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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Adsorption and Reaction of Methanol on Anatase TiO2(101) Single Crystals and Faceted Nanoparticles

Anatase TiO2 is used extensively in a wide range of heterogeneous and photocatalysis processes. As such, understanding its interactions with prototypical organics such as methanol is of high importance. In this study, we employ a cross-disciplinary approach and study adsorption and dissociation of methanol on the (101) surfaces of anatase TiO2 single crystals (SCs) and faceted nanoparticles (F-NPs). While scanning tunneling microscopy was used to follow the spatial distribution of adsorbed methanol molecules and methoxy and hydroxy species on SC TiO2(101), comparative temperature programmed desorption and infrared reflection absorption spectroscopy measurements were performed on both SC and F-NP TiO2(101). We find that adsorption of molecular methanol at 80 K on SC TiO2(101) leads to the formation of chains along the titania rows. These chains are metastable and fall apart upon annealing due to the repulsion of neighboring molecules. Further, we find that on SC TiO2(101) methanol deprotonates to produce neighboring methoxy and hydroxyl groups following the annealing of high coverages to room temperature (RT). The coverage of methoxy and hydroxyl groups can be increased with repeated adsorption and annealing cycles, and the annealing above room temperature leads to the recombinative desorption of methanol. On F-NP TiO2(101) surfaces, we find that methanol adsorbs both molecularly and dissociatively at RT. With increasing temperature, molecularly adsorbed methanol gradually converts to methoxy. Therefore, parallel behavior in adsorption and surface reaction is found on both SC and F-NP TiO2(101). An added layer of complexity for the latter is that Ti-OH groups on the nanoparticle surfaces, likely associated with defects, also play an important role in methanol adsorption and surface reaction.

oxides, alcohols, adsorption, scanning, tunneling,↗
DOE OSTI2023

Fission with Exotic Nuclei (Abbreviated Report)

Nuclear fission is a key mechanism involved in the synthesis of heavy elements in the Cosmos and is the primary explanation for the stability of superheavy elements. Nevertheless, our knowledge of fission remains extremely fragmented. Most experiments have been conducted only on a tiny number of stable actinide nuclei and are often incomplete, leading to gaps in our basic understanding of the process. For many radioactive isotopes, basic fission data such as the charge or mass distribution of the fragments is unknown. These gaps cannot always be filled by simulation alone. Common fission models contain too many free parameters and lack predictive power. In contrast, the fundamental theory of fission under development at LLNL is much more predictive, but its current computational cost is too high to be used extensively for data evaluations. A unique window of opportunity to resolve these limitations has recently opened: the U.S. nuclear science community is ramping up major experimental programs at the Facility for Rare Isotope Beams (FRIB, the DOE flagship facility in low-energy nuclear science), and techniques from machine learning have shown great potential to simplify the use of a fundamental, quantum-mechanical theory of fission. This project has two components. On the experimental side, we acquired and deployed at the HIGS facility a new dual Frisch-Grid ionization chamber to measure correlated fragment-mass, kinetic energy, and angular distributions of fission fragments from induced fission. This new device was used to perform measurements of charge, mass and total kinetic energy of fission fragments in the photofission of 238 U and eight gamma-ray beam energies between 6.2 and 13 MeV, which allowed extracting high-precision independent yields for this reaction. The device was also used to perform measurements of the same quantities in the neutron-induced fission of 234 U with monoenergetic beams of energy between 5 and 8 MeV. In parallel, we collaborated with a team at Commissariat à l’énergie atomique et aux énergies alternatives (CEA) to perform a series of measurements of fission yields in inverse kinematics for the two isotopes of 236 U and 240 Pu. The experiment took place at the Grand Accélérateur National d’Ions Lourds in France in June 2023. The deployment of the VAMOS spectrometer with a new array called PISTA allowed determining the excitation energy of the fissioning system within 1 Mega-electronvolts. The second component of the project involved using deep neural networks to build fast and reliable emulators of our current fission models. In an invited paper published in Frontier in Physics, we showed that autoencoders could successfully compress nuclear wavefunctions in nuclear density functional theory. We achieved a dimensionality reduction of the order of two orders of magnitude while keeping the error in the total energy to less than 0.01%. In a second paper submitted to Physical Review Letters in June 2023 with our collaborators at CEA, we showed that variational autoencoders can learn the collective degrees of freedom driving the fission process.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Novel Geometric Operations for Linear Programming

