Struct criterion::Bencher
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pub struct Bencher { /* fields omitted */ }
Helper struct to time routines
This struct provides different timing loops as methods. Each timing loop provides a different way to time a routine and each has advantages and disadvantages.
- If your routine returns a value with an expensive
drop
method, useiter_with_large_drop
. - If your routine requires some per-iteration setup that shouldn't be timed,
use
iter_with_setup
or (if the setup is expensive) useiter_with_large_setup
to construct a pool of input data ahead of time - Otherwise, use
iter
.
Methods
impl Bencher
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pub fn iter<O, R>(&mut self, routine: R) where
R: FnMut() -> O,
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R: FnMut() -> O,
Times a routine
by executing it many times and timing the total elapsed time.
Prefer this timing loop when routine
returns a value that doesn't have a destructor.
Timing loop
let start = Instant::now(); for _ in 0..iters { routine(); } let elapsed = start.elapsed();
Timing model
Note that the Bencher
also times the time required to destroy the output of routine()
.
Therefore prefer this timing loop when the runtime of mem::drop(O)
is negligible compared
to the runtime of the routine
.
elapsed = Instant::now + iters * (routine + mem::drop(O) + Range::next)
pub fn iter_with_setup<I, O, S, R>(&mut self, setup: S, routine: R) where
S: FnMut() -> I,
R: FnMut(I) -> O,
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S: FnMut() -> I,
R: FnMut(I) -> O,
Times a routine
that requires some setup
on each iteration.
For example, use this loop to benchmark sorting algorithms because they require unsorted data on each iteration.
Example
extern crate criterion; use criterion::Bencher; fn create_scrambled_data() -> Vec<u64> { // ... } // The sorting algorithm to test fn sort(data: &mut [u64]) { // ... } fn benchmark(b: &mut Bencher) { let data = create_scrambled_data(); b.iter_with_setup(move || data.to_vec(), |mut data| sort(&mut data)) }
Timing loop
let mut elapsed = Duration::new(0, 0); for _ in 0..iters { let input = setup(); let start = Instant::now(); let output = routine(input); let elapsed_in_iter = start.elapsed(); mem::drop(output); elapsed = elapsed + elapsed_in_iter; }
Timing model
elapsed = iters * (Instant::now + routine)
pub fn iter_with_large_drop<O, R>(&mut self, routine: R) where
R: FnMut() -> O,
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R: FnMut() -> O,
Times a routine
by collecting its output on each iteration. This avoids timing the
destructor of the value returned by routine
.
WARNING: This requires iters * mem::size_of::<O>()
of memory, and iters
is not under the
control of the caller.
Timing loop
let mut outputs = Vec::with_capacity(iters); let start = Instant::now(); for _ in 0..iters { outputs.push(routine()); } let elapsed = start.elapsed(); mem::drop(outputs);
Timing model
elapsed = Instant::now + iters * (routine + Vec::push + Range::next)
pub fn iter_with_large_setup<I, S, R>(&mut self, setup: S, routine: R) where
S: FnMut() -> I,
R: FnMut(I),
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S: FnMut() -> I,
R: FnMut(I),
Times a routine
that needs to consume its input by first creating a pool of inputs.
This function is handy for benchmarking destructors.
WARNING This requires iters * mem::size_of::<I>()
of memory, and iters
is not under the
control of the caller.
Timing loop
let inputs: Vec<()> = (0..iters).map(|_| setup()).collect(); let start = Instant::now(); for input in inputs { routine(input); } let elapsed = start.elapsed();
Timing model
elapsed = Instant::now + iters * (routine + vec::IntoIter::next)
Trait Implementations
impl Clone for Bencher
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fn clone(&self) -> Bencher
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Returns a copy of the value. Read more
fn clone_from(&mut self, source: &Self)
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Performs copy-assignment from source
. Read more