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

• Ignacio Laguna (Lawrence Livermore National Laboratory)

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 programing 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.