How do you reason about correctness in highly optimized Julia code?

Correctness relies on careful control of mutation, dispatch behavior, and deterministic execution paths.

Optimized Julia code often uses in-place operations, SIMD, and low-level memory control. This increases risk of subtle bugs like aliasing errors or race conditions. Ensuring correctness requires strict testing, deterministic seeding for randomness, and avoiding unsafe optimizations without validation.