Got myself a few months ago into the optimization rabbit hole as I had a slow quant finance library to take care of, and for now my most successful optimizations are using local memory allocators (see my C++ post, I also played with mimalloc which helped but custom local memory allocators are even better) and rethinking class layouts in a more “data-oriented” way (mostly going from array-of-structs to struct-of-arrays layouts whenever it’s more advantageous to do so, see for example this talk).
What are some of your preferred optimizations that yielded sizeable gains in speed and/or memory usage? I realize that many optimizations aren’t necessarily specific to any given language so I’m asking in [email protected].
another funny optimization I made was we were setting up a big Perl project for Docker. The automated tests were very slow because each test was doing includes which was searching many include paths and if you’re using Docker on Windows or Mac then I/O stuff can be slow. So we made the docker init script create a single master lib folder and have it make symlinks to the actual files, perl then only had the default include path and this master folder, gave us like 2x or 3x speed boost on running the test suite.