Title page for ETD etd-060507-172337

Document Type masters report

Author Name Skjerven, Brian M.

URN etd-060507-172337

Title A Parallel Implementation of an Agent-Based Brain Tumor Model

Degree MS

Department Mathematical Sciences

Advisors
Homer Walker, Advisor

Keywords
Visualization
Numerical analysis
Computational biology
Scientific computation
High-performance computing

Date of Presentation/Defense 2007-06-11

Availability unrestricted

Abstract
The complex growth patterns of malignant brain tumors can present challenges in developing accurate models. In particular, the computational costs associated with modeling a realistically sized tumor can be prohibitive. The use of high-performance computing (HPC) and novel mathematical techniques can help to overcome this barrier. This paper presents a parallel implementation of a model for the growth of glioma, a form of brain cancer, and discusses how HPC is being used to take a first step toward realistically sized tumor models. Also, consideration is given to the visualization process involved with large-scale computing. Finally, simulation data is presented with a focus on scaling.

Files
bskjerven.pdf

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Document Type	masters report
Author Name	Skjerven, Brian M.
URN	etd-060507-172337
Title	A Parallel Implementation of an Agent-Based Brain Tumor Model
Degree	MS
Department	Mathematical Sciences
Advisors	Homer Walker, Advisor
Keywords	Visualization Numerical analysis Computational biology Scientific computation High-performance computing
Date of Presentation/Defense	2007-06-11
Availability	unrestricted
Abstract The complex growth patterns of malignant brain tumors can present challenges in developing accurate models. In particular, the computational costs associated with modeling a realistically sized tumor can be prohibitive. The use of high-performance computing (HPC) and novel mathematical techniques can help to overcome this barrier. This paper presents a parallel implementation of a model for the growth of glioma, a form of brain cancer, and discusses how HPC is being used to take a first step toward realistically sized tumor models. Also, consideration is given to the visualization process involved with large-scale computing. Finally, simulation data is presented with a focus on scaling.
Files	bskjerven.pdf