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].
If you think it’s slow, first prove it with a benchmark
So many crimes against maintainability are committed in the name of performance. Optimisation tears down abstractions, exposes internals, and couples tightly. If you’re choosing to shoulder that cost, ensure it is done for good reason.
Yep, absolutely.
In another project, I had some throwaway code, where I used a naive approach that was easy to understand/validate. I assumed I would need to replace it once we made sure it was right because it would be too slow.
Turns out it wasn’t a bottleneck at all. It was my first time using Java streams with relatively large volumes of data (~10k items) and it turned out they were damn fast in this case. I probably could have optimized it to be faster, but for their simplicity and speed, I ended up using them everywhere in that project.