07-28, 13:00–13:10 (UTC), Blue
Ever written code that was too slow because of excessive allocations, but didn't know where in your code they were coming from? Julia 1.8 introduces a new Allocation Profiler for finding and understanding sources of allocations in your julia programs, providing stack traces and type info for allocation hotspots. In this talk we will introduce the allocation profiler, cover how to use it, and talk through a small success story in our own codebase.
The Julia 1.8 release includes a sampling Allocation Profiler, producing a profile of sampled allocations from a running program, which you can use to understand, and hopefully reduce, the most expensive allocations in your program. The profiles are best viewed together with PProf.jl, which is a powerful (but complex) visual profile analysis tool.
Using this new profiler to track down and eliminate allocations can help improve performance, but there are some gotchas to keep in mind. What sample rate should you be using to get an accurate view of your program's behavior? How should you interpret the results? How do you navigate pprof's interface? We'll introduce these topics with quick practical guidance for the budding allocation hunters in the audience.
Software Engineer at RelationalAI
Works at RelationalAI.