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.
More than ten years ago, I was a team-lead in a game development company and during another crunch I was very overloaded with tasks, including a code review. And of course, I missed some things. We optimized the game, which in general did not do 30 fps on the target machine with minimum requirements, and the target was at least 40 fps. During the month, we jointly optimized everything so that we were able to achieve almost stable 40 frames, but this was not enough and periodically there were drawdowns up to 15-20 fps. Everyone is in panic, there are no ideas, we’re optimized everything.
No, I understand that there is no optimization limit apriori, but I mean an adequate opts without rewriting everything to assembler specifically for all supported architectures.
So, one night I looked into the event loop initialization and found that the initialization happens twice. Twice. Two parallel event loops. That is, two parallel cycles of logic and state updates. That night, by deleting one line, I optimized the performance of the game by more than 100%. 🤦🏻♂️
I investigated and found out that this line of secondary initialization was left by junior “for debugging” and forgotten in the crunch. And I missed it on the review.
I’ve keep this story a secret all these years. And now I’m not revealing names. Otherwise, it can have a dramatic impact on the careers of many.
Human code review is very error prone