Abstract

I would first like to dedicate this thesis to God by whom all things were made and in whom they are held together. Only by His grace and mercy through the gift of His Son, Jesus Christ, was I afforded the opportunity to live and work and play. I would also like to dedicate this thesis to my parents, Stephen and Debra Lebsack, whose support was pivotal in the completion of this work. I want to thank my siblings, Mary and her husband, Aaron Hudlemeyer, David and Laurel, and the rest of my family for their unwavering belief in my ability to complete PhD study and their continual love and support during my unbelief. I also thank Mary for her help in proofing and editing the thesis text. I, am, terrible, with, commas. Laurel deserves an extra thanks for enduring the front lines of battle while living with me during her own transition into college. I would also like to thank my fiancee, Tegwin Taylor, who, although she came along near the end of my study, was extremely uplifting and provided significant motivation to finish at long last. Thanks also goes out to the rest of my friends and family for their continued encouragement

Numerical simulations of dynamical systems are an obvious application of high-performance computing. Unfortunately, this application is underutilized because many modelers lack the technical expertise and financial resources to leverage high-performance computing hardware. Additionally, few platforms exist that can enable high-performance computing with real-time guarantees for inclusion into embedded systems--a prerequisite for working with medical devices. Here we introduce simEngine, a platform for numerical simulations of dynamical systems that reduces modelers' programming effort, delivers simulation speeds 10-100 times faster than a conventional microprocessor, and targets high-performance hardware suitable for real-time and embedded applications. This platform consists of a high-level mathematical language used to describe the simulation, a compiler/resource scheduler that generates the high-performance implementation of the simulation, and the high-performance hardware target. In this paper we present an overview of the platform, including a network-attached embedded computing device utilizing field-programmable gate arrays (FPGAs) suitable for real-time, high-performance computing. We go on to describe an example model implementation to demonstrate the platform's performance and describe how future development will improve system performance.

Performance of modern computers is tied closely to the effective use of cache because of the continually increasing speed discrepancy between processors and main memory. Optimum system performance is achieved when software and hardware work symbiotically to increase performance. This work focuses on identifying locality in object-oriented systems and developing techniques for approaching optimum performance with respect to the memory hierarchy.