UNDERGRADUATE RESEARCH JOURNAL
Nick Weidner

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Nick Weidner

My name is Nick Weidner, I am from Columbia, SC, and I am a sophomore majoring in computer science and mathematics at the University of South Carolina. I am a Wilson Scholar in Computing as well as a Capstone Scholar. I plan to attend graduate school in computer science, although I am still trying to determine an area of specialization. For this reason, I participated in the Research Experience for Undergraduates (REU) program at the University of Notre Dame in the summer of 2013 sponsored by the National Science Foundation (NSF). During my 10 weeks at Notre Dame, I worked under the guidance of Dr. Tim Stitt in the Center for Research Computing. My research objective was to take a sophisticated storm-surge model developed in their Computational Hydraulics Laboratory over the past 20 years, and improve run-time performance using a highly paralleled supercomputer at the Texas Supercomputer facility. This project has given me insight into the field of high performance computing (HPC), as well as the opportunity to work alongside other graduate students and faculty doing very important and fascinating research. I plan to continue this project under the direction of Dr. Jijun Tang in the Computer Science and Engineering Department at USC with input from Dr. Stitt. I would like to thank the NSF, the Notre Dame Center for Research Computing, Dr. Stitt, and the members of the Notre Dame Computational Hydraulics Lab. This work was presented as a poster at the Norte Dame Research Symposium on August 2, 2013.

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My name is Nick Weidner, I am from Columbia, SC, and I am a sophomore majoring in computer science and mathematics at the University of South Carolina. I am a Wilson Scholar in Computing as well as a... read more

Articles by Nick Weidner

Towards Improving the Performance of the ADCIRC Storm Surge Modeling Software

caravel_sept

Abstract Accurately predicting storms and hurricanes is critical to saving lives and reducing economic loss. Therefore, it is necessary to use the most efficient software and hardware technology available in order to improve the performance and fidelity of these predictive mathematical models. For over ten years, the Computational Hydraulics Lab (CHL) at the University of

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