This report summarizes the work performed under the project "Linear Programming in Strongly Polynomial Time." Linear programming (LP) is a classic combinatorial optimization problem heavily used directly and as an enabling subroutine in integer programming (IP). Specifically IP is the same as LP except that some solution variables must take integer values (e.g. to represent yes/no decisions). Together LP and IP have many applications in resource allocation including general logistics, and infrastructure design and vulnerability analysis. The project was motivated by the PI's recent success developing methods to efficiently sample Voronoi vertices (essentially finding nearest neighbors in high-dimensional point sets) in arbitrary dimension. His method seems applicable to exploring the high-dimensional convex feasible space of an LP problem. Although the project did not provably find a strongly-polynomial algorithm, it explored multiple algorithm classes. The new medial simplex algorithms may still lead to solvers with improved provable complexity. We describe medial simplex algorithms and some relevant structural/complexity results. We also designed a novel parallel LP algorithm based on our geometric insights and implemented it in the Spoke-LP code. A major part of the computational step is many independent vector dot products. Our parallel algorithm distributes the problem constraints across processors. Current commercial and high-quality free LP solvers require all problem details to fit onto a single processor or multicore. Our new algorithm might enable the solution of problems too large for any current LP solvers. We describe our new algorithm, give preliminary proof-of-concept experiments, and describe a new generator for arbitrarily large LP instances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Toward A High-Performance Emulation Platform for Brain-Inspired Intelligent Systems Exploring Dataflow-Based Execution Model and Beyond

Brain-inspired computing is a novel computing technology based on neural morphological engineering, which draws lessons from methods of human brain information processing and storage. Combining with the high-performance computing (HPC) platform, they constitute the foundation of general artificial intelligence. However, current brain HPC platforms generally suffer from slow speed, poor scalability, and high energy consumption, which severely restrain its potential and circumscribe the development of general artificial intelligence. The dataflow model was first proposed in the 1970s, providing a novel idea for the development of HPC. In addition, the dataflow model shares similar information processing mechanisms with human's neural system, which makes dataflow models naturally suit the emulation of brain-inspired computing. Based on the contemporary progress of the dataflow model, the Codelet model was proposed. Through a fine-grained asynchronous program execution and resource allocation, the Codelet model successfully realized the distributed computing on the heterogeneous system, effectively improved the computing power and speed, and open up a new path to overcome the shortcomings of the existing high-performance computing technology. We propose a dataflow-based emulation platform, aiming at providing high-performance computing technology support for general brain-inspired intelligent system, as well as using characteristics of dataflow models to fully explore the potential of brain-inspired intelligence. As an example, we will select a convolutional neural network (LeNet5) that already has a spectacular user base to initially verify the superiority and feasibility of our proposal.

brain-inspired intelligent system↗
DOE OSTI2023

Implementation of Distributed Memory Computing in MOSAIC to Enable Large 3D Simulations of Irradiated Concrete

