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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

High-Performance Key-Value Store on OpenSHMEM

Recently, there has been a growing interest in enabling fast data analytics by leveraging system capabilities from large-scale high-performance computing (HPC) systems. OpenSHMEM is a popular run-time system on HPC systems that has been used for large-scale compute-intensive scientific applications. In this paper, we propose to leverage OpenSHMEM to design a distributed in-memory key-value store for fast data analytics. Accordingly, we have developed SHMEMCache on top of OpenSHMEM to leverage its symmetric global memory, efficient one-sided communication operations and general portability. We have also evaluated SHMEMCache through extensive experimental studies. Our results show that SHMEMCache has accomplished significant performance improvements over hte original Memcached in terms of latency and throughput. Our evaluation on the Titan supercomputer has also demonstrated that SHMEMCache can scale to 1024 nodes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Locally adaptive artificial viscosity strategies for Lagrangian hydrodynamics

To accurately model inviscid flow with shock waves using staggered-grid Lagrangian hydrodynamics, artificial viscosity is introduced to convert kinetic energy into internal energy, thereby providing a mechanism to generate the required entropy increase across shocks. In this paper, we propose a new method for constructing an adaptive, artificial viscosity in the context of one-dimensional, staggered-grid Lagrangian hydrodynamics. Our adaptive, artificial viscosity is defined in terms of two parameters that depend on density locally in a neighborhood around a shock, and hence, vary cell-by-cell. Our methodology is based on building a reference set of pre-computed optimal, globally constant artificial viscosity coefficients for a family of isolated shock test problems. For arbitrary flows, the evaluation of the unknown coefficients is automated by first estimating shock intensity locally, and second computing the corresponding adaptive parameter value via interpolation over the data from the pre-computed reference set of optimal, globally constant coefficient values. To illustrate the performance of our new approach, we compare our results against two existing methods. The first method is a limiter-based approach, which relies on estimating velocity gradients of the flow, and the second method is utilized in several commercial codes. Finally, we demonstrate that our new adaptive methodology produces more accurate results for a variety of tests with propagating shock waves, as well as for the aforementioned family of isolated shock problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

MAPA: Multi-Accelerator Pattern Allocation Policy for Multi-Tenant GPU Servers

Multi-accelerator servers are increasingly being deployed in shared multi-tenant environments (such as in cloud data centers) in order to meet the demands of large-scale compute-intensive workloads. In addition, these accelerators are increasingly being inter-connected in complex topologies and workloads are exhibiting a wider variety of inter-accelerator communication patterns. However, existing al-location policies are ill-suited for these emerging use-cases. Specifically, this work identifies that multi-accelerator workloads are commonly fragmented leading to reduced bandwidth and increased latency for inter-accelerator communication. We propose Multi-Accelerator Pattern Allocation (MAPA), a graph pattern mining approach towards providing generalized allocation support for allocating multi-accelerator workloads on multi-accelerator servers. We demonstrate that MAPA is able to improve the execution time of multi-accelerator workloads and that MAPA is able to provide generalized benefits across various accelerator topologies. Finally, we demonstrate a speedup of 12.4% for75th percentile of jobs with the worst case execution time reduced by up to 35% against baseline policy using MAPA.

