Python is one of the most widely used languages in scientific computing, and its adoption on HPC systems continues to grow, particularly among users with limited training in HPC and performance-oriented software development. At the same time, the ecosystem of tools for high-performance Python has expanded rapidly, making it increasingly difficult for users—and the research computing support teams advising them—to identify effective strategies to improve performance. I will discuss the landscape of high-performance Python with an emphasis on decision-making: how to choose appropriate tools and approaches based on workload characteristics and performance goals, focusing on common performance pitfalls, practical tradeoffs, and guidance for selecting technologies. The content will be most useful for Python users and research computing and data (RCD) facilitators who support Python workflows on HPC systems.
