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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Volume-scalable high-brightness three-dimensional visible light source

A volume-scalable, high-brightness, electrically driven visible light source comprises a three-dimensional photonic crystal (3DPC) comprising one or more direct bandgap semiconductors. The improved light emission performance of the invention is achieved based on the enhancement of radiative emission of light emitters placed inside a 3DPC due to the strong modification of the photonic density-of-states engendered by the 3DPC.

42 ENGINEERING↗
DOE OSTI2014

Parallel Heuristics for Scalable Community Detection

Community detection has become a fundamental operation in numerous graph-theoretic applications. It is used to reveal natural divisions that exist within real world networks without imposing prior size or cardinality constraints on the set of communities. Despite its potential for application, there is only limited support for community detection on large-scale parallel computers, largely owing to the irregular and inherently sequential nature of the underlying heuristics. In this paper, we present parallelization heuristics for fast community detection using the Louvain method as the serial template. The Louvain method is an iterative heuristic for modularity optimization. Originally developed by Blondel et al. in 2008, the method has become increasingly popular owing to its ability to detect high modularity community partitions in a fast and memory-efficient manner. However, the method is also inherently sequential, thereby limiting its scalability to problems that can be solved on desktops. Here, we observe certain key properties of this method that present challenges for its parallelization, and consequently propose multiple heuristics that are designed to break the sequential barrier. Our heuristics are agnostic to the underlying parallel architecture. For evaluation purposes, we implemented our heuristics on shared memory (OpenMP) and distributed memory (MapReduce-MPI) machines, and tested them over real world graphs derived from multiple application domains (internet, biological, natural language processing). Experimental results demonstrate the ability of our heuristics to converge to high modularity solutions comparable to those output by the serial algorithm in nearly the same number of iterations, while also drastically reducing time to solution.

clustering, graph algorithms↗
DOE OSTI2014

Scalable quantum computer architecture with coupled donor-quantum dot qubits

A quantum bit computing architecture includes a plurality of single spin memory donor atoms embedded in a semiconductor layer, a plurality of quantum dots arranged with the semiconductor layer and aligned with the donor atoms, wherein a first voltage applied across at least one pair of the aligned quantum dot and donor atom controls a donor-quantum dot coupling. A method of performing quantum computing in a scalable architecture quantum computing apparatus includes arranging a pattern of single spin memory donor atoms in a semiconductor layer, forming a plurality of quantum dots arranged with the semiconductor layer and aligned with the donor atoms, applying a first voltage across at least one aligned pair of a quantum dot and donor atom to control a donor-quantum dot coupling, and applying a second voltage between one or more quantum dots to control a Heisenberg exchange J coupling between quantum dots and to cause transport of a single spin polarized electron between quantum dots.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Focal plane array with modular pixel array components for scalability

A modular, scalable focal plane array is provided as an array of integrated circuit dice, wherein each die includes a given amount of modular pixel array circuitry. The array of dice effectively multiplies the amount of modular pixel array circuitry to produce a larger pixel array without increasing die size. Desired pixel pitch across the enlarged pixel array is preserved by forming die stacks with each pixel array circuitry die stacked on a separate die that contains the corresponding signal processing circuitry. Techniques for die stack interconnections and die stack placement are implemented to ensure that the desired pixel pitch is preserved across the enlarged pixel array.

42 ENGINEERING↗
DOE OSTI2014

k-merSNP discovery: Software for alignment-and reference-free scalable SNP discovery, phylogenetics, and annotation for hundreds of microbial genomes

With the flood of whole genome finished and draft microbial sequences, we need faster, more scalable bioinformatics tools for sequence comparison. An algorithm is described to find single nucleotide polymorphisms (SNPs) in whole genome data. It scales to hundreds of bacterial or viral genomes, and can be used for finished and/or draft genomes available as unassembled contigs or raw, unassembled reads. The method is fast to compute, finding SNPs and building a SNP phylogeny in minutes to hours, depending on the size and diversity of the input sequences. The SNP-based trees that result are consistent with known taxonomy and trees determined in other studies. The approach we describe can handle many gigabases of sequence in a single run. The algorithm is based on k-mer analysis.

Source record↗
DOE OSTI2014

Scripts for Scalable Monitoring of Parallel Filesystem Infrastructure

Scripts for scalable monitoring of parallel filesystem infrastructure provide frameworks for monitoring the health of block storage arrays and large InfiniBand fabrics. The block storage framework uses Python multiprocessing to within scale the number monitored arrays to scale with the number of processors in the system. This enables live monitoring of HPC-scale filesystem with 10-50 storage arrays. For InfiniBand monitoring, there are scripts included that monitor InfiniBand health of each host along with visualization tools for mapping the topology of complex fabric topologies.

Caldwell, Blake↗
DOE OSTI2015

pcircle - A Suite of Scalable Parallel File System Tools

Most of the software related to file system are written for conventional local file system, they are serialized and can't take advantage of the benefit of a large scale parallel file system. "pcircle" software builds on top of ubiquitous MPI in cluster computing environment and "work-stealing" pattern to provide a scalable, high-performance suite of file system tools. In particular - it implemented parallel data copy and parallel data checksumming, with advanced features such as async progress report, checkpoint and restart, as well as integrity checking.