Ranganath, Kiran↗
DOE OSTI2020

Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated models

One of the most promising devices for realizing power production through nuclear fusion is the tokamak. To maximize performance, it is preferable that tokamak reactors achieve advanced operating scenarios characterized by good plasma confinement, improved magnetohydrodynamic (MHD) stability, and a largely non-inductively driven plasma current. Such scenarios could enable steady-state reactor operation with high \emph{fusion gain} --- the ratio of produced fusion power to the external power provided through the plasma boundary. Precise and robust control of the evolution of the plasma boundary shape as well as the spatial distribution of the plasma current, density, temperature, and rotation will be essential to achieving and maintaining such scenarios. The complexity of the evolution of tokamak plasmas, arising due to nonlinearities and coupling between various parameters, motivates the use of model-based control algorithms that can account for the system dynamics. In this work, a learning-based accelerated model trained on data from the National Spherical Torus Experiment Upgrade (NSTX-U) is employed to develop planning and control strategies for regulating the density and temperature profile evolution around desired trajectories. The proposed model combines empirical scaling laws developed across multiple devices with neural networks trained on empirical data from NSTX-U and a database of first-principles-based computationally intensive simulations. The reduced execution time of the accelerated model will enable practical application of optimization algorithms and reinforcement learning approaches for scenario planning and control development. An initial demonstration of applying optimization approaches to the learning-based model is presented, including a strategy for mitigating the effect of leaving the finite validity range of the accelerated model. The approach shows promise for actuator planning between experiments and in real-time.

machine learning↗
NASA NTRS1997

Women in Flight Research at NASA Dryden Flight Research Center from 1946 to 1995: Monographs in Aerospace History - Number 6

This monograph discusses the working and living environment of women involved with flight research at NASA Dryden Flight Research Center during the late 1940s and early 1950s. The women engineers, their work and the airplanes they worked on from 1960 to December 1995 are highlighted. The labor intensive data gathering and analysis procedures and instrumentation used before the age of digital computers are explained by showing and describing typical instrumentation found on the X-series aircraft from the X-1 through the X-15. The data reduction technique used to obtain the Mach number position error curve for the X-1 aircraft and which documents the historic first flight to exceed the speed of sound is described and a Mach number and altitude plot from an X-15 flight is shown.

Powers, Sheryll Goecke↗
NASA NTRS1999

Technology Assessment of High Capacity Data Storage Systems: Can We Avoid a Data Survivability Crisis?

In a recent address at the California Science Center in Los Angeles, Vice President Al Gore articulated a Digital Earth Vision. That vision spoke to developing a multi-resolution, three-dimensional visual representation of the planet into which we can roam and zoom into vast quantities of embedded geo-referenced data. The vision was not limited to moving through space, but also allowing travel over a time-line, which can be set for days, years, centuries, or even geological epochs. A working group of Federal Agencies, developing a coordinated program to implement the Vice President's vision, developed the definition of the Digital Earth as a visual representation of our planet that enables a person to explore and interact with the vast amounts of natural and cultural geo-referenced information gathered about the Earth. One of the challenges identified by the agencies was whether the technology existed that would be available to permanently store and deliver all the digital data that enterprises might want to save for decades and centuries. Satellite digital data is growing by Moore's Law as is the growth of computer generated data. Similarly, the density of digital storage media in our information-intensive society is also increasing by a factor of four every three years. The technological bottleneck is that the bandwidth for transferring data is only growing at a factor of four every nine years. This implies that the migration of data to viable long-term storage is growing more slowly. The implication is that older data stored on increasingly obsolete media are at considerable risk if they cannot be continuously migrated to media with longer life times. Another problem occurs when the software and hardware systems for which the media were designed are no longer serviced by their manufacturers. Many instances exist where support for these systems are phased out after mergers or even in going out of business. In addition, survivability of older media can suffer from physical breakdown of components (e.g. tapes simply lose their magnetic properties after a long time in storage). As a result, a potential data survivability crisis is emerging. The scale of the crisis is comparable to that facing the Social Security System. Sometime in one or two decades, the exponential growth of data will become so great that many enterprises will not be able to migrate through their data to more permanent media during the lifetime of the media on which it resides. This will result in significant losses of data and their resultant impacts. To avoid this crisis, we need to plan and devote greater financial and intellectual resources are needed for the development and refinement of new storage media and migration technologies in order to preserve all data any organization determines worth saving permanently. This talk will explore technological solutions and suggested recommendations to address this technological data crisis.

