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At least 883 records · Page 49

Knowledge management: Role of the the Radiation Safety Information Computational Center (RSICC)

The Radiation Safety Information Computational Center (RSICC) at Oak Ridge National Laboratory (ORNL) is an information analysis center that collects, archives, evaluates, synthesizes and distributes information, data and codes that are used in various nuclear technology applications. RSICC retains more than 2,000 software packages that have been provided by code developers from various federal and international agencies. RSICC’s customers (scientists, engineers, and students from around the world) obtain access to such computing codes (source and/or executable versions) and processed nuclear data files to promote on-going research, to ensure nuclear and radiological safety, and to advance nuclear technology. The role of such information analysis centers is critical for supporting and sustaining nuclear education and training programs both domestically and internationally, as the majority of RSICC’s customers are students attending U.S. universities. Additionally, RSICC operates a secure CLOUD computing system to provide access to sensitive export-controlled modeling and simulation (M&S) tools that support both domestic and international activities. This presentation will provide a general review of RSICC’s activities, services, and systems that support knowledge management and education and training in the nuclear field.

Valentine, Timothy↗

Toward tuning the surface functionalization of small ceria nanoparticles

Understanding and controlling the performance of ceria nanoparticle (CNP) catalysts requires knowledge of the detailed structure and property of CNP surfaces and any attached functional groups. Here we report thermogravimetric analysis results showing that hydrothermally synthesized ∼30 nm CNPs are decorated with 12.9 hydroxyl groups per nm{sup 2} of CNP surface. Quantum mechanical calculations of the density and distribution of bound surface groups imply a scaling relationship for surface group density that balances formal charges in the functionalized CNP system. Computational results for CNPs with only hydroxyl surface groups yield a predicted density of bound hydroxyl groups for ∼30 nm CNPs that is ∼33% higher than measured densities. Quantitative agreement between predicted and measured hydroxyl surface densities is achieved when calculations consider CNPs with both –OH and –O{sub x} surface groups. For this more general treatment of CNP surface functionalizations, quantum mechanical calculations predict a range of stable surface group configurations that depend on the chemical potentials of O and H, and demonstrate the potential to tune CNP surface functionalizations by varying temperature and/or partial pressures of O{sub 2} and H{sub 2}O.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Fragment transition density method to calculate electronic coupling for excitation energy transfer

A general approach, the Fragment Transition Density (FTD) scheme, is introduced to estimate electronic coupling for excitation energy transfer in a molecular system. Within this method, the excitation energies and transition densities of the system are used to derive the coupling matrix element. The scheme allows one to treat systems where exciton donor and acceptor are close together and their exchange interaction and orbital overlap are significant. The FTD method can be applied in combination with any quantum mechanical approach to treat excited states of general nature including single-, double-, and higher excitations. Using FTD approach, we derive excitonic couplings for several systems computed with the CIS, TD DFT and MS-CASPT2 methods. In particular, it is shown that the estimated coupling values in DNA π-stacks are strongly affected by the short-range electronic interaction of adjacent nucleobases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

(Bi 0.2 Sb 0.8 ) 2 Te 3 based dynamic synapses with programmable spatio-temporal dynamics

Neuromorphic computing has recently emerged as a promising paradigm to overcome the von-Neumann bottleneck and enable orders of magnitude improvement in bandwidth and energy efficiency. However, existing complementary metal-oxide-semiconductor (CMOS) digital devices, the building block of our computing system, are fundamentally different from the analog synapses, the building block of the biological neural network—rendering the hardware implementation of the artificial neural networks (ANNs) not scalable in terms of area and power, with existing CMOS devices. In addition, the spatiotemporal dynamic, a crucial component for cognitive functions in the neural network, has been difficult to replicate with CMOS devices. Here, we present the first topological insulator (TI) based electrochemical synapse with programmable spatiotemporal dynamics, where long-term and short-term plasticity in the TI synapse are achieved through the charge transfer doping and ionic gating effects, respectively. We also demonstrate basic neuronal functions such as potentiation/depression and paired-pulse facilitation with high precision (>500 states per device), as well as a linear and symmetric weight update. We envision that the dynamic TI synapse, which shows promising scaling potential in terms of energy and speed, can lead to the hardware acceleration of truly neurorealistic ANNs with superior cognitive capabilities and excellent energy efficiency.

