Data scientists and ML engineers write a lot of code: building data pipelines, wiring up models, and sometimes translating concepts from research papers into algorithms.  

Once in a while, that code runs into performance problems.  These can be painful to debug when you don't come from a formal software development background.  That's why Formulatedby's Senior Content Advisor Q McCallum rang up Matt Godbolt to learn the deep details of software testing, tracing performance bugs, working with data at scale, and how data scientists can work with developers to prepare their code for a production handoff.

Matt Godbolt has more than 30 years' experience writing code.  He's spent most of that time working in the performance-focused environments of console video games, high-frequency trading (HFT), and algorithmic trading.  Matt is the creator of the Compiler Explorer website, and also co-host of the Two's Complement podcast.

(Note from Q: My audio is a little choppy, but Matt's is perfect.  And you're here to hear him, anyway...)

Matt and Q mentioned a few links during their talk:

• Michael Abrash’s Zen of Code Optimization
• Brendan Gregg’s Flame Graphs
• Matt's blog and videos
• Related to the discussion on performance enhancements in Quake, Matt has a video on “how Wolfenstein worked”
• Compiler Explorer