I wondered how to benchmark functions correctly that modify always the same input in-place. In particular, using the @btime macro of BenchmarkTools.jl. For example consider the following minimal code:
const array = [1, 2, 3]
Here, I want to benchmark how long it takes to push 4 at the end of array with the input array always staying [1, 2, 3]. However, what @btime actually benchmarks is pushing 4 at the end of [1, 2, 3] in the first iteration, at the end of [1, 2, 3, 4] in the second iteration, at the end of [1, 2, 3, 4, 4] in the third iteration and so on.
I have the feeling I’m missing something obvious here; is there a way to do this?
Note that the setup and teardown phases are executed for each sample, not each evaluation . Thus, the sorting example above wouldn’t produce the intended results if evals/sample > 1 (it’d suffer from the same problem of benchmarking against an already sorted vector).
I suspect you’re seeing an artifact of the amortized constant time of push! (see https://cs.stackexchange.com/a/9382 for some discussion). The longer the vector gets, the less frequently its capacity is changed, so the more likely it is that your benchmark loop will happen to catch a set of pushes that do not contain a capacity change (and thus don’t contain any memory allocation).
In other words: don’t worry about those few extra nanoseconds and the one allocation here