36 MATERIALS SCIENCE↗

Implementation of a real-time MSE system

Motional Stark effect polarimetry is a key diagnostic for plasma fusion research since its usage on PBX-M. The MSE diagnostic measures the radial magnetic pitch angle profile in a plasma from a neutral beam by observation of Stark split D-alpha emission from atoms excited by collision with ions and electrons in the plasma. The pitch angle measurement is used with equilibrium reconstruction codes to determine the q-profile for studies of plasma stability, confinement, and transport. Historically, the algorithm was used in a post-processing fashion. The goal of our work was to apply this method in real time and pass the results to the plasma control system computer for real-time equilibrium reconstruction and control.

Instruments & Instrumentation↗

An open source analysis framework for large-scale building energy modeling

Full integration of building energy modelling into the design and retrofit process has long been a goal of building scientists and practitioners. However, significant barriers still exist. Among them are the lack of available: (1) configurable technology stacks for performing both small- and large-scale analyses, (2) different classes of algorithms compatible with common design workflows, and (3) analysis tools for effectively visualizing large-scale simulation results. This article discusses the OpenStudio® Analysis Framework: a scalable analysis framework for building energy modelling that was developed to overcome the three barriers listed above. The framework is open-source and scalable to facilitate wider adoption and has a clearly defined application programming interface upon which other applications can be built. It runs on high-performance computing systems, within cloud infrastructure, and on laptops, and uses a common workflow to enable different classes of algorithms. Lessons learned from previous development efforts are also discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

An ATLAS distributed computing architecture for HL-LHC

The ATLAS collaboration started a process to understand the computing needs for the High Luminosity LHC era. Based on our best understanding of the computing model input parameters for the HL-LHC data taking conditions, results indicate the need for a larger amount of computational and storage resources with respect to the projection of constant yearly budget for computing in 2026. Filling the gap between the projection and the needs will be one of the challenges in preparation for HL-LHC. While the gains from improvements in offline software will play a crucial role in this process, a different model for data processing, management, access and bookkeeping should also be envisaged to optimise resource usage. In this contribution we will describe a straw man of this model, founded on basic principles such as single event level granularity for data processing and virtual data. We will explain how the current architecture will evolve adiabatically into the future distributed computing system, through the prototyping of building blocks that would be integrated in the production infrastructure as early as possible, so that specific use cases can be covered much earlier with respect to the HL-LHC time scale. We will also discuss how such system would adapt to and drive the evolution of the WLCG infrastructure in terms of facilities and services.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Evolution of CMS Workload Management Towards Multicore Job Support

The successful exploitation of multicore processor architectures is a key element of the LHC distributed computing system in the coming era of the LHC Run 2. High-pileup complex-collision events represent a challenge for the traditional sequential programming in terms of memory and processing time budget. The CMS data production and processing framework is introducing the parallel execution of the reconstruction and simulation algorithms to overcome these limitations. CMS plans to execute multicore jobs while still supporting singlecore processing for other tasks difficult to parallelize, such as user analysis. The CMS strategy for job management thus aims at integrating single and multicore job scheduling across the Grid. This is accomplished by employing multicore pilots with internal dynamic partitioning of the allocated resources, capable of running payloads of various core counts simultaneously. An extensive test programme has been conducted to enable multicore scheduling with the various local batch systems available at CMS sites, with the focus on the Tier-0 and Tier-1s, responsible during 2015 of the prompt data reconstruction. Scale tests have been run to analyse the performance of this scheduling strategy and ensure an efficient use of the distributed resources. This paper presents the evolution of the CMS job management and resource provisioning systems in order to support this hybrid scheduling model, as well as its deployment and performance tests, which will enable CMS to transition to a multicore production model for the second LHC run.

Perez-Calero Yzquierdo, A.↗

A continuous integration and web framework in support of the ATLAS publication process

The ATLAS collaboration defines methods, establishes procedures, and organises advisory groups to manage the publication processes of scientific papers, conference papers, and public notes. All stages are managed through web systems, computing programs, and tools that are designed and developed by the collaboration. A framework called FENCE is integrated into the CERN GitLab software repository, to automatically configure workspaces where each analysis can be documented by the analysis team and managed by the relevant coordinators. Continuous integration is used to guide the writers in applying consistent and correct formatting when preparing papers to be submitted to scientific journals. Additional software assures the correctness of other aspects of each paper, such as the lists of collaboration authors, funding agencies, and foundations. The framework and the workflow therein provide automatic and easy support to the researchers and facilitates each phase of the publication process, allowing authors to focus on the article contents. The framework and its integration with the most up to date and efficient tools has consequently provided a more professional and efficient automatized work environment to the whole collaboration.

