Measuring Multiple Facets of Python Performance With Scalene | Real Python Podcast #172

When choosing a tool for profiling Python code performance, should it focus on the CPU, GPU, memory, or individual lines of code? What if it looked at all those factors and didn't alter code performance while measuring it? This week on the show, we talk about Scalene with Emery Berger, Professor of Computer Science at the University of Massachusetts Amherst. 👉 Links from the show: https://realpython.com/podcasts/rpp/172/ Emery talks about his background in memory management and his collaboration on Hoard, a scalable memory manager system used in Mac OS X. We discuss the need for improving code performance on modern computer architecture. He highlights this idea by contrasting the familiar limitations of Moore's law with the lesser-known rule of Dennard scaling. Working with his students in the university lab, they developed Scalene. Scalene is a high-performance CPU, GPU, and memory profiler. It can look at code from the individual function or line-by-line level and compare time spent in Python vs. C code. Emery talks about the recent Scalene feature of AI-powered optimization proposals and covers a couple examples. He also shares a collection of additional Python code-assistant tools from their lab. Topics: 00:00:00 -- Introduction 00:02:13 -- College of information and Computer Sciences 00:03:25 -- Memory management systems background 00:05:15 -- Dennard Scaling vs Moore's Law 00:10:12 -- Starting work on Python profiling 00:15:00 -- Deciding on a statistical profiler 00:17:05 -- Wanting to trace memory 00:21:21 -- Finding memory issues 00:23:59 -- Line-by-line analysis 00:25:56 -- Video Course Spotlight 00:27:14 -- Measuring profiler performance 00:30:30 -- Memory leak detection 00:34:31 -- When should you run a profiler? 00:37:27 -- Considerations for measuring cloud performance 00:39:12 -- Working with Jupyter and Conda 00:42:18 -- Common issues and AI solutions 00:45:50 -- Using a profiler to learn a code base 00:50:48 -- Examples of AI-powered optimizations 00:55:50 -- What are you excited about in the world of Python? 00:58:30 -- What do you want to learn next? 01:01:48 -- How can people follow your work online? 01:02:56 -- Thanks and goodbye 👉 Links from the show: https://realpython.com/podcasts/rpp/172/