This analysis includes both analytic approximation equations from existing literature, and a numerical radar simulation to extract key system performance parameters like phase error and signal-to-noise ratio (SNR)degradation. Precision time-transfer techniques facilitate the accurate synchronization of clocks between any combination of terminals. Most time-transfer technology for comparing two clocks at different terminals use radio frequencies (RF) to measure the time delay between the sending and receiving of signals. Laser technology offers the capability to transmit high data rates with systems that are of smaller size and lower power than comparable RF systems. The clocks on independent spacecraft will have some phase and frequency errors between them that result in clock drift. The two clock models that are included in this bi-directional MATLAB simulation are a cesium-based Chip-Scale Atomic Clock (CSAC) and a rubidium-based Miniature Atomic Clock (MAC). The CSAC has flown as hardware for small satellite missions such as the University of Florida’s CHOMPTT mission. A study of example orbits, including that of NASA NASA-ISRO Synthetic Aperture Radar Mission (NISAR) mission, and lasing rates demonstrate the impact of flight configuration parameters on the synchronization error between two spacecraft. The MATLAB timing simulation uses a Runge-Kutta 4th-order method to propagate spacecraft orbits and computes the light-travel time estimate between them. The simulation outputs the estimated range and estimated clock error based on a user-defined spacecraft cluster configuration. The radar simulation and analytic approximations are applied to evaluate a potential future NASA bistatic SAR constellation architecture. In the proposed architecture, satellites follow each other in the same orbit at 800 km altitude, with a 210 km baseline. We also baseline the CSAC as an ultra stable oscillator, and use NASA’s NISAR for baseline radar system parameters to compute a clock-system introduced phase error of 5.6 degrees without synchronization by frequent time transfer. We build on this base case with a sensitivity analysis of radar performance over a proposed range of constellation and radar system parameters. With this analysis pipeline, we comment on which radar parameters should or should not be changed to minimize synchronization requirements. This analysis technique could be extended or modified to evaluate the timing requirements of other geometries for other future multistatic SAR missions, or other interferometric satellite missions.

Surface Deformation and Change↗

NASA NTRS2016

ERRATUM: 'MAPPING THE GAS TURBULENCE IN THE COMA CLUSTER: PREDICTIONS FOR ASTRO-H'

The published version of this paper contained an error in Figure 5. This figure is intended to show the effect on the structure function of subtracting the bias induced by the statistical and systematic errors on the line shift. The filled circles show the bias-subtracted structure function. The positions of these points in the left panel of the original figure were calculated incorrectly. The figure is reproduced below (with the original caption) with the correct values for the bias-subtracted structure function. No other computations or figures in the original manuscript are affected.

galaxies: clusters: intracluster medium- methods: ↗

NASA NTRS2004

Markov Chain Monte Carlo Joint Analysis of Chandra X-Ray Imaging Spectroscopy and Sunyaev-Zel'dovich Effect Data

X-ray and Sunyaev-Zel'dovich effect data can be combined to determine the distance to galaxy clusters. High-resolution X-ray data are now available from Chandra, which provides both spatial and spectral information, and Sunyaev-Zel'dovich effect data were obtained from the BIMA and Owens Valley Radio Observatory (OVRO) arrays. We introduce a Markov Chain Monte Carlo procedure for the joint analysis of X-ray and Sunyaev- Zel'dovich effect data. The advantages of this method are the high computational efficiency and the ability to measure simultaneously the probability distribution of all parameters of interest, such as the spatial and spectral properties of the cluster gas and also for derivative quantities such as the distance to the cluster. We demonstrate this technique by applying it to the Chandra X-ray data and the OVRO radio data for the galaxy cluster A611. Comparisons with traditional likelihood ratio methods reveal the robustness of the method. This method will be used in follow-up paper to determine the distances to a large sample of galaxy cluster.

Bonamente, Massimillano↗