WANG, FEIYI↗
DOE OSTI2016

A Reasoning And Hypothesis-Generation Framework Based On Scalable Graph Analytics

Finding actionable insights from data has always been difficult. As the scale and forms of data increase tremendously, the task of finding value becomes even more challenging. Data scientists at Oak Ridge National Laboratory are leveraging unique leadership infrastructure (e.g. Urika-XA and Urika-GD appliances) to develop scalable algorithms for semantic, logical and statistical reasoning with unstructured Big Data. We present the deployment of such a framework called ORiGAMI (Oak Ridge Graph Analytics for Medical Innovations) on the National Library of Medicine s SEMANTIC Medline (archive of medical knowledge since 1994). Medline contains over 70 million knowledge nuggets published in 23.5 million papers in medical literature with thousands more added daily. ORiGAMI is available as an open-science medical hypothesis generation tool - both as a web-service and an application programming interface (API) at http://hypothesis.ornl.gov . Since becoming an online service, ORIGAMI has enabled clinical subject-matter experts to: (i) discover the relationship between beta-blocker treatment and diabetic retinopathy; (ii) hypothesize that xylene is an environmental cancer-causing carcinogen and (iii) aid doctors with diagnosis of challenging cases when rare diseases manifest with common symptoms. In 2015, ORiGAMI was featured in the Historical Clinical Pathological Conference in Baltimore as a demonstration of artificial intelligence to medicine, IEEE/ACM Supercomputing and recognized as a Centennial Showcase Exhibit at the Radiological Society of North America (RSNA) Conference in Chicago. The final paper will describe the workflow built for the Cray Urika-XA and Urika-GD appliances that is able to reason with the knowledge of every published medical paper every time a clinical researcher uses the tool.

graph analytics↗
DOE OSTI2016

Low-cost scalable quartz crystal microbalance array for environmental sensing

Proliferation of environmental sensors for internet of things (IoT) applications has increased the need for low-cost platforms capable of accommodating multiple sensors. Quartz crystal microbalance (QCM) crystals coated with nanometer-thin sensor films are suitable for use in high-resolution (~1 ng) selective gas sensor applications. We demonstrate a scalable array for measuring frequency response of six QCM sensors controlled by low-cost Arduino microcontrollers and a USB multiplexer. Gas pulses and data acquisition were controlled by a LabVIEW user interface. We test the sensor array by measuring the frequency shift of crystals coated with different compositions of polymer composites based on poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) while films are exposed to water vapor and oxygen inside a controlled environmental chamber. Our sensor array exhibits comparable performance to that of a commercial QCM system, while enabling high-throughput 6 QCM testing for under $1,000. We use deep neural network structures to process sensor response and demonstrate that the QCM array is suitable for gas sensing, environmental monitoring, and electronic-nose applications.

Anazagasty, Cristain↗
DOE OSTI2016

An analysis of image storage systems for scalable training of deep neural networks

This study presents a principled empirical evaluation of image storage systems for training deep neural networks. We employ the Caffe deep learning framework to train neural network models for three different data sets, MNIST, CIFAR-10, and ImageNet. While training the models, we evaluate five different options to retrieve training image data: (1) PNG-formatted image files on local file system; (2) pushing pixel arrays from image files into a single HDF5 file on local file system; (3) in-memory arrays to hold the pixel arrays in Python and C++; (4) loading the training data into LevelDB, a log-structured merge tree based key-value storage; and (5) loading the training data into LMDB, a B+tree based key-value storage. The experimental results quantitatively highlight the disadvantage of using normal image files on local file systems to train deep neural networks and demonstrate reliable performance with key-value storage based storage systems. When training a model on the ImageNet dataset, the image file option was more than 17 times slower than the key-value storage option. Along with measurements on training time, this study provides in-depth analysis on the cause of performance advantages/disadvantages of each back-end to train deep neural networks. We envision the provided measurements and analysis will shed light on the optimal way to architect systems for training neural networks in a scalable manner.

Lim, Seung-Hwan↗
DOE OSTI2014

Scalability, Scintillation Readout and Charge Drift in a Kilogram Scale Solid Xenon Particle Detector

We report a demonstration of the scalability of optically transparent xenon in the solid phase for use as a particle detector above a kilogram scale. We employ a liquid nitrogen cooled cryostat combined with a xenon purification and chiller system to measure the scintillation light output and electron drift speed from both the solid and liquid phases of xenon. Scintillation light output from sealed radioactive sources is measured by a set of high quantum efficiency photomultiplier tubes suitable for cryogenic applications. We observed a reduced amount of photons in solid phase compared to that in liquid phase. We used a conventional time projection chamber system to measure the electron drift time in a kilogram of solid xenon and observed faster electron drift speed in the solid phase xenon compared to that in the liquid phase.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Particle In Cell Scalable Application Resource (PICSAR) v2

PICSAR, for Particle-In-Cell Scalable Application Resource, is a high-performance Particle-in-Cell library developed in Fortran 90 and Python. This library was first developed to be coupled with WARP and provide efficient subroutines optimized for Many-Integrated Core (MIC) architectures such as the forthcoming system Cori Phase II that will be equipped with Intel Xeon Phi KNL processors. It is a compact self-contained proxy that adequately portrays the computational loads and dataflow of the more complex WARP code but can also be used by other PIC codes. PICSAR also contains a Fortran stand-alone Particle-In-Cell "mini-app" that includes the key functionalities of a PIC code. This stand-alone code is used for testing implementation performances and to do some benchmarks.

