This event is a part of the "Best Practices for HPC Software Developers" webinar series, produced by the IDEAS Productivity Project. The HPC Best Practices webinars address issues faced by developers of computational science and engineering (CSE) software on high-performance computers (HPC) and occur approximately monthly.
| Resource Information | Details |
|---|---|
| Webinar Title | Mining Development Data to Understand and Improve Software Engineering Processes in HPC Projects |
| Date and Time | 2021-07-07 01:00 pm EDT |
| Presenter | Boyana Norris (University of Oregon) |
| Registration, Information, and Archives | https://ideas-productivity.org/resources/series/hpc-best-practices-webinars/#webinar054 |
Webinars are free and open to the public, but advance registration is required through the Event website. Archives (recording, slides, Q&A) will be posted at the same link soon after the event.
Abstract
The webinar will explore the role of software-related data mining tools in supporting productive development of high-performance scientific software. The webinar will discuss a variety of existing and emerging tools for analyzing code, git, emails, issues, test results, and dependencies, with the long-term goal of improving the understanding of development processes and enhancing developer productivity. The webinar will include specific analysis examples by applying a subset of those tools to ECP projects.
Presenter Bio
Boyana Norris received her B.S. in Computer Science at Wake Forest University in 1995 and her Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2000. She joined Argonne National Laboratory as a postdoctoral researcher in 1999 and continued working there through 2013 as an Assistant Computer Scientist and Computer Scientist. She is currently an Associate Professor at the Department of Computer and Information Science at the University of Oregon. Her research in high-performance computing (HPC) focuses on methodologies and tools for performance reasoning and automated optimization of scientific applications, while ensuring continued or better usability of HPC tools and libraries and improving developer productivity. She has coauthored over 90 peer-reviewed publications on topics including performance modeling, automated performance optimization (autotuning) of parallel scientific applications, embedding of domain-specific languages into legacy codes, source-transformation-based automatic differentiation, adaptive algorithms for HPC, component-based software engineering for HPC, and taxonomy-based approaches to learning and using HPC libraries.