The concrete biological shield (CBS) of light-water reactors protects workers and the surrounding environment by absorbing neutron and gamma irradiation emitted from the reactor core. The radiation dose increases with the CBS’s operational time and, in the long term, becomes significant enough to raise the question of irradiation effects on concrete—and particularly on the structural integrity of the CBS. Irradiation-induced damage has been identified as one of the main degradation mechanisms in the CBS. Neutron radiation causes the swelling of aggregate-forming minerals at different rates and amplitudes depending on the mineral’s nature. Silicate-bearing minerals such as quartz are particularly sensitive to neutron radiation and experience up to 17.8% volumetric expansion. Aggregates comprise several minerals with different orientations and are, therefore, subject to cracking as a result of mismatch strains. Additionally, the swelling of aggregates creates significant stresses in the surrounding cement paste matrix, which also results in crack formation. In parallel with the collection of characterization and irradiation test data, development of modeling and simulation tools for irradiated concrete is ongoing with the support of the US Department of Energy Office of Nuclear Energy’s Light Water Reactor Sustainability (LWRS) program. This effort resulted in the development and application of the fast-Fourier transform (FFT)–based code Microstructure-Oriented Scientific Analysis of Irradiated Concrete (MOSAIC) at Oak Ridge National Laboratory.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Integration Of PanDA Workload Management System With Supercomputers for ATLAS and Data Intensive Science

The.LHC, operating at CERN, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe. ATLAS, one of the largest collaborations ever assembled in the sciences, is at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, the ATLAS experiment is relying on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System for managing the workflow for all data processing on over 150 data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. While PanDA currently uses more than 250,000 cores with a peak performance of 0.3 petaFLOPS, LHC data taking runs require more resources than grid can possibly provide. To alleviate these challenges, LHC experiments are engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. We will describe a project aimed at integration of PanDA WMS with supercomputers in United States, in particular with Titan supercomputer at Oak Ridge Leadership Computing Facility. Current approach utilizes modified PanDA pilot framework for job submission to the supercomputers batch queues and local data management, with light-weight MPI wrappers to run single threaded workloads in parallel on LCFs multi-core worker nodes. This implementation was tested with a variety of Monte-Carlo workloads on several supercomputing platforms for ALICE and ATLAS experiments and it is in full pro duction for the ATLAS since September 2015. We will present our current accomplishments with running PanDA at supercomputers and demonstrate our ability to use PanDA as a portal independent of the computing facilities infrastructure for High Energy and Nuclear Physics as well as other data-intensive science applications, such as bioinformatics and astro-particle physics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Axially-deformed solution of the Skyrme-Hartree-Fock-Bogoliubov equations using the transformed harmonic oscillator basis (IV) HFBTHO (v4.0): A new version of the program

We describe the new version 4.0 of the code HFBTHO that solves the nuclear Hartree-Fock-Bogoliubov problem by using the deformed harmonic oscillator basis in cylindrical coordinates. In the new version, we have implemented the restoration of rotational, particle number, and reflection symmetry for even-even nuclei. The restoration of rotational symmetry does not require using bases closed under rotation. Furthermore, we added the SeaLL1 functional and improved the calculation of the Coulomb potential. Finally, we refactored the code to facilitate maintenance and future developments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Fault Tolerant Frequent Pattern Mining

FP-Growth algorithm is a Frequent Pattern Mining (FPM) algorithm that has been extensively used to study correlations and patterns in large scale datasets. While several researchers have designed distributed memory FP-Growth algorithms, it is pivotal to consider fault tolerant FP-Growth, which can address the increasing fault rates in large scale systems. In this work, we propose a novel parallel, algorithm-level fault-tolerant FP-Growth algorithm. We leverage algorithmic properties and MPI advanced features to guarantee an O(1) space complexity, achieved by using the dataset memory space itself for checkpointing. We also propose a recovery algorithm that can use in-memory and disk-based checkpointing, though in many cases the recovery can be completed without any disk access, and incurring no memory overhead for checkpointing. We evaluate our FT algorithm on a large scale InfiniBand cluster with several large datasets using up to 2K cores. Our evaluation demonstrates excellent efficiency for checkpointing and recovery in comparison to the disk-based approach. We have also observed 20x average speed-up in comparison to Spark, establishing that a well designed algorithm can easily outperform a solution based on a general fault-tolerant programming model.

