  By their very name caches are often overlooked and yet play a vital role in
the performance of modern and indeed future hardware. Using MAGPIE (Machine
Automated General Performance Improvement via Evolution of software) we show
genetic improvement GI can reduce the cache load of existing computer programs.
Operating on lines of C and C++ source code using local search, Magpie can
generate new functionally equivalent variants which generate fewer L1 data
cache misses. Cache miss reduction is tested on two industrial open source
programs (Google's Open Location Code OLC and Uber's Hexagonal Hierarchical
Spatial Index H3) and two 2D photograph image processing tasks, counting pixels
and OpenCV's SEEDS segmentation algorithm.
  Magpie's patches functionally generalise. In one case they reduce data misses
on the highest performance L1 cache dramatically by 47 percent.
