6× faster binary search: from compiled code to mechanical sympathy

This article explains how to accelerate binary search in Python by writing compiled extensions (Rust/Cython) and applying low-level hardware optimizations. It starts with a standard binary search implementation used in scikit-learn's gradient histogram boosting, then demonstrates a series of improvements that achieve a 6× speedup. Topics include instruction-level parallelism, branch misprediction, memory caches, and SIMD. The author walks through each optimization step, showing how to avoid fighting the CPU and instead leverage mechanical sympathy. Aimed at developers seeking extreme performance for computational Python code, the article also provides resources for learning more about hardware-aware programming.