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 !programming@programming.dev.

  • @80avin@programming.dev
    8•8 months ago

    Don’t have an objective definition of “Best” but here are few over the top of my head.

    1. Profiled backend’s startup and reduced startup time by ~20-30 times. Issue was we were traversing node_modules folder to scan for API documentation.
    2. Profiled python using cProfiler & snakeviz and optimized dozens of regex calls by simple pre-filtering. Improved performance by ~20 times.
    3. Profiled python using pprofile & qcalgrind (IIRC) to pinpoint a single heavy regex compilation and optimized it by pre-filtering. Improved performance 8-10 times.
    4. Use nodejs streams to do load data from s3, parse as CSV, transform into different structure and save into s3 in a continuous stream. Didn’t measure the performance gains but should be several times atleast. 5… many more, some coming from embedded world and even more drastic & unexpected