Reeber v2.0
Reeber is a library for shared- and distributed-memory parallel computation of merge trees
Morozov, Dmitriy↗
SEARCH · Engineering Papers
Results for “distributed computation”
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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Elga v.1
SAND2021-4635 O Elga is a software system that provides distributed and elastic computation for temporal graph algorithms. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Gabert, Kasimir↗
Orchestration of materials science workflows for heterogeneous resources at large scale
In the era of big data, materials science workflows need to handle large-scale data distribution, storage, and computation. Any of these areas can become a performance bottleneck. We present a framework for analyzing internal material structures (e.g., cracks) to mitigate these bottlenecks. We demonstrate the effectiveness of our framework for a workflow performing synchrotron X-ray computed tomography reconstruction and segmentation of a silica-based structure. Our framework provides a cloud-based, cutting-edge solution to challenges such as growing intermediate and output data and heavy resource demands during image reconstruction and segmentation. Specifically, our framework efficiently manages data storage, scaling up compute resources on the cloud. The multi-layer software structure of our framework includes three layers. A top layer uses Jupyter notebooks and serves as the user interface. A middle layer uses Ansible for resource deployment and managing the execution environment. A low layer is dedicated to resource management and provides resource management and job scheduling on heterogeneous nodes (i.e., GPU and CPU). At the core of this layer, Kubernetes supports resource management, and Dask enables large-scale job scheduling for heterogeneous resources. The broader impact of our work is four-fold: through our framework, we hide the complexity of the cloud’s software stack to the user who otherwise is required to have expertise in cloud technologies; we manage job scheduling efficiently and in a scalable manner; we enable resource elasticity and workflow orchestration at a large scale; and we facilitate moving the study of nonporous structures, which has wide applications in engineering and scientific fields, to the cloud. While we demonstrate the capability of our framework for a specific materials science application, it can be adapted for other applications and domains because of its modular, multi-layer architecture.
97 MATHEMATICS AND COMPUTING↗
Confidentiality-preserving machine learning algorithms for soft-failure detection in optical communication networks
Automated fault management is at the forefront of next-generation optical communication networks. The increase in complexity of modern networks has triggered the need for programmable and software-driven architectures to support the operation of agile and self-managed systems. In these scenarios, the European Telecommunications Standards Institute zero-touch network and service management approach is imperative. The need for machine learning algorithms to process the large volume of telemetry data brings safety concerns as distributed cloud-computing solutions become the preferred approach for deploying reliable communication network automation. This paper’s contribution is twofold. First, we propose a simple yet effective method to guarantee the confidentiality of the telemetry data based on feature scrambling. The method allows the operation of third-party computational services without direct access to the full content of the collected data. Additionally, the effectiveness of four unsupervised machine learning algorithms for soft-failure detection is evaluated when applied to the scrambled telemetry data. The methods are based on factor analysis, principal component analysis, nonlinear principal component analysis, and singular value decomposition. Most dimensionality reduction algorithms have the common property that they can maintain similar levels of fault classification performance while hiding the data structure from unauthorized access. Evaluations of the proposed algorithms demonstrate this capability.
97 MATHEMATICS AND COMPUTING↗
A New Capability of E4D For 3D Parallel Joint Inversion of DC Resistivity And Traveltime Data on Unstructured Mesh
A major challenge in interpreting geophysical data is how to derive consistent three-dimensional (3D) earth models of different physical properties from spatially and temporally limited measurements. Joint inversion with cross-gradient constraints is an approach to find such models by imposing structural similarities between different physical parameters. We have developed a parallel distributed-memory joint inversion code for direct-current (DC) resistivity and traveltime data using the cross-gradient constraint on unstructured mesh. The code utilizes existing E4D framework for parallel forward simulation, distributed storage and computation of the Jacobian matrix of forward operator, and parallel execution of matrix-vector multiplication during inversion. Besides, the joint inversion is solved by nonlinear conjugate gradient algorithm parallelized for DC resistivity and traveltime data. The joint inversion capability of E4D was tested using synthetic data from cross-borehole DC resistivity and traveltime data. The results indicate that the shape and size of the anomalies from the joint inversion are more reliable than those from separate inversions.
