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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Parallel processing spacecraft communication system

An uplink controlling assembly speeds data processing using a special parallel codeblock technique. A correct start sequence initiates processing of a frame. Two possible start sequences can be used; and the one which is used determines whether data polarity is inverted or non-inverted. Processing continues until uncorrectable errors are found. The frame ends by intentionally sending a block with an uncorrectable error. Each of the codeblocks in the frame has a channel ID. Each channel ID can be separately processed in parallel. This obviates the problem of waiting for error correction processing. If that channel number is zero, however, it indicates that the frame of data represents a critical command only. That data is handled in a special way, independent of the software. Otherwise, the processed data further handled using special double buffering techniques to avoid problems from overrun. When overrun does occur, the system takes action to lose only the oldest data.

Bolotin, Gary S.↗
NASA NTRS1999

Massively Parallel Assimilation of TOGA/TAO and Topex/Poseidon Measurements into a Quasi Isopycnal Ocean General Circulation Model Using an Ensemble Kalman Filter

A massively parallel ensemble Kalman filter (EnKF)is used to assimilate temperature data from the TOGA/TAO array and altimetry from TOPEX/POSEIDON into a Pacific basin version of the NASA Seasonal to Interannual Prediction Project (NSIPP)ls quasi-isopycnal ocean general circulation model. The EnKF is an approximate Kalman filter in which the error-covariance propagation step is modeled by the integration of multiple instances of a numerical model. An estimate of the true error covariances is then inferred from the distribution of the ensemble of model state vectors. This inplementation of the filter takes advantage of the inherent parallelism in the EnKF algorithm by running all the model instances concurrently. The Kalman filter update step also occurs in parallel by having each processor process the observations that occur in the region of physical space for which it is responsible. The massively parallel data assimilation system is validated by withholding some of the data and then quantifying the extent to which the withheld information can be inferred from the assimilation of the remaining data. The distributions of the forecast and analysis error covariances predicted by the ENKF are also examined.

Keppenne, Christian L.↗
NASA NTRS1978

An integrated optical preprocessor for multichannel analog data

As a precursor to the construction of a 16-channel device, a 3-channel integrated optical data preprocessor has been fabricated and tested. The device, which is fabricated in an outdiffused LiNbO3 waveguide, provides a signal related to the channel-by-channel difference between a prerecorded reference set and an incoming data set which is presented in the form of parallel analog voltages. The data are impressed upon the guided wave as a set of electrooptically induced phase shifts, and the comparison to the reference set is performed by holographic subtraction. The reference set can either be introduced as a separate operation or it can be 'learned' by the preprocessor during operation.

Verber, C. M.↗
NASA NTRS1979

Fabrication and testing of a three-channel integrated optical data preprocessor

As a precursor to the construction of a 16-channel device, a 3-channel integrated optical data preprocessor has been fabricated and tested. The device, which is fabricated in an outdiffused LiNbO3 waveguide, provides a signal related to the channel-by-channel difference between a prerecorded reference set and an incoming data set which is presented in the form of parallel analog voltages. The data are impressed upon the guided wave as a set of electrooptically induced phase shifts, and the comparison to the reference set is performed by holographic subtraction. The reference set can either be introduced as a separate operation or it can be 'learned' by the preprocessor during operation.

Verber, C. M.↗
NASA NTRS1984

Space Station Data Systems development

The content and status of the evolving Space Station Data System (SSDS) development program are presented. High level system requirements and schedules are reviewed, and the challenges of SSDS development are discussed. Present content of the program consists of three major activities: (1) two contracted parallel Space Station Data System architecture studies; (2) the space station phase B definition studies to be awarded in early 1985; and (3) a test bed project for the evaluation and proof-of-concept of data systems technology for space station applications and for the advanced development of long-lead hardware and software for the initial and growth space station configurations. An overview of the present SSDS concept is provided, and the roles of NASA centers in this activity are summarized.

Bigham, J. P., Jr.↗
NASA NTRS1992

Dynamic object management for distributed data structures

In distributed-memory multiprocessors, remote memory accesses incur larger delays than local accesses. Hence, insightful allocation and access of distributed data can yield substantial performance gains. The authors argue for the use of dynamic data management policies encapsulated within individual distributed data structures. Distributed data structures offer performance, flexibility, abstraction, and system independence. This approach is supported by data from a trace-driven simulation study of parallel scientific benchmarks. Experimental data on memory locality, message count, message volume, and communication delay suggest that data-structure-specific data management is superior to a single, system-imposed policy.