Shohdy, Sameh↗
DOE OSTI2015

Scalable parallel distance field construction for large-scale applications

Computing distance fields is fundamental to many scientific and engineering applications. Distance fields can be used to direct analysis and reduce data. In this paper, we present a highly scalable method for computing 3D distance fields on massively parallel distributed-memory machines. Anew distributed spatial data structure, named parallel distance tree, is introduced to manage the level sets of data and facilitate surface tracking overtime, resulting in significantly reduced computation and communication costs for calculating the distance to the surface of interest from any spatial locations. Our method supports several data types and distance metrics from real-world applications. We demonstrate its efficiency and scalability on state-of-the-art supercomputers using both large-scale volume datasets and surface models. We also demonstrate in-situ distance field computation on dynamic turbulent flame surfaces for a petascale combustion simulation. In conclusion, our work greatly extends the usability of distance fields for demanding applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Three wide planetary-mass companions to FW Tau, ROXs 12, and ROXs 42B

We report the discovery of three planetary-mass companions (M = 6-20 M {sub Jup}) in wide orbits (ρ ∼ 150-300 AU) around the young stars FW Tau (Taurus-Auriga), ROXs 12 (Ophiuchus), and ROXs 42B (Ophiuchus). All three wide planetary-mass companions (PMCs) were reported as candidate companions in previous binary survey programs, but then were neglected for >10 yr. We therefore obtained followup observations that demonstrate that each candidate is comoving with its host star. Based on the absolute M{sub K{sup ′}} magnitudes, we infer masses (from hot-start evolutionary models) and projected separations of 10 ± 4 M {sub Jup} and 330 ± 30 AU for FW Tau b, 16 ± 4 M {sub Jup} and 210 ± 20 AU for ROXs 12, and 10 ± 4 M {sub Jup} and 140 ± 10 AU for ROXs 42B b. We also present similar observations for 10 other candidates that show that they are unassociated field stars, as well as multicolor JHK'L' near-infrared photometry for our new PMCs and for five previously identified substellar or planetary-mass companions. The near-infrared photometry for our sample of eight known and new companions generally parallels the properties of free-floating, low-mass brown dwarfs in these star-forming regions. However, five of the seven objects with M < 30 M {sub Jup} are redder in K' – L' than the distribution of young free-floating counterparts of similar J – K' color. We speculate that this distinction could indicate a structural difference in circumplanetary disks, perhaps tied to higher disk mass since at least two of the objects in our sample are known to be accreting more vigorously than typical free-floating counterparts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

The Kokkos Ecosystem [Brief]

In 2016/2017, the field of High-Performance Computing (HPC) entered a new era driven by fundamental physics challenges to produce ever more energy and cost-efficient processors. Since the convergence on the Message-Passing Interface (MPI) standard in the mid-1990s, application developers enjoyed a seemingly static view of the underlying machine — that of a distributed collection of homogeneous nodes executing in collaboration. However, after almost two decades of dominance, the sole use of MPI to derive parallelism acted as a limiter to improved future performance. While MPI is widely expected to continue to function as the basic mechanism for communication between compute nodes for the immediate future, additional parallelism is required on the computing node itself if high performance and efficiency goals are to be realized. When reviewing the architectures of the top HPC systems today, the change in paradigm is clear: the compute nodes of the leading machines in the world are either powered by many-core chips with a few dozen cores each, or use heterogeneous designs, where traditional CPUs marshal work to massively parallel compute accelerators which has as many as 200,000 processing threads in flight simultaneously. Complicating matters further for application developers, each processor vendor has its own preferred way of writing code for their architecture.The Kokkos EcoSystem was released by Sandia in 2017 to address this new era in HPC system design by providing a vendor independent performance portable programming system for scientific, engineering, and mathematical software applications written in the C++ programming language. Using Kokkos, application developers can be more productive because they will not have to create and maintain separate versions of their software for each architecture, nor will they have to be experts in each architecture's peculiar requirements. Instead, they will have a single method of programming for the diverse set of modern HPC architectures. While Kokkos started in 2011 as a programming model only, it soon became clear that complex applications needed more. It is also critical to have a portable mathematical functions and developers need tools to debug their applications, gain insight into the performance characteristics of their codes and tune algorithm performance parameters through automated processes. The Kokkos EcoSystem addresses those needs through its three main components: the Kokkos Core programming model, the Kokkos Kernels math library, and the Kokkos Tools project.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Inter-digital capacitive sensor for evaluating cable jacket and insulation aging