58 GEOSCIENCES↗
Platform for efficient large-scale storage and analysis of multi-omics data in plant and microbial systems (Final Technical Report)
Genomic variation at the sequence level fundamentally affects the phenotypic state of all organisms at all stages of development, while dynamic processes such as changes in the epigenome (e.g. DNA methylation state) and transcriptome regulate the specific phenotype expressed at any given state of development based upon that genomic variation. In plants, DNA methylation is a particularly important mechanism for both regulating transcriptomic expression and for management of genomic variations that could be deleterious to the organism due to the presence of active retrotransposons in plant genomes. While DNA methylation is heritable, it is also dynamic through a given plant’s development and life cycle, particularly during the development from seed to mature specimen suggesting variations in DNA methylation could be critical regulators of biologically and commercially important phenotypes such as time to flowering; in addition, plant DNA methylation is more complex than that of animals, with methylation of CHG and CHH trinucleotides evident in addition to the better-known CG methylation. The complexity of plant DNA methylation and its interplay with genomic sequence variation, transcriptomics and other epigenomic factors demand a storage and analysis framework that can cope with the complexity both within a single specimen and with analyses that span many individuals and even many species, such as attempts to extend models from model organisms to commercially relevant species. In addition to complexity, the rapid development and proliferation of sequencing technology has led to an explosion of data volume that conventional storage and analysis solutions will likely be unable to cope with in the long run. We proposed to study these with suitable distributed storage and computation and therefore for the application of cloud computing to biological analyses; integrate with existing data sources and compatible with virtually any interface use case, from fully automated shell scripts to notebooks and do all these at scale in this STTR grant.
60 APPLIED LIFE SCIENCES↗
MPACT Theory Manual (V.4.3)
This manual presents the theory underlying the three-dimensional (3D) whole-core, pin-resolved neutron transport calculation methodologies employed in the MPACT code. MPACT’s primary goal is to provide accurate sub-pin power distributions in a computationally efficient manner. To accomplish this, MPACT offers several different transport method options. The 2D/1D method, in which 3D problems are decomposed into an axial stack of radial “slices,” is currently the most commonly used. In this option, two-dimensional (2D) planar solutions are provided by the method of characteristics (MOC), and axial solutions are provided via one-dimensional (1D) approximate diffusion, or P3 solutions. The radial and axial solutions are coupled by (i) axial and radial transverse leakages, and (ii) a global 3D coarse mesh finite difference (CMFD) solve, which provides both acceleration and stability to the solution iteration scheme. The subsequent chapters of this manual present a range of topics, including MOC, CMFD, axial nodal transport solvers, 2D/1D, self-shielding, depletion, thermal-hydraulics, and transient methods. Development of the underlying theory of the 2D/1D method is presented, diagrams are included to highlight important algorithmic flow, and important concepts are discussed as appropriate. This manual is intended to be self-sufficient, but references to published articles and other materials are included for further reading. MPACT is a relatively new code, with new capabilities and many computational methods that did not exist until recently. This manual is an even newer document, with chapters written by several different code contributors working under time constraints. For these reasons, the manual is not yet complete and finalized. Future versions of this manual will address current deficiencies.
97 MATHEMATICS AND COMPUTING↗
Single Photon Detection with On-Chip Number Resolving Capability
Single photon detection (SPD) plays an important role in many forefront areas of fundamental science and advanced engineering applications. In recent years, rapid developments in superconducting quantum computation, quantum key distribution, and quantum sensing call for SPD in the microwave frequency range. We have explored in this LDRD project a new approach to SPD in an effort to provide deterministic photon-number-resolving capability by using topological Josephson junction structures. In this SAND report, we will present results from our experimental studies of microwave response and theoretical simulations of microwave photon number resolving detector in topological Dirac semimetal Cd 3 As 2 . These results are promising for SPD at the microwave frequencies using topological quantum materials.
47 OTHER INSTRUMENTATION↗
Tempus Project (Final Report)
This final technical report tracks the accomplishments of the Tempus Project to the statement of project objectives and resulting deliverables. The objective of the Tempus project was to develop and demonstrate the capabilities of a secure, modular, and customizable time synchronization platform that provides layers of protection from GPS spoofing attacks and other vulnerabilities. These vulnerabilities present a challenge to power system operators utilizing GPS-based time synchronization, as time synchronization increasingly plays a critical role in the efficient and reliable operation of power systems. The Tempus project addressed these concerns through the development of a secure, customizable time synchronization platform. The Tempus platform applies a layered approach to time security that protects from manipulation of timing systems used by power utilities. The Tempus platform consists of a component framework utilizing modularized time sources and customizable manipulation detection algorithms. With multiple time sources, and comparisons between sources and tracking sudden or subtle changes to the timing phase and time information, the Tempus system can detect and mitigate compromised timing sources.
24 POWER TRANSMISSION AND DISTRIBUTION↗
RTDP: Streaming Readout Real-Time Development and Testing Platform
The Thomas Jefferson National Accelerator Facility (JLab) has created and is currently working on various tools to facilitate streaming readout (SRO) for upcoming experiments. These include reconstruction frameworks with support for Artificial Intelligence/Machine Learning, distributed High Throughput Computing (HTC), and heterogeneous computing which all contribute significantly to swift data processing and analysis. Designing SRO systems that combine such components for new experiments would benefit from a platform that would combine both simulation and execution components for simulation, testing, and validation before large investments are made. The Real-Time Development Platform (RTDP) is being developed as part of an LDRD funded project at JLab. RTDP aims to establish a seamless connection between algorithms, facilitating the seamless processing of data from SRO to analysis, as well as enabling the execution of these algorithms in various configurations on compute and data centers. Individual software components simulating specific hardware can be replaced with actual hardware when it is available.
