Julia Performance Tips
Summary
The article provides a comprehensive guide to making Julia code faster, covering core practices like keeping performance-critical code inside functions, avoiding untyped globals, measuring with @time, and breaking complex logic into multiple definitions. It also discusses type inference, avoiding abstract containers, and the importance of type-stable code; it describes tools for profiling and debugging, and strategies for memory management, preallocation, and views. It also touches on parallelism, multithreading, and choosing appropriate linear-algebra backends, plus practical tweaks such as inbounds, fastmath, and dot fusion.