An inter-digital capacitive (TDC) sensor has previously been used to measure dielectric properties of cable insulation polymer material when placed in direct contact with the insulation. Often cable insulation is covered by a polymer jacket. The dielectric properties of many cable jacket and insulation polymers are known to change due to thermal and radiation exposure-related damage. These dielectric properties frequently track with other measures of cable aging, such as tensile elongation-at-break and indenter modulus that have been broadly established as cable insulation polymer assessment methods. The external jacket of a cable is likely to have a different permittivity from the underlying insulation, and frequently the jacket material exhibits more severe damage than the insulation material due to environmental exposure. Because the jacket serves primarily to guard the cable during installation, as long as the underlying insulation condition is acceptable, the jacket condition is relatively unimportant in service. As part of a continuing program to develop and evaluate nondestructive examination methods that may be applied to cable condition assessment, a set of tools has been developed including (1) a parallel-plate sensor to directly measure the permittivity spectrum of flat sheet material and (2) an TDC and fixture to measure the effect of cable polymer dielectric property change on the sensor response. The TDC consists of two fork-like electrodes facing each other with the fork tines interspersed and separated by a small gap. The electrodes are printed on one side of a flexible substrate that can be conformed to the surface of a cylindrical cable, with tines parallel to the cable axis. The electrodes are connected to a broad-frequency-spectral impedance meter that senses the capacitance between the narrowly gapped electrode tines. This capacitance is known to vary as a function of the permittivity of any material in close proximity to the electrodes. By finite element modeling (FEM) and experimentation, this study investigates the effect of tine spacing and other design parameters associated with the TDC on the voltage (potential) distribution and electric field depth of penetration. The TDC measurement of an unshielded ethylene-propylene rubber (EPR)-insulated cable is shown to track with the degree of aging and quantities obtained by established methods. For jacketed cable systems, the TDC response is dominated by the jacket but, by analyzing measurements from TDC sensors with different depths-of-field penetration into the cable under test, the influence of the chlorinated polyethylene (CPE) cable jacket material degradation can be separated from an assessment of the cable insulation thereby enabling assessment of the insulation beneath/through the jacket.

Cable NDE, Interdigital Capacitance, Cable test↗
DOE OSTI2023

A numerical solver for investigating the space charge effect on the electric field in liquid argon time projection chambers

This paper reports the development of a numerical solver aimed to simulate the interaction between the space charge (i.e. ions) distribution and the electric field in liquid argon time projection chamber (LArTPC) detectors. The ion transport equation is solved by a time-accurate, cell-centered finite volume method and the electric potential equation by a continuous finite element method. The electric potential equation updates the electric field which provides the drift velocity to the ion transport equation. The ion transport equation updates the space charge density distribution which appears as the source term in the electric potential equation. The interaction between the space charge distribution and the electric field is numerically simulated within each physical time step. The convective velocity in the ion transport equation can include the background flow velocity in addition to the electric drift velocity. The numerical solver has been parallelized using the Message Passing Interface (MPI) library. Numerical tests show and verify the capability and accuracy of the current numerical solver. It is planned that the developed numerical solver, together with a Computational Fluid Dynamics (CFD) package which provides the flow velocity field, can be used to investigate the space charge effect on the electric field in large-scale particle detectors.