47 OTHER INSTRUMENTATION↗

PSR J1906+0722: an Elusive Gamma-Ray Pulsar

Here, we report the discovery of PSR J1906+0722, a gamma-ray pulsar detected as part of a blind survey of unidentified Fermi Large Area Telescope (LAT) sources being carried out on the volunteer distributed computing system, Einstein@Home. This newly discovered pulsar previously appeared as the most significant remaining unidentified gamma-ray source without a known association in the second Fermi-LAT source catalog (2FGL) and was among the top 10 most significant unassociated sources in the recent third catalog (3FGL). PSR J1906+0722 is a young, energetic, isolated pulsar, with a spin frequency of 8.9 Hz, a characteristic age of 49 kyr, and spin-down power $1.0\times {10}^{36}$ erg s -1 . In 2009 August it suffered one of the largest glitches detected from a gamma-ray pulsar (${\rm{\Delta }}f/f\approx 4.5\times {10}^{-6}$). Remaining undetected in dedicated radio follow-up observations, the pulsar is likely radio-quiet. An off-pulse analysis of the gamma-ray flux from the location of PSR J1906+0722 revealed the presence of an additional nearby source, which may be emission from the interaction between a neighboring supernova remnant and a molecular cloud. We discuss possible effects which may have hindered the detection of PSR J1906+0722 in previous searches and describe the methods by which these effects were mitigated in this survey. Lastly, we also demonstrate the use of advanced timing methods for estimating the positional, spin and glitch parameters of difficult-to-time pulsars such as this.

79 ASTRONOMY AND ASTROPHYSICS↗

Integration of Ag-CBRAM crossbars and Mott ReLU neurons for efficient implementation of deep neural networks in hardware

In-memory computing with emerging non-volatile memory devices (eNVMs) has shown promising results in accelerating matrix-vector multiplications. However, activation function calculations are still being implemented with general processors or large and complex neuron peripheral circuits. Here, we present the integration of Ag-based conductive bridge random access memory (Ag-CBRAM) crossbar arrays with Mott rectified linear unit (ReLU) activation neurons for scalable, energy and area-efficient hardware (HW) implementation of deep neural networks. We develop Ag-CBRAM devices that can achieve a high ON/OFF ratio and multi-level programmability. Compact and energy-efficient Mott ReLU neuron devices implementing ReLU activation function are directly connected to the columns of Ag-CBRAM crossbars to compute the output from the weighted sum current. We implement convolution filters and activations for VGG-16 using our integrated HW and demonstrate the successful generation of feature maps for CIFAR-10 images in HW. Our approach paves a new way toward building a highly compact and energy-efficient eNVMs-based in-memory computing system.

Mott insulators↗

Intrinsic and Extrinsic Factors Influencing the Dynamics of VO 2 Mott Oscillators

Oscillatory devices have gained significant interest recently as key components of computing systems based on biomimetic neuronal spiking. An understanding of the time scales underlying the spiking is essential for engineering fast, controllable, low energy devices. However, we find that the intrinsic dynamics of these devices are difficult to properly characterize, as they can be heavily influenced by the external circuitry used to measure them. Here we demonstrate these challenges using a VO 2 Mott oscillator with a sub-100 nm effective size, achieved using a nanogap cut in a metallic carbon nanotube electrode. Given the nanoscale thermal volume of this device, it would be expected to exhibit rapid oscillations. However, due to external parasitics present within commonly used current sources, we see orders of magnitude slower dynamics. Here, we outline methods for determining when measurements are dominated by extrinsic factors and discuss the operating conditions under which intrinsic oscillation frequencies may be observed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Thermal fluctuations of the Josephson current in a ring of superconducting grains

Thermal fluctuations of the Josephson current induced by the magnetic flux through a ring of N superconducting grains are studied. When a half-fluxon is threading the ring, I exhibits incoherent transitions between the two degenerate states due to thermal phase slips. In this study, we propose a numerical method to deal with both equilibrium and dynamic properties of Josephson systems. Computed transition rate has the form Γ = A(N) exp[-B(N)/T ], where B(N) agrees with the analytical result derived for the energy barrier associated with phase slips. In the nondegenerate case (e.g., at a quarter-fluxon) the equilibrium value of I decreases with T due to harmonic excitations and then gets destroyed by phase slips.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Towards string order melting of spin-1 particle chains in superconducting transmons using optimal control

