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Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
A Parallel Direct Method For Finite Element Electromagnetic Computations Based On Domain Decomposition, Javad Moshfegh
A Parallel Direct Method For Finite Element Electromagnetic Computations Based On Domain Decomposition, Javad Moshfegh
Doctoral Dissertations
High performance parallel computing and direct (factorization-based) solution methods have been the two main trends in electromagnetic computations in recent years. When time-harmonic (frequency-domain) Maxwell's equation are directly discretized with the Finite Element Method (FEM) or other Partial Differential Equation (PDE) methods, the resulting linear system of equations is sparse and indefinite, thus harder to efficiently factorize serially or in parallel than alternative methods e.g. integral equation solutions, that result in dense linear systems. State-of-the-art sparse matrix direct solvers such as MUMPS and PARDISO don't scale favorably, have low parallel efficiency and high memory footprint. This work introduces a new …
Parallel Algorithms For Time Dependent Density Functional Theory In Real-Space And Real-Time, James Kestyn
Parallel Algorithms For Time Dependent Density Functional Theory In Real-Space And Real-Time, James Kestyn
Doctoral Dissertations
Density functional theory (DFT) and time dependent density functional theory (TDDFT) have had great success solving for ground state and excited states properties of molecules, solids and nanostructures. However, these problems are particularly hard to scale. Both the size of the discrete system and the number of needed eigenstates increase with the number of electrons. A complete parallel framework for DFT and TDDFT calculations applied to molecules and nanostructures is presented in this dissertation. This includes the development of custom numerical algorithms for eigenvalue problems and linear systems. New functionality in the FEAST eigenvalue solver presents an additional level of …
A Scalable Architecture For Simplifying Full-Range Scientific Data Analysis, Wesley James Kendall
A Scalable Architecture For Simplifying Full-Range Scientific Data Analysis, Wesley James Kendall
Doctoral Dissertations
According to a recent exascale roadmap report, analysis will be the limiting factor in gaining insight from exascale data. Analysis problems that must operate on the full range of a dataset are among the most difficult. Some of the primary challenges in this regard come from disk access, data managment, and programmability of analysis tasks on exascale architectures. In this dissertation, I have provided an architectural approach that simplifies and scales data analysis on supercomputing architectures while masking parallel intricacies to the user. My architecture has three primary general contributions: 1) a novel design pattern and implmentation for reading multi-file …