Gyurjyan, Vardan↗
Summary Report of the SOS26 Workshop held March 11-14, 2024
The SOS26 workshop was organized by Oak Ridge National Laboratory (ORNL) and held March 11-14, 2024, at Cocoa Beach, Florida. The SOS is a workshop organized annually, with a focus on distributed high performance computing (HPC). The technical program of the workshop was developed jointly by Sandia National Laboratories (SNL), ORNL and the Swiss National Supercomputing Center (CSCS). The 2024 SOS26 workshop theme was "Versatile HPC for the evolving and expanding needs of science" and had seven technical sessions covering HPC, data and machine learning (ML) topics. Each session consisted of four or five presentations, followed by a panel discussion. This report documents the workshop proceedings covering all the technical sessions.
97 MATHEMATICS AND COMPUTING↗
X-Ray Emission of Ultraviolet Variable Active Galactic Nucleus Candidates
We analyze the X-ray properties of a sample of 23 high-probability active galactic nucleus (AGN) candidates with ultraviolet variability identified in Wasleske et al. Using data from the Chandra X-ray Observatory and XMM-Newton Observatory, we that find 11/23 nuclei are X-ray detected. We use spectral energy distribution modeling to compute star formation rates and show that the X-ray luminosities are typically in excess of the X-ray emission expected from star formation by at least an order of magnitude. Interestingly, this sample shows a diversity of optical spectroscopic properties. We explore possible reasons for why some objects lack optical spectroscopic signatures of black hole activity while still being UV-variable and X-ray bright. We find that host galaxy stellar emission and obscuration from gas and dust are potential factors. We study where this sample falls on relationships such as α OX - L 2500 and L X - L IR and find that some of the sample falls outside the typical scatter for these relations, indicating that they differ from the standard quasar population. With the diversity of optical spectroscopic signatures and varying impacts of dust and stellar emissions on our sample, these results emphasize the strength of variability in selecting the most complete set of AGN, regardless of other host galaxy properties.
79 ASTRONOMY AND ASTROPHYSICS↗
Size-Resolved Chemical Composition of Particles Collected Using STAC at the Ground Site During the SAIL Campaign in Gunnison, Colorado
Aerosol particles were collected using a four-stage Size and Time-resolved Aerosol Collector (STAC) during the SAIL field campaign. Each stage of STAC separates particles into distinct aerodynamic size fractions with 50% cut-off diameters: Stage A: 2.27 µm Stage B: 0.615 µm Stage C: 0.421 µm Stage D: 0.119 µm Each stage provides both size- and time-resolved sampling, enabling investigation of particle composition across different atmospheric regimes. Only a subset of samples was selected for analysis based on prevailing meteorological conditions (e.g., temperature, humidity, and air-mass influence) to capture representative aerosol types under distinct weather patterns. Collected substrates were first examined under Scanning Electron Microscopy (SEM) to evaluate particle loading, morphology, and spatial distribution. Subsequently, Computer-Controlled Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (CCSEM/EDX) was performed to obtain size-resolved elemental composition of individual particles. A rule-based classification scheme was applied to categorize particles into major compositional groups (e.g., biological, carbonaceous, dust, sulfate, Na-rich, and mixed types). This dataset provides high-resolution morphological and chemical information on atmospheric particles collected during the SAIL campaign, offering insights into the influence of meteorology on aerosol composition and mixing state.
Size and Time-resolved Aerosol Collector↗
Instrument calculates moments of inertia of complex plane figures
Instrument consisting of a narrow field scanner coupled with a simple preprogrammed computer calculates distributive-area properties of complex or irregular plane figures representing cross sections of structural members. The calculator obtains the properties quickly and with a high degree of accuracy.
Myers, W. J.↗
A variational principle for a one-dimensional stellar system.
Hohl-Feix minimum energy principle extended to arbitrary distribution functions for computer experiments on stellar systems
Feix, M. R.↗
Simulation of service load distributions utilizing stationary, non-Gaussian random time histories
Simulation of service load distributions using digital computers with FORTRAN 4 program
Leybold, H. A.↗
Mie Scattering - A computer program and an atlas
Mie scattering computer program, particle distributions, radiation detector lens effects, and scattering intensity graphs
Bicket, N. A.↗
Opacity-probability distribution functions for carbon monoxide at elevated temperatures
Opacity-probability distributions for CO, computing theoretical spectra of line absorption coefficient, discussing effects of temperature and/or turbulent velocity
Kunde, V. G.↗