42 ENGINEERING↗
DOE OSTI2020

The MPACT 2020 Milestone: Safeguards and Security by Design of Future Nuclear Fuel Cycle Facilities.

The Materials Protection, Accounting, and Control Technologies (MPACT) campaign, within the U.S. Department of Energy Office of Nuclear Energy, has developed a Virtual Facility Distributed Test Bed for safeguards and security design for future nuclear fuel cycle facilities. The purpose of the Virtual Test Bed is to bring together experimental and modeling capabilities across the U.S. national laboratory and university complex to provide a one-stop-shop for advanced Safeguards and Security by Design (SSBD). Experimental testing alone of safeguards and security technologies would be cost prohibitive, but testbeds and laboratory processing facilities with safeguards measurement opportunities, coupled with modeling and simulation, provide the ability to generate modern, efficient safeguards and security systems for new facilities. This Virtual Test Bed concept has been demonstrated using a generic electrochemical reprocessing facility as an example, but the concept can be extended to other facilities. While much of the recent work in the MPACT program has focused on electrochemical safeguards and security technologies, the laboratory capabilities have been applied to other facilities in the past (including aqueous reprocessing, fuel fabrication, and molten salt reactors as examples). This paper provides an overview of the Virtual Test Bed concept, a description of the design process, and a baseline safeguards and security design for the example facility. Parallel papers in this issue go into more detail on the various technologies, experimental testing, modeling capabilities, and performance testing.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2019

A Scalable Online Monitoring System Based on Elasticsearch for Distributed Data Acquisition in Cms

The part of the CMS Data Acquisition (DAQ) system responsible for data readout and event building is a complex network of interdependent distributed applications. To ensure successful data taking, these programs have to be constantly monitored in order to facilitate the timeliness of necessary corrections in case of any deviation from specified behaviour. A large number of diverse monitoring data samples are periodically collected from multiple sources across the network. Monitoring data are kept in memory for online operations and optionally stored on disk for post-mortem analysis. We present a generic, reusable solution based on an open source NoSQL database, Elasticsearch, which is fully compatible and non-intrusive with respect to the existing system. The motivation is to benefit from an off the-shelf software to facilitate the development, maintenance and support efforts. Elasticsearch provides failover and data redundancy capabilities as well as a programming language independent JSON-over-HTTP interface. The possibility of horizontal scaling matches the requirements of a DAQ monitoring system. The data load from all sources is balanced by redistribution over an Elasticsearch cluster that can be hosted on a computer cloud. In order to achieve the necessary robustness and to validate the scalability of the approach the above monitoring solution currently runs in parallel with an existing in-house developed DAQ monitoring system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

An Experimental Investigation of Distributed Acoustic Sensing (DAS) on Lake Ice

A vibration-sensitive, Distributed Acoustic Sensor (DAS) array, using fiber-optic cables, was deployed in a triangularly shaped geometry on the frozen surface of Lake Mendota in Madison, Wisconsin, USA. The purpose of the array and testing program was to analyze the DAS response and to utilize the high spatial density of the distributed array for system response characterization in a well-constrained, small, surface array. A geophone array was also deployed to provide a reference system. The design of the array allowed us to assess the response of DAS with respect to distance from the seismic sources, the degradation of the response with length of the cable, the directivity of the fiber response with respect of the direction of the particle motion, and the quality of the signal with respect to cable type. The DAS array was examined for different cable constructions and orientations relative to the source propagation direction. Tight-buffered and loose-tube fiber-optic cable constructions were used, with both having good signal responses when well-coupled to the ice. In general, the tight-buffered cable was better suited for DAS applications. Directional sensitivity of the DAS was also inspected for several directions of wave propagation and particle motion. The results showed that the strongest DAS signals were recorded when the direction of the fiber was oriented parallel to the direction of particle motion. Lastly, the DAS and geophone data sets were examined together to qualitatively determine, in conjunction with established DAS best practices, how the high spatial density offered by DAS could improve results over traditional point sensor arrays in certain situations.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Scalable, Economical Fabrication Processes for Ultra-Compact Warm-White LEDs