VINCENTI, HENRI↗
DOE OSTI2017

Towards Scalable Deep Learning via I/O Analysis and Optimization

Deep learning systems have been growing in prominence as a way to automatically characterize objects, trends, and anomalies. Given the importance of deep learning systems, researchers have been investigating techniques to optimize such systems. An area of particular interest has been using large supercomputing systems to quickly generate effective deep learning networks: a phase often referred to as “training” of the deep learning neural network. As we scale existing deep learning frameworks—such as Caffe—on these large supercomputing systems, we notice that the parallelism can help improve the computation tremendously, leaving data I/O as the major bottleneck limiting the overall system scalability. In this paper, we first present a detailed analysis of the performance bottlenecks of Caffe on large supercomputing systems. Our analysis shows that the I/O subsystem of Caffe—LMDB—relies on memory-mapped I/O to access its database, which can be highly inefficient on large-scale systems because of its interaction with the process scheduling system and the network-based parallel filesystem. Based on this analysis, we then present LMDBIO, our optimized I/O plugin for Caffe that takes into account the data access pattern of Caffe in order to vastly improve I/O performance. Our experimental results show that LMDBIO can improve the overall execution time of Caffe by nearly 20-fold in some cases.

Pumma, Sarunya↗
DOE OSTI2018

Flexible and Scalable Data-Acquisition Using the artdaq Toolkit

The Real-Time Systems Engineering Department of the Scientific Computing Division at Fermilab is developing a flexible, scalable, and powerful data-acquisition (DAQ) toolkit which serves the needs of experiments from bench-top hardware tests to large high-energy physics experiments. The toolkit provides data transport and event building capabilities with the option for experimenters to inject art analysis code at key points in the DAQ for filtering or monitoring. The toolkit also provides configuration management, run control, and low-level hardware communication utilities. Firmware blocks for several commercial data acquisition boards are provided, allowing experimenters to approach the DAQ from a high level. A fully-functional DAQ "solution" of the toolkit is provided in otsdaq, sacrificing some flexibility in favor of being ready-to-use. artdaq is being used for several current and upcoming experiments, and will continue to be refined and expanded for use in the next generation of neutrino and muon experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Simplifying Energy Efficiency for Homeowners - Results from a California Utility's Pilot Study of a Scalable Solution to Assess Retrofit and Solar Opportunities: Preprint

For homeowners, typical choices to reduce net energy use in their homes include: upgrading their home with a tailored efficiency retrofit or investing in roof-top solar. Determining the unique combination of these options that will provide the most cost-effective solution requires analysis for each home. This analysis needs to be presented in a simple and actionable way to homeowners. The solution should also be easily scalable to thousands of homes to enable utilities to better target efficiency program options and support solar installations. Sacramento Municipal Utility District (SMUD) piloted a new tool developed by Vistar Energy called XeroHome that automates the assessment of energy efficiency and solar opportunities in existing homes. XeroHome demonstrates an approach to using building energy modeling, energy use data analytics and homeowner engagement to identify and target energy efficiency opportunities at scale. XeroHome uses automation to build an EnergyPlus model for each home, based on information from public data sources such as property assessment records, building permit records, aerial/satellite photography, and template models derived from ResStock. Utilizing cloud computing, machine learning and optimization algorithms, energy upgrade packages are ranked by cost-effectiveness for each home and presented over an interactive web-portal. This paper discusses SMUD's pilot project using XeroHome to conduct analysis for 1,500 existing homes in Sacramento, CA. Development of XeroHome's advanced analytics capabilities are discussed in the context of applicability for utility programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Scalable PETSc Implementation of the Three-Phase Unbalanced AC Power Flow Solver

A revolutionary transition of energy infrastructure, spanning across supply and demand sectors, is happening all around the world. Integration of renewable energy sources into our ever-complex power distribution networks is central to the infrastructure planning of the future. One step towards achieving this goal is to solve unbalanced transmission and distribution systems. Traditional approaches and software tools are not suitable because they assume a completely balanced 3-phase system, hence a single-phase equivalent can be solved. Renewable energy such as wind and solar power are intermittent and can pose a challenge when integrating into an already unbalanced power grid. Furthermore, these power grids can be quite large at scale consisting of potentially thousands of buses, which can be computationally demanding. This poster presents preliminary work on designing a robust and scalable 3-phase unbalanced AC power flow solver. The proposed power flow solver will eventually be built on top of the PETSc library and utilizes state-of-the-art parallel data structures like the DMNetwork as well as iterative solvers and preconditioners.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