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Large-Scale System Monitoring Experiences and Recommendations

Ahlgren, V.; Andersson, S.; Brandt, James M.; Cardo, N.; Chunduri, S.; Enos, J.; Fields, P.; Gentile, Ann C.; Gerber, R.; Gienger, M.; Greenseid, J.; Greiner, A.; Hadri, B.; He, Y.; Hoppe, D.; Kaila, U.; Kelly, K.; Klein, M.; Kristiansen, A.; Leak, S.; Mason, M.; Laros, James H.; Piccinali, J-G; Repik, Jason; Rogers, J.; Salminen, S.; Showerman, M.; Whitney, C.; Williams, J.

Abstract not provided.

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TYPE Conference Poster YEAR 2018
DOIOSTI

Engineering Spin-Orbit Interaction in Silicon

Lu, Tzu-Ming L.; Maurer, Leon M.; Bussmann, Ezra B.; Harris, Charles T.; Tracy, Lisa A.; Sapkota, Keshab R.

There has been much interest in leveraging the topological order of materials for quantum information processing. Among the various solid-state systems, one-dimensional topological superconductors made out of strongly spin-orbit-coupled nanowires have been shown to be the most promising material platform. In this project, we investigated the feasibility of turning silicon, which is a non-topological semiconductor and has weak spin-orbit coupling, into a one-dimensional topological superconductor. Our theoretical analysis showed that it is indeed possible to create a sizable effective spin-orbit gap in the energy spectrum of a ballistic one-dimensional electron channel in silicon with the help of nano-magnet arrays. Experimentally, we developed magnetic materials needed for fabricating such nano-magnets, characterized the magnetic behavior at low temperatures, and successfully demonstrated the required magnetization configuration for opening the spin-orbit gap. Our results pave the way toward a practical topological quantum computing platform using silicon, one of the most technologically mature electronic materials.

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TYPE SAND Report YEAR 2018
DOIOSTI

Neural Networks as Surrogates of Nonlinear High-Dimensional Parameter-to-Prediction Maps

Jakeman, John D.; Perego, Mauro P.; Severa, William M.

We present a preliminary investigation of the use of Multi-Layer Perceptrons (MLP) and Recurrent Neural Networks (RNNs) as surrogates of parameter-to-prediction maps of computational expensive dynamical models. In particular, we target the approximation of Quantities of Interest (QoIs) derived from the solution of a Partial Differential Equations (PDEs) at different time instants. In order to limit the scope of our study while targeting a relevant application, we focus on the problem of computing variations in the ice sheets mass (our QoI), which is a proxy for global mean sea-level changes. We present a number of neural network formulations and compare their performance with that of Polynomial Chaos Expansions (PCE) constructed on the same data.

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TYPE SAND Report YEAR 2018
DOIOSTI

Underlying one-step methods and nonautonomous stability of general linear methods

Discrete and Continuous Dynamical Systems - Series B

Steyer, Andrew S.; Van Vleck, Erik S.

We generalize the theory of underlying one-step methods to strictly stable general linear methods (GLMs) solving nonautonomous ordinary differential equations (ODEs) that satisfy a global Lipschitz condition. We combine this theory with the Lyapunov and Sacker-Sell spectral stability theory for one-step methods developed in [34, 35, 36] to analyze the stability of a strictly stable GLM solving a nonautonomous linear ODE. These results are applied to develop a stability diagnostic for the solution of nonautonomous linear ODEs by strictly stable GLMs.

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TYPE Journal Article YEAR 2018
DOIOSTIScopus

Vanguard Astra and ATSE – an ARM-based Advanced Architecture Prototype System and Software Environment (FY18 L2 Milestone #8759 Report)

Laros, James H.; Laros, James H.; Hammond, Simon D.; Aguilar, Michael J.; Curry, Matthew L.; Grant, Ryan E.; Hoekstra, Robert J.; Klundt, Ruth A.; Monk, Stephen T.; Ogden, Jeffry B.; Olivier, Stephen L.; Scott, Randall D.; Ward, Harry L.; Younge, Andrew J.

The Vanguard program informally began in January 2017 with the submission of a white paper entitled "Sandia's Vision for a 2019 Arm Testbed" to NNSA headquarters. The program proceeded in earnest in May 2017 with an announcement by Doug Wade (Director, Office of Advanced Simulation and Computing and Institutional R&D at NNSA) that Sandia National Laboratories (Sandia) would host the first Advanced Architecture Prototype platform based on the Arm architecture. In August 2017, Sandia formed a Tri-lab team chartered to develop a robust HPC software stack for Astra to support the Vanguard program goal of demonstrating the viability of Arm in supporting ASC production computing workloads.

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TYPE SAND Report YEAR 2018
DOIOSTI
Results 2851–2875 of 9,998
Results 2851–2875 of 9,998