Knowledge is power: Quantum chemistry on novel computer architectures

In the first chapter of this thesis, a background of fundamental quantum chemistry concepts is provided. Chapter two contains an analysis of the performance and energy efficiency of various modern computer processor architectures while performing computational chemistry calculations. In chapter three, the processor architectural study is expanded to include parallel computational chemistry algorithms executed across multiple-node computer clusters. Chapter four describes a novel computational implementation of the fundamental Hartree-Fock method which significantly reduces computer memory requirements. In chapter five, a case study of quantum chemistry two-electron integral code interoperability is described. The final chapters of this work discuss applications of quantum chemistry. In chapter six, an investigation of the esterification of acetic acid on acid-functionalized silica is presented. In chapter seven, the application of ab initio molecular dynamics to study the photoisomerization and photocyclization of stilbene is discussed. Final concluding remarks are noted in chapter eight

Knowledge is power: Quantum chemistry on novel computer architectures

In the first chapter of this thesis, a background of fundamental quantum chemistry concepts is provided. Chapter two contains an analysis of the performance and energy efficiency of various modern computer processor architectures while performing computational chemistry calculations. In chapter three, the processor architectural study is expanded to include parallel computational chemistry algorithms executed across multiple-node computer clusters. Chapter four describes a novel computational implementation of the fundamental Hartree-Fock method which significantly reduces computer memory requirements. In chapter five, a case study of quantum chemistry two-electron integral code interoperability is described. The final chapters of this work discuss applications of quantum chemistry. In chapter six, an investigation of the esterification of acetic acid on acid-functionalized silica is presented. In chapter seven, the application of ab initio molecular dynamics to study the photoisomerization and photocyclization of stilbene is discussed. Final concluding remarks are noted in chapter eight

Proceedings, MSVSCC 2015

The Virginia Modeling, Analysis and Simulation Center (VMASC) of Old Dominion University hosted the 2015 Modeling, Simulation, & Visualization Student capstone Conference on April 16th. The Capstone Conference features students in Modeling and Simulation, undergraduates and graduate degree programs, and fields from many colleges and/or universities. Students present their research to an audience of fellow students, faculty, judges, and other distinguished guests. For the students, these presentations afford them the opportunity to impart their innovative research to members of the M&S community from academic, industry, and government backgrounds. Also participating in the conference are faculty and judges who have volunteered their time to impart direct support to their students’ research, facilitate the various conference tracks, serve as judges for each of the tracks, and provide overall assistance to this conference. 2015 marks the ninth year of the VMASC Capstone Conference for Modeling, Simulation and Visualization. This year our conference attracted a number of fine student written papers and presentations, resulting in a total of 51 research works that were presented. This year’s conference had record attendance thanks to the support from the various different departments at Old Dominion University, other local Universities, and the United States Military Academy, at West Point. We greatly appreciated all of the work and energy that has gone into this year’s conference, it truly was a highly collaborative effort that has resulted in a very successful symposium for the M&S community and all of those involved. Below you will find a brief summary of the best papers and best presentations with some simple statistics of the overall conference contribution. Followed by that is a table of contents that breaks down by conference track category with a copy of each included body of work. Thank you again for your time and your contribution as this conference is designed to continuously evolve and adapt to better suit the authors and M&S supporters. Dr.Yuzhong Shen Graduate Program Director, MSVE Capstone Conference Chair John ShullGraduate Student, MSVE Capstone Conference Student Chai

Study of Condensed Matter Systems with Monte Carlo Simulation on Heterogeneous Computing Systems

We study the Edwards-Anderson model on a simple cubic lattice with a finite constant external field. We employ an indicator composed of a ratio of susceptibilities at finite momenta, which was recently proposed to avoid the difficulties of a zero momentum quantity, for capturing the spin glass phase transition. Unfortunately, this new indicator is fairly noisy, so a large pool of samples at low temperature and small external field are needed to generate results with a sufficiently small statistical error for analysis. We thus implement the Monte Carlo method using graphics processing units to drastically speed up the simulation. We confirm previous findings that conventional indicators for the spin glass transition, including the Binder ratio and the correlation length do not show any indication of a transition for rather low temperatures. However, the ratio of spin glass susceptibilities does show crossing behavior, albeit a systematic analysis is beyond the reach of the present data. This reveals the difficulty with current numerical methods and computing capability in studying this problem. One of the fundamental challenges of theoretical condensed matter physics is the accurate solution of quantum impurity models. By taking expansion in the hybridization about an exactly solved local limit, one can formulate a quantum impurity solver. We implement the hybridization expansion quantum impurity solver on Intel Xeon Phi accelerators, and aim to apply this approach on the Dynamic Hubbard Models

Viriato: a Fourier-Hermite spectral code for strongly magnetised fluid-kinetic plasma dynamics

We report on the algorithms and numerical methods used in Viriato, a novel fluid-kinetic code that solves two distinct sets of equations: (i) the Kinetic Reduced Electron Heating Model (KREHM) equations [Zocco & Schekochihin, Phys. Plasmas 18, 102309 (2011)] (which reduce to the standard Reduced-MHD equations in the appropriate limit) and (ii) the kinetic reduced MHD (KRMHD) equations [Schekochihin et al., Astrophys. J. Suppl. 182:310 (2009)]. Two main applications of these equations are magnetised (Alfvenic) plasma turbulence and magnetic reconnection. Viriato uses operator splitting (Strang or Godunov) to separate the dynamics parallel and perpendicular to the ambient magnetic field (assumed strong). Along the magnetic field, Viriato allows for either a second-order accurate MacCormack method or, for higher accuracy, a spectral-like scheme composed of the combination of a total variation diminishing (TVD) third order Runge-Kutta method for the time derivative with a 7th order upwind scheme for the fluxes. Perpendicular to the field Viriato is pseudo-spectral, and the time integration is performed by means of an iterative predictor-corrector scheme. In addition, a distinctive feature of Viriato is its spectral representation of the parallel velocity-space dependence, achieved by means of a Hermite representation of the perturbed distribution function. A series of linear and nonlinear benchmarks and tests are presented, including a detailed analysis of 2D and 3D Orszag-Tang-type decaying turbulence, both in fluid and kinetic regimes.Comment: 42 pages, 15 figures, submitted to J. Comp. Phy

Proceedings, MSVSCC 2014

Proceedings of the 8th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 17, 2014 at VMASC in Suffolk, Virginia

最先端高性能計算システムにおける第一原理電子動力学シミュレーションのコデザイン

筑波大学 (University of Tsukuba)201

Deep Model for Improved Operator Function State Assessment

A deep learning framework is presented for engagement assessment using EEG signals. Deep learning is a recently developed machine learning technique and has been applied to many applications. In this paper, we proposed a deep learning strategy for operator function state (OFS) assessment. Fifteen pilots participated in a flight simulation from Seattle to Chicago. During the four-hour simulation, EEG signals were recorded for each pilot. We labeled 20- minute data as engaged and disengaged to fine-tune the deep network and utilized the remaining vast amount of unlabeled data to initialize the network. The trained deep network was then used to assess if a pilot was engaged during the four-hour simulation