JuliaCon 2022 (Times are UTC)

Reducing Running Time and Time to First X: A Walkthrough
07-28, 13:20–13:30 (UTC), Purple

Optimizing Julia isn't hard if you compare it to Python or R where you have to be an expert in Python or R and C/C++. I'll describe what type stability is and why it is important for performance. I'll discuss it in the context of performance (raw throughput) and in the context of time to first X (TTFX). Julia is sort of notorious for having really bad TTFX in certain cases. This talk explains the workflow that you can use to reduce running time and TTFX.

I'm a PhD student at the University of Groningen and co-author of the Julia Data Science book. I think that Julia solves a lot of problems that other languages have, so that's why I like contributing to the language ecosystem. To this end, I have created the Books.jl, PowerAnalyses.jl, Skans.jl and PlutoStaticHTML.jl packages and I contributed to Turing, MLJ, Pluto, julia-actions and more.