07-30, 16:40–17:10 (UTC), Red Track
Julia provides an intimidating array of ways to write programs that write programs. There are macros, generated functions, custom compiler passes, ASTs, IRs, DSLs and backends galore. This talk is a deep-dive into all of these tools that will hopefully clarify how and when you'd want to use them, with examples from advanced projects across the Julia ecosystem.
This talk will try to clarify the relationship between Julia's many metaprogramming tools, showing how you should (and shouldn't) use Julia's metaprogramming to create DSLs, build program transformations like differentiation, SPMD or batching, or apply domain-specific optimisations in areas like differential equations or probabilistic programming. We'll also clarify the relationship Julia's tools have to advanced programming tools from other language communities, like staged programming and algebraic effect handlers.
Mike is a software engineer at Julia Computing, where he works on Julia's machine learning stack.