Conventional warm-white LED component fabrication consists of a large number of sequential steps which are required to incorporate electrical, mechanical, and optical functionality into the component. Each of these steps presents cost and yield challenges which multiply throughout the entire process. Although there has been significant progress in LED fabrication over the last decade, significant advances are needed to enable further reductions in cost per lumen while not sacrificing efficacy or color quality. Cree conducted a focused 18-month program to develop a new low-cost, high-efficiency light emitting diode (LED) architecture enabled by novel large-area parallel processing technologies, reduced number of fabrication steps, and minimized raw materials use. This new scheme is expected to enable ultra-compact LED components exhibiting simultaneously high efficacy and high color quality. By the end of the program, Cree fabricated warm-white LEDs with a room-temperature “instant on” efficacy of >135 lm/W at ~3500K and 90 CRI (when driven at the DOE baseline current density of 35 A/cm2). Cree modified the conventional LED fabrication process flow in a manner that is expected to translate into simultaneously high throughput and yield for ultra-compact packages. Building on its deep expertise in LED wafer fabrication, Cree developed these ultra-compact LEDs to have no compromises in color quality or efficacy compared to their conventional counterparts. Despite their very small size, the LEDs will also be robustly electrically integrated into luminaire systems with the same attach yield as conventional packages. The versatility of the prototype high-efficacy LED architecture will likely benefit solid-state lighting (SSL) luminaire platforms ranging from bulbs to troffers. We anticipate that the prototype LEDs will particularly benefit luminaires with large numbers of distributed compact packages, such as linear and area luminaires (e.g. troffers). The fraction of total SSL luminaire cost made up by the LEDs themselves has steadily fallen over the past several years, but can still make up 30% or more of the bill of materials; the new LED design will radically lower this proportion. Ultra-compact, highly efficient LEDs with optimal distribution in the system will further benefit luminaire materials and assembly costs by reducing the complexity and volume of thermal management and optical subsystems.

42 ENGINEERING↗
DOE OSTI2016

INTEGRATION OF PANDA WORKLOAD MANAGEMENT SYSTEM WITH SUPERCOMPUTERS

Abstract The Large Hadron Collider (LHC), operating at the international CERN Laboratory in Geneva, Switzerland, is leading Big Data driven scientific explorations. Experiments at the LHC explore the funda- mental nature of matter and the basic forces that shape our universe, and were recently credited for the dis- covery of a Higgs boson. ATLAS, one of the largest collaborations ever assembled in the sciences, is at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, the ATLAS experiment is relying on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System for managing the workflow for all data processing on over 140 data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data cen- ters are physically scattered all over the world. While PanDA currently uses more than 250000 cores with a peak performance of 0.3+ petaFLOPS, next LHC data taking runs will require more resources than Grid computing can possibly provide. To alleviate these challenges, LHC experiments are engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. We will describe a project aimed at integration of PanDA WMS with supercomputers in United States, Europe and Russia (in particular with Titan supercomputer at Oak Ridge Leadership Com- puting Facility (OLCF), Supercomputer at the National Research Center Kurchatov Institute , IT4 in Ostrava, and others). The current approach utilizes a modified PanDA pilot framework for job submission to the supercomputers batch queues and local data management, with light-weight MPI wrappers to run single- threaded workloads in parallel on Titan s multi-core worker nodes. This implementation was tested with a variety of Monte-Carlo workloads on several supercomputing platforms. We will present our current accom- plishments in running PanDA WMS at supercomputers and demonstrate our ability to use PanDA as a portal independent of the computing facility s infrastructure for High Energy and Nuclear Physics, as well as other data-intensive science applications, such as bioinformatics and astro-particle physics.

De, K↗