Totty, Brian K.↗
NASA NTRS2015

Between a Map and a Data Rod

A Digital Divide has long stood between how NASA and other satellite-derived data are typically archived (time-step arrays or maps) and how hydrology and other point-time series oriented communities prefer to access those data. In essence, the desired method of data access is orthogonal to the way the data are archived. Our approach to bridging the Divide is part of a larger NASA-supported data rods project to enhance access to and use of NASA and other data by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS) and the larger hydrology community. Our main objective was to determine a way to reorganize data that is optimal for these communities. Two related objectives were to optimally reorganize data in a way that (1) is operational and fits in and leverages the existing Goddard Earth Sciences Data and Information Services Center (GES DISC) operational environment and (2) addresses the scaling up of data sets available as time series from those archived at the GES DISC to potentially include those from other Earth Observing System Data and Information System (EOSDIS) data archives. Through several prototype efforts and lessons learned, we arrived at a non-database solution that satisfied our objectivesconstraints. We describe, in this presentation, how we implemented the operational production of pre-generated data rods and, considering the tradeoffs between length of time series (or number of time steps), resources needed, and performance, how we implemented the operational production of on-the-fly (virtual) data rods. For the virtual data rods, we leveraged a number of existing resources, including the NASA Giovanni Cache and NetCDF Operators (NCO) and used data cubes processed in parallel. Our current benchmark performance for virtual generation of data rods is about a years worth of time series for hourly data (9,000 time steps) in 90 seconds. Our approach is a specific implementation of the general optimal strategy of reorganizing data to match the desired means of access. Results from our project have already significantly extended NASA data to the large and important hydrology user community that has been, heretofore, mostly unable to easily access and use NASA data.

machine learning↗
NASA NTRS1984

Nuclear Science Symposium, 30th, and Symposium on Nuclear Power Systems, 15th, San Francisco, CA, October 19-21, 1983, Proceedings

The range of disciplines covered includes physics instrumentation, data acquisition, FASTBUS, radiation detectors, scintillators, photomultipliers and optical detectors, subnanosecond 2-D imaging, medical and health instrumentation, reactor instrumentation, and space instrumentation. Cerenkov detectors, the prevention and rate capability of breakdown processes in wire chambers, and an interactive parallel processor for data analysis are studied. Attention is also given to bismuth germanate's role in gamma-ray spectroscopy, and the construction of a broadband universal sampling head is described. Gamma-ray imaging with a rotating hexagonal uniform redundant array, and broadband X-ray astronomical spectrometry are considered.

Nakamura, M.↗
NASA NTRS1999

Parallel Grid Manipulations in Earth Science Calculations

The National Aeronautics and Space Administration (NASA) Data Assimilation Office (DAO) at the Goddard Space Flight Center is moving its data assimilation system to massively parallel computing platforms. This parallel implementation of GEOS DAS will be used in the DAO's normal activities, which include reanalysis of data, and operational support for flight missions. Key components of GEOS DAS, including the gridpoint-based general circulation model and a data analysis system, are currently being parallelized. The parallelization of GEOS DAS is also one of the HPCC Grand Challenge Projects. The GEOS-DAS software employs several distinct grids. Some examples are: an observation grid- an unstructured grid of points at which observed or measured physical quantities from instruments or satellites are associated- a highly-structured latitude-longitude grid of points spanning the earth at given latitude-longitude coordinates at which prognostic quantities are determined, and a computational lat-lon grid in which the pole has been moved to a different location to avoid computational instabilities. Each of these grids has a different structure and number of constituent points. In spite of that, there are numerous interactions between the grids, e.g., values on one grid must be interpolated to another, or, in other cases, grids need to be redistributed on the underlying parallel platform. The DAO has designed a parallel integrated library for grid manipulations (PILGRIM) to support the needed grid interactions with maximum efficiency. It offers a flexible interface to generate new grids, define transformations between grids and apply them. Basic communication is currently MPI, however the interfaces defined here could conceivably be implemented with other message-passing libraries, e.g., Cray SHMEM, or with shared-memory constructs. The library is written in Fortran 90. First performance results indicate that even difficult problems, such as above-mentioned pole rotation- a sparse interpolation with little data locality between the physical lat-lon grid and a pole rotated computational grid- can be solved efficiently and at the GFlop/s rates needed to solve tomorrow's high resolution earth science models. In the subsequent presentation we will discuss the design and implementation of PILGRIM as well as a number of the problems it is required to solve. Some conclusions will be drawn about the potential performance of the overall earth science models on the supercomputer platforms foreseen for these problems.