Utilizing optimal control to simulate a model Hamiltonian is an emerging strategy that leverages the intrinsic physics of a device with digital quantum simulation methods. Here we evaluate optimal control for probing the nonequilibrium properties of symmetry-protected topological (SPT) states simulated with superconducting hardware. Assuming a tunable transmon architecture, we cast the evolution of these SPT states as a series of one- and two-site pulse optimization problems that are solved in the presence of leakage constraints. From the generated pulses, we classically simulate the time-dependent melting of the perturbed SPT string order across a six-site model with an average state infidelity of 10 -3 . The feasibility of these pulses as well as their efficient application indicate that high-fidelity simulations of string order melting are within reach of current quantum computing systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Fault Location Using the Natural Frequency of Oscillation of Current Discharge in MTdc Networks

This paper discusses a novel offline-fault location approach using single ended measurements. The natural dissipation of the circuit parameters are considered for fault location. A relationship between the damped natural frequency of oscillation of the transmission line current and fault location is established. The hybrid dc circuit breaker (dcCB) interrupts the fault current, whereby the transmission line current attenuates under the absence of any driving voltage source. The line capacitance discharges into the fault at a specific frequency of oscillation and rate of attenuation. Utilizing this information, the fault location in a multi-terminal direct current (MTdc) network can be obtained. A three terminal radial model of a MTdc is used for performance evaluation of the proposed method using Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc).

42 ENGINEERING↗

Fail-Safe Logic Design Strategies Within Modern FPGA Architectures

Fail-safe computing refers to computing systems that revert to a non-operational safe state when a fault occurs. In this paper, we investigate a circuit level technique as mitigation for single event upsets (SEUs) and fault injection attacks on field programmable gate arrays (FPGAs), and analyze the effectiveness of the technique as a fail-safe monitor for an encryption algorithm. The propagation of fault effects through FPGA primitives including lookup tables (LUTs) and programmable interconnect points (PIPs) is assessed within an FPGA architecture created using an open source tool, and validated using fault injection experiments on an FPGA. The analysis reveals additional vulnerabilities exist within reconfigurable architectures over those in equivalent fail-safe application specific integrated circuit (ASIC), thus requiring a more elaborate network of redundant circuits and checking logic. The configuration memory bits (CMBs), which configure routing and designate logic functions within the LUTs of the FPGA, add complexity to fail-safe design strategies by introducing additional fault conditions and fault propagation paths. A resource-efficient fail-safe circuit design technique called DEsign for Fail-safe in reCONfigurable systems (DEFCON) is proposed. The benefits and limitations associated with DEFCON are described in the context of fault injection experiments carried out as simulations and in FPGA hardware.

Bhakta, Priya A. [Univ. of New Mexico, Albuquerque↗

Collective I/O Tuning Using Analytical and Machine-Learning Models

The ever larger demand of scientific applications for computation and data is currently driving a continuous increase in scale of parallel computers. The inherent complexity of scaling up a computing systems in terms of both hardware and software stack exposes an increasing number of factors impacting the performance and complicating the process of optimization. In particular, the optimization of parallel I/O has become increasingly challenging due to increasing storage hierarchy and well known performance variability of shared storage systems. This paper focuses on model-based autotuning of the two-phase collective I/O algorithm from a popular MPI distribution on the Blue Gene/Q architecture. We propose a novel hybrid model, constructed as a composition of analytical models for communication and storage operations and black-box models for the performance of the individual operations. We perform an in-depth study of the complexity involved in performance modeling including architecture, software stack and noise. In particular we address this challenges of modeling the performance of shared storage systems by building a benchmark that helps synthesizing factors such as topology, file caching, and noise. The experimental results show that the hybrid approach produces significantly better results than state-of-the-art machine learning approaches and shows a higher robustness to noise, at the cost of a higher modeling complexity

I/O performance modeling↗

Global Experiences with HPC Operational Data Measurement, Collection and Analysis

As we move into the exascale era, supercomputers grow larger, denser, more heterogeneous, and ever more complex. Operating such machines reliably and efficiently requires deep insight into the operational parameters of the machine itself as well as its supporting infrastructure. To fulfill this need, early adopter sites have started the development and deployment of Operational Data Analytics (ODA) frameworks allowing the continuous monitoring, archiving, and analysis of near realtime performance data from the machine and infrastructure levels, providing immediately actionable information for multiple operational uses. To understand their ODA goals, requirements, and use cases, we have conducted a survey among eight early adopter sites from the US, Europe, and Japan that operate top 50 high-performance computing systems. We have assessed the technologies leveraged to build their ODA frameworks, identified use cases and other push and pull factors that drive the sites' ODA activities, and report on their operational lessons.

Ott, Michael↗