Halem, Milton↗
NASA NTRS1991

Dynamics Explorer guest investigator

A data base of satellite particle, electric field, image, and plasma data was used to determine correlations between the fields and the particle auroral boundaries. A data base of 8 days of excellent coverage from all instruments was completed. The geomagnetic conditions associated with each of the selected data periods, the number of UV image passes per study day that were obtained, and the total number of UV images for each day are given in tabular form. For each of the days listed in Table 1, both Vector Electric Field Instrument (VEFI) electric potential data and LAPI integrated particle energy fluxes were obtained. On the average, between 8 and 11 passes of useful data per day were obtained. These data are displayed in a format such that either the statistical electric field model potential or the statistical precipitation energy flux could be superimposed. The Heppner and Maynard (1987) and Hardy et al. (1987) models were used for the electric potential and precipitation, respectively. In addition, the auroral image intensity along the Dynamics Explorer-2 satellite pass could be computed and plotted along with the LAPI precipitation data and Hardy et al. (1987) values.

Sojka, Jan J.↗
NASA NTRSDate not supplied

Computing rovibrational, vibrational, and cascade emission spectra for comparison to astronomical observations

Over the last several years, our group has been involved in developing approaches to compute highly accurate spectroscopic constants and vibrational frequencies for small transient molecules that may be used in the interpretation and assignment of high-resolution laboratory experiments as well as high-resolution astronomical spectra. Additionally, we have used the computed spectroscopic constants to simulate purely rotational and rovibrational spectra so that these may be compared directly with high-resolution astronomical observations, and we have worked on developing approaches that can be applied to much larger molecules, such as polycyclic aromatic hydrocarbon (PAH) molecules, where we can explicitly determine anharmonic corrections to vibrational frequencies as well as take into account intensity sharing due to resonances. In addition, for PAH molecules we have used the anharmonic data to compute a library of temperature-dependent anharmonic vibrational spectra, with proper inclusion of polyad resonances, which can then be used to model the cascade emission spectra of PAH molecules – the type of spectra directly observed by astronomers. Another part of our work in spectroscopic signatures involves computing highly accurate line lists for common molecules, such as CO2, SO2, and NH3, which occur in many astrophysical environments, including the atmospheres of exoplanents, and often need to have their lines identified in high-resolution observations in order to determine which lines are due to other molecules. In order to characterize the atmospheres of hot exoplanets, these line lists need to be very accurate and extend to very high energies. I will discuss our latest work in these areas of astrochemical spectroscopy research.

Timothy J Lee↗
DOE OSTI2016

High Energy Physics Exascale Requirements Review. An Office of Science review sponsored jointly by Advanced Scientific Computing Research and High Energy Physics, June 10-12, 2015, Bethesda, Maryland

The U.S. Department of Energy (DOE) Office of Science (SC) Offices of High Energy Physics (HEP) and Advanced Scientific Computing Research (ASCR) convened a programmatic Exascale Requirements Review on June 10–12, 2015, in Bethesda, Maryland. This report summarizes the findings, results, and recommendations derived from that meeting. The high-level findings and observations are as follows. Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude — and in some cases greater — than that available currently. The growth rate of data produced by simulations is overwhelming the current ability of both facilities and researchers to store and analyze it. Additional resources and new techniques for data analysis are urgently needed. Data rates and volumes from experimental facilities are also straining the current HEP infrastructure in its ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. A close integration of high-performance computing (HPC) simulation and data analysis will greatly aid in interpreting the results of HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. Long-range planning between HEP and ASCR will be required to meet HEP’s research needs. To best use ASCR HPC resources, the experimental HEP program needs (1) an established, long-term plan for access to ASCR computational and data resources, (2) the ability to map workflows to HPC resources, (3) the ability for ASCR facilities to accommodate workflows run by collaborations potentially comprising thousands of individual members, (4) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, (5) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

A feasibility study on porting the community land model onto accelerators using OpenACC