Sawyer, W.↗
NASA NTRS1994

Progressive Vector Quantization on a massively parallel SIMD machine with application to multispectral image data

A progressive vector quantization (VQ) compression approach is discussed which decomposes image data into a number of levels using full search VQ. The final level is losslessly compressed, enabling lossless reconstruction. The computational difficulties are addressed by implementation on a massively parallel SIMD machine. We demonstrate progressive VQ on multispectral imagery obtained from the Advanced Very High Resolution Radiometer instrument and other Earth observation image data, and investigate the trade-offs in selecting the number of decomposition levels and codebook training method.

Manohar, Mareboyana↗
DOE OSTI2018

Distributed X-ray photon correlation spectroscopy data reduction using Hadoop MapReduce

Multi-speckle X-ray photon correlation spectroscopy (XPCS) is a powerful technique for characterizing the dynamic nature of complex materials over a range of time scales. XPCS has been successfully applied to study a wide range of systems. Recent developments in higher-frame-rate detectors, while aiding in the study of faster dynamical processes, creates large amounts of data that require parallel computational techniques to process in near real-time. Here, an implementation of the multi-tau and two-time autocorrelation algorithms using the Hadoop MapReduce framework for distributed computing is presented. In conclusion, the system scales well with regard to the increase in the data size, and has been serving the users of beamline 8-ID-I at the Advanced Photon Source for near real-time autocorrelations for the past five years.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

A Parallel Machine Learning Workflow for Neutron Scattering Data Analysis

As part of a larger effort, this work-in-progress reports the possible advantages of modifying conventional workflows used to generate labelled training samples and train machine learning (ML) models on them. We compare results from three different workflows using neutron scattering data analysis as the motivating application and report about 20% improvement in speedup, with no appreciable loss of model accuracy, over a baseline workflow.

Wang, Tianle↗
DOE OSTI2019

Parallel Tensor Decompositions for Massive Heterogeneous, Incomplete Data (Final Report: LDRD Project 199986).

We extended the fundamental capabilities of tensor decomposition to a broader range of problems - handling new data types and larger problems. This has implications for data analysis across a range of applications in sensor monitoring, cybersecurity, treaty verification, signal processing, etc. Identifies latent structure within data, enabling anomaly detection, process monitoring, scientific discovery.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Parallel computers - Estimate errors caused by imprecise data

A new approach to the problem of estimating errors caused by imprecise data is proposed in the context of software engineering. A software device is used to produce an ideal solution to the problem, when the computer is capable of computing errors of arbitrary programs. The software engineering aspect of this problem is to describe a device for computing the error estimates in software terms and then to provide precise numbers with error estimates to the user. The feasibility of the program capable of computing both some quantity and its error estimate in the range of possible measurement errors is demonstrated.

Kreinovich, Vladik↗
NASA NTRS2012

Graphics Processing Unit Assisted Thermographic Compositing

Objective Develop a software application utilizing high performance computing techniques, including general purpose graphics processing units (GPGPUs), for the analysis and visualization of large thermographic data sets. Over the past several years, an increasing effort among scientists and engineers to utilize graphics processing units (GPUs) in a more general purpose fashion is allowing for previously unobtainable levels of computation by individual workstations. As data sets grow, the methods to work them grow at an equal, and often greater, pace. Certain common computations can take advantage of the massively parallel and optimized hardware constructs of the GPU which yield significant increases in performance. These common computations have high degrees of data parallelism, that is, they are the same computation applied to a large set of data where the result does not depend on other data elements. Image processing is one area were GPUs are being used to greatly increase the performance of certain analysis and visualization techniques.

Ragasa, Scott↗