Struct Bencher

Source

pub struct Bencher { /* private fields */ }

Expand description

Timer struct to iterate a benchmarked function and measure the runtime.

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 requires no per-iteration setup and returns a value with an expensive drop method, use iter_with_large_drop.
If your routine requires some per-iteration setup that shouldn’t be timed, use iter_batched or iter_batched_ref. See BatchSize for a discussion of batch sizes. If the setup value implements Drop and you don’t want to include the drop time in the measurement, use iter_batched_ref, otherwise use iter_batched. These methods are also suitable for benchmarking routines which return a value with an expensive drop method, but are more complex than iter_with_large_drop.
Otherwise, use iter.

Implementations§

Source §

impl Bencher

Source

pub fn iter<O, R>(&mut self, routine: R)
where 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 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)

§Example

#[macro_use] extern crate criterion;

use criterion::*;

// The function to benchmark
fn foo() {
    // ...
}

fn bench(c: &mut Criterion) {
    c.bench_function("iter", move |b| {
        b.iter(|| foo())
    });
}

criterion_group!(benches, bench);
criterion_main!(benches);

Source

pub fn iter_batched<I, O, S, R>( &mut self, setup: S, routine: R, size: BatchSize, )
where S: FnMut() -> I, R: FnMut(I) -> O,

Times a routine that requires some input by generating a batch of input, then timing the iteration of the benchmark over the input. See BatchSize for details on choosing the batch size. Use this when the routine must consume its input.

For example, use this loop to benchmark sorting algorithms, because they require unsorted data on each iteration.

§Timing model

elapsed = (Instant::now * num_batches) + (iters * (routine + O::drop)) + Vec::extend

§Example

#[macro_use] extern crate criterion;

use criterion::*;

fn create_scrambled_data() -> Vec<u64> {
    // ...
}

// The sorting algorithm to test
fn sort(data: &mut [u64]) {
    // ...
}

fn bench(c: &mut Criterion) {
    let data = create_scrambled_data();

    c.bench_function("with_setup", move |b| {
        // This will avoid timing the to_vec call.
        b.iter_batched(|| data.clone(), |mut data| sort(&mut data), BatchSize::SmallInput)
    });
}

criterion_group!(benches, bench);
criterion_main!(benches);

Source

pub fn iter_batched_ref<I, O, S, R>( &mut self, setup: S, routine: R, size: BatchSize, )
where S: FnMut() -> I, R: FnMut(&mut I) -> O,

Times a routine that requires some input by generating a batch of input, then timing the iteration of the benchmark over the input. See BatchSize for details on choosing the batch size. Use this when the routine should accept the input by mutable reference.

For example, use this loop to benchmark sorting algorithms, because they require unsorted data on each iteration.

§Timing model

elapsed = (Instant::now * num_batches) + (iters * routine) + Vec::extend

§Example

#[macro_use] extern crate criterion;

use criterion::*;

fn create_scrambled_data() -> Vec<u64> {
    // ...
}

// The sorting algorithm to test
fn sort(data: &mut [u64]) {
    // ...
}

fn bench(c: &mut Criterion) {
    let data = create_scrambled_data();

    c.bench_function("with_setup", move |b| {
        // This will avoid timing the to_vec call.
        