As environmental models (such as Accelerated Climate Model for Energy (ACME), Parallel Reactive Flow and Transport Model (PFLOTRAN), Arctic Terrestrial Simulator (ATS), etc.) became more and more complicated, we are facing enormous challenges regarding to porting those applications onto hybrid computing architecture. OpenACC appears as a very promising technology, therefore, we have conducted a feasibility analysis on porting the Community Land Model (CLM), a terrestrial ecosystem model within the Community Earth System Models (CESM)). Specifically, we used automatic function testing platform to extract a small computing kernel out of CLM, then we apply this kernel into the actually CLM dataflow procedure, and investigate the strategy of data parallelization and the benefit of data movement provided by current implementation of OpenACC. Even it is a non-intensive kernel, on a single 16-core computing node, the performance (based on the actual computation time using one GPU) of OpenACC implementation is 2.3 time faster than that of OpenMP implementation using single OpenMP thread, but it is 2.8 times slower than the performance of OpenMP implementation using 16 threads. On multiple nodes, MPI_OpenACC implementation demonstrated very good scalability on up to 128 GPUs on 128 computing nodes. This study also provides useful information for us to look into the potential benefits of “deep copy” capability and “routine” feature of OpenACC standards. In conclusion, we believe that our experience on the environmental model, CLM, can be beneficial to many other scientific research programs who are interested to porting their large scale scientific code using OpenACC onto high-end computers, empowered by hybrid computing architecture.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Energy Efficient Stochastic-Based Deep Spiking Neural Networks for Sparse Datasets

With large deep neural networks (DNNs) neces- sary to solve complex and data-intensive problems, energy efficiency is a key bottleneck for effectively deploying DL in the real world. Deep spiking NNs have gained much research at- tention recently due to the interest in building biological neural networks and the availability of neuromorphic platforms, which can be orders of magnitude more energy efficient compared to CPUs and GPUs. Although spiking NNs have proven to be an efficient technique for solving many machine learning and computer vision problems, to the best of our knowledge, this is the first attempt to adapt spiking NNs to sparse datasets. In this paper, we study the behaviour of spiking NNs in handling NLP datasets and the sparsity in their data representation. Then, we propose a novel framework for spiking NN using the concept of stochastic computing. Specifically, instead of generating spike trains with firing rates proportional to the intensity of each value in the feature set separately, the whole feature set is treated as a distribution function and a stochastic spiking train that follow this distribution is generated. This framework reduces the connectivity between NN layers from O(N) to O(logN). Also, it encodes input data differently and make suitable to handle sparse datasets. Finally, the framework achieves high energy efficiency since it uses Integrate and Fire neurons same as conventional spiking NNs. The results show that our proposed stochastic-based SNN achieves nearly the same accuracy as the original DNN on MNIST dataset, and it has better performance than state- of-the-art SNN. Besides that stochastic-based SNN is energy efficient, where the fully connected DNN, the conventional SNN, and the data normalized SNN consume 38.24, 1.83, and 1.85 -times more energy than the stochastic-based SNN, respectively. For sparse datasets, including IMDb and In-House clinical datasets, stochastic-based SNN achieves performance comparable to that of the conventional DNN. However, the conventional spiking NN has a significant decline in classification accuracy.

Alawad, Mohammed↗
DOE OSTI2021

SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees

Deep unfolding networks have recently gained popularity for solving imaging inverse problems. However, the computational and memory complexity of data-consistency layers within traditional deep unfolding networks scales with the number of measurements, limiting their applicability to large-scale imaging inverse problems. We propose SGD-Net as a new methodology for improving the efficiency of deep unfolding through stochastic approximations of the data-consistency layers. Our theoretical analysis shows that SGD-Net can be trained to approximate batch deep unfolding networks to an arbitrary precision. Our simulations on intensity diffraction tomography and sparse-view computed tomography show that SGD-Net can match the performance of the traditional batch network at a fraction of training and testing complexity.Deep unfolding networks have recently gained popularity for solving imaging inverse problems. However, the computational and memory complexity of data-consistency layers within traditional deep unfolding networks scales with the number of measurements, limiting their applicability to large-scale imaging inverse problems. We propose SGD-Net as a new methodology for improving the efficiency of deep unfolding through stochastic approximations of the data-consistency layers. Our theoretical analysis shows that SGD-Net can be trained to approximate batch deep unfolding networks to an arbitrary precision. Our simulations on intensity diffraction tomography and sparse-view computed tomography show that SGD-Net can match the performance of the traditional batch network at a fraction of training and testing complexity.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

