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.
- Location: Online
- Event Website: https://ideas-productivity.org/events/hpc-best-practices-webinars/#webinar045
- Organizers: The IDEAS Productivity Project
|Webinar Title||Scalable Precision Tuning of Numerical Software|
|Date and Time||2020-10-14 05:00 pm WTZ|
|Presenter||Cindy Rubio-Gonzalez (University of California, Davis)|
|Registration, Information, and Archives||https://ideas-productivity.org/events/hpc-best-practices-webinars/#webinar045|
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.
The use of numerical software has grown rapidly over the past few years, providing the foundation for a large variety of applications including scientific software and machine learning. Given the variety of numerical errors that can occur, floating-point programs are difficult to write, test and debug. One common practice among developers is to use the highest available precision when allocating variables. While more robust, this can degrade program performance significantly. This webinar describes our research on developing tools to assist programmers in tuning the precision of their floating-point programs. These tools conduct a data-driven approach to search over the types of floating-point variables to lower their precision subject to accuracy constraints and performance goals. In the last part of the webinar, I will discuss challenges and opportunities for scalable precision tuning of large HPC applications.
Cindy Rubio-Gonzalez is an Associate Professor of Computer Science at the University of California, Davis. Prior to joining UC Davis, she was a Postdoctoral Researcher in the EECS Department at the University of California, Berkeley. She received her Ph.D. in Computer Science from the University of Wisconsin–Madison in 2012. Dr. Rubio’s work spans the areas of Programming Languages and Software Engineering, with a focus on program analysis for automated bug finding and program optimization. She is particularly interested in the reliability and performance of systems software and scientific computing applications. Dr. Rubio is a Better Scientific Software Fellow 2020, and a recipient of a DOE Early Career Award 2019, an NSF CAREER award 2018, a Hellman Fellowship 2017, and a UC Davis CAMPOS Faculty Award 2014. Dr. Rubio earned her M.S. in Computer Science from the University of Wisconsin–Milwaukee and her B.S. in Computer Engineering from Saltillo Institute of Technology (Mexico). She also holds a B.M. in Piano Performance from the Autonomous University of Coahuila (Mexico).