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.
As a more fun or absurd anecdote, I reviewed a change in a SQL server stored procedure that merely copied the parameter value into a variable, solving a significant performance issue.
The query optimizer on first invocation determines the optimal query execution and resolution approach. For a stored procedure with parameters, parameter values can change what the best approach is.
In this case, the parameter could hold a record ID or null for all records. Because it included sub queries, whether it’s one or the other makes a huge difference on the query execution approach.
Using an intermediate variable prevents it of taking and remembering the optimization path for the other case.