Degree Type

Dissertation

Date of Award

2018

Degree Name

Doctor of Philosophy

Department

Computer Science

Major

Computer Science

First Advisor

Gurpur M. Prabhu

Abstract

Various aspects of high performance computing (HPC) are addressed in this thesis. The main focus is on analyzing and suggesting novel ideas to improve an application's performance and scalability on HPC systems and to make the most out of the available computational resources.

The choice of inter-process communication is one of the main factors that can influence an application's performance. This study investigates other computational paradigms, such as one-sided communication, that was known to improve the efficiency of current implementation methods. We compare the performance and scalability of the SHMEM and corresponding MPI-3 routines for five different benchmark tests using a Cray XC30. The performance of the MPI-3 get and put operations was evaluated using fence synchronization and also using lock-unlock synchronization. The five tests used communication patterns ranging from light to heavy data traffic: accessing distant messages, circular right shift, gather, broadcast and all-to-all. Each implementation was run using message sizes of 8 bytes, 10 Kbytes and 1 Mbyte and up to 768 processes. For nearly all tests, the SHMEM get and put implementations outperformed the MPI-3 get and put implementations. We noticed significant performance increase using MPI-3 instead of MPI-2 when compared with performance results from previous studies. One can use this performance and scalability analysis to choose the implementation method best suited for a particular application to run on a specific HPC machine.

Today's HPC machines are complex and constantly evolving, making it important to be able to easily evaluate the performance and scalability of HPC applications on both existing and new HPC computers. The evaluation of the performance of applications can be time consuming and tedious. HPC-Bench is a general purpose tool used to optimize benchmarking workflow for HPC to aid in the efficient evaluation of performance using multiple applications on an HPC machine with only a "click of a button". HPC-Bench allows multiple applications written in different languages, with multiple parallel versions, using multiple numbers of processes/threads to be evaluated. Performance results are put into a database, which is then queried for the desired performance data, and then the R statistical software package is used to generate the desired graphs and tables. The use of HPC-Bench is illustrated with complex applications that were run on the National Energy Research Scientific Computing Center's (NERSC) Edison Cray XC30 HPC computer.

With the advancement of HPC machines, one needs efficient algorithms and new tools to make the most out of available computational resources. This work also discusses a novel application of deep learning to a nuclear physics application. In recent years, several successful applications of the artificial neural networks (ANNs) have emerged in nuclear physics and high-energy physics, as well as in biology, chemistry, meteorology, and other fields of science. A major goal of nuclear theory is to predict nuclear structure and nuclear reactions from the underlying theory of the strong interactions, Quantum Chromodynamics (QCD). The nuclear quantum many-body problem is a computationally hard problem to solve. With access to powerful HPC systems, several ab initio approaches, such as the No-Core Shell Model (NCSM), have been developed for approximately solving finite nuclei with realistic strong interactions. However, to accurately solve for the properties of atomic nuclei, one faces immense theoretical and computational challenges. To obtain the nuclear physics observables as close as possible to the exact results, one seeks NCSM solutions in the largest feasible basis spaces. These results obtained in a finite basis, are then used to extrapolate to the infinite basis space limit and thus, obtain results corresponding to the complete basis within evaluated uncertainties. Each observable requires a separate extrapolation and most observables have no proven extrapolation method. We propose a feed-forward ANN method as an extrapolation tool to obtain the ground state energy and the ground state point-proton root-mean-square (rms) radius along with their extrapolation uncertainties. The designed ANNs are sufficient to produce results for these two very different observables in ^6Li from the ab initio NCSM results in small basis spaces that satisfy the following theoretical physics condition: independence of basis space parameters in the limit of extremely large matrices. Comparisons of the ANN results with other extrapolation methods are also provided.

Copyright Owner

Gianina Alina Negoita

Language

en

File Format

application/pdf

File Size

168 pages

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