Bayesian learning

In 1983 and 1984, the Infrared Astronomical Satellite (IRAS) detected 5,425 stellar objects and measured their infrared spectra. In 1987 a program called AUTOCLASS used Bayesian inference methods to discover the classes present in these data and determine the most probable class of each object, revealing unknown phenomena in astronomy. AUTOCLASS has rekindled the old debate on the suitability of Bayesian methods, which are computationally intensive, interpret probabilities as plausibility measures rather than frequencies, and appear to depend on a subjective assessment of the probability of a hypothesis before the data were collected. Modern statistical methods have, however, recently been shown to also depend on subjective elements. These debates bring into question the whole tradition of scientific objectivity and offer scientists a new way to take responsibility for their findings and conclusions.

Denning, Peter J.↗
NASA NTRS2004

QuakeSim and the Solid Earth Research Virtual Observatory

We are developing simulation and analysis tools in order to develop a solid Earth science framework for understanding and studying active tectonic and earthquake processes. The goal of QuakeSim and its extension, the Solid Earth Research Virtual Observatory (SERVO), is to study the physics of earthquakes using state-of-the-art modeling, data manipulation, and pattern recognition technologies. We are developing clearly defined accessible data formats and code protocols as inputs to simulations, which are adapted to high-performance computers. The solid Earth system is extremely complex and nonlinear resulting in computationally intensive problems with millions of unknowns. With these tools it will be possible to construct the more complex models and simulations necessary to develop hazard assessment systems critical for reducing future losses from major earthquakes. We are using Web (Grid) service technology to demonstrate the assimilation of multiple distributed data sources (a typical data grid problem) into a major parallel high-performance computing earthquake forecasting code. Such a linkage of Geoinformatics with Geocomplexity demonstrates the value of the Solid Earth Research Virtual Observatory (SERVO) Grid concept, and advances Grid technology by building the first real-time large-scale data assimilation grid.

virtual observatory↗
NASA NTRS1999

Mars Gravity Field Model Development from Mars Global Surveyor Tracking Data

Since Feb. 99 the MGS spacecraft has been in a near circular orbit at about 400 km altitude. The MGS has been regularly tracked by the Deep Space Network (DSN) at X-band and for a 3 week period in February was tracked almost continuously for an intensive gravity modeling activity that would form the basis of the orbital computations for the rest of the mission. The data collected during this calibration period and the earlier SPO and Hiatus periods have now been used to develop a new gravity field model for Mars that is showing considerable new detail in both the northern and southern hemispheres. Until February no data at 400 km altitude or lower had been acquired on any previous mission south of about 35S and all the previous data were of significantly lower quality. Low altitude data (-170 km) were obtained over the higher latitudes of the northern hemisphere during the SPO periods but because of the high eccentricity of the orbit nothing of similar quality was obtainable for the southern hemisphere. The new models are of spherical harmonic degree and order 70 or higher and are suggesting large anomalies are often associated with the large impact features. Gravity data have also been obtained over both the northern and southern polar ice caps. The MGS orbit quality resulting from the use of these newer models is better than any previous Mars missions and is approaching the ten's of meter level that had been hoped would be eventually realizable.

Lemoine, F. G.↗
DOE OSTI2024

Elimination LArTPC Simulation Uncertainty With Modifcations to TPC Wire Waveforms

This paper introduces a novel approach to reducing systematic uncertainties in LArTPC detector simulations, which arise from known detector effects that are challenging and computationally intensive to model. The proposed method involves modifying simulated waveforms based on observed discrepancies in ionization signals from the TPC between actual data and simulations. This approach reduces the sensitivity to detailed detector modeling and decreases the computational resources required for accurate simulations.

Mktchyan, Karen↗