Cache-Aware Programming in Python: A Surprisingly Significant Performance Difference

This post investigates the impact of cache-aware programming on Python performance through experiments. Results show that random access to list elements in Python is consistently slower than sequential access, especially when data size exceeds CPU cache. This suggests that even in interpreted environments, cache-aware programming can improve Python program performance. Experiments also compare the performance difference between native Python lists and NumPy arrays, showing NumPy arrays have a significant performance advantage due to their more compact memory layout.