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 | Tools and Techniques for Floating-Point Analysis |
| Date and Time | 2019-10-16 01:00 pm EDT |
| Presenter | Ignacio Laguna (Lawrence Livermore National Laboratory) |
| Registration, Information, and Archives | https://ideas-productivity.org/resources/series/hpc-best-practices-webinars/#webinar034 |
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
Scientific software is central to the practice of research computing. While software is widely used in many science and engineering disciplines to simulate real-world phenomena, developing accurate and reliable scientific software is notoriously difficult. One of the most serious difficulties comes from dealing with floating-point arithmetic to perform numerical computations. Round-off errors occur and accumulate at all levels of computation, while compiler optimizations and low-precision arithmetic can significantly affect the final computational results. With accelerators such as GPUs dominating high-performance computing systems, computational scientists are faced with even bigger challenges, given that ensuring numerical reproducibility in these systems poses a very difficult problem. This webinar provides highlights from a half-day tutorial discussing tools that are available today to analyze floating-point scientific software. We focus on tools that allow programmers to get insight about how different aspects of floating-point arithmetic affect their code and how to fix potential bugs.
Presenter Bio
Ignacio Laguna is a Computer Scientist at the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory (LLNL). His main area of research is high-performance computing (HPC) and main sub-area of research in HPC is programming models and systems. He is a 2019 Better Scientific Software Fellow helping code teams to improve the reliability of scientific software through analyzing and debugging floating-point software.
