Optimizing Python code often begins with selecting efficient algorithms, using compiled language extensions, and incorporating parallelism. However, achieving even greater speed mandates a deeper understanding of CPU architecture. For instance, a common problem in scikit-learn's gradient histogram boosting involves assigning a large array of floating-point numbers to 255 integer buckets evenly. The initial approach uses a compiled, parallel binary search on sorted bucket boundaries. Significant speed improvements were achieved by ensuring the code aligned with CPU capabilities, rather than working against them. This involved understanding concepts like instruction-level parallelism and memory caches. The article will demonstrate a simplified example, illustrating how to achieve a 6x speedup over the original implementation. This journey will touch upon advanced low-level hardware topics such as branch prediction and SIMD. While not an in-depth tutorial, it will introduce the possibilities of such optimizations. The author will provide resources for further learning on these complex hardware topics.