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.
This isn’t any great C++ low level optimization but I have an Angular Project which contains a list that shows the live logs of various backend services and other things comming in through websockets (I keep up to 500K lines in memory). Essentially a extremly long scrollable list. First I obviously used lazy list which only renders the elements in view that are visibile instead of one that renders everything all the time. Secondly I removed as many html elements from each list element as possible, mostly divs that were just used to apply a css class.
When it comes to JS code I got a huge performance improvement by replacing a bunch of pure array.map, array.filter, array.toSorted, … method calls with a big ol for loop. These pure functions caused an insane memory churn because they create a shallow copy each time and doing that for an array with 500K entries caused the garbage collector to work overtime. Also the whole filtering and searching in the first draft was done every time new elements were added to the list via websocket for the whole entire list. I changed that so it only does it for new elements.
Overall this allowed me to increase the number of logs kept in memory from about 20K (because then performance would start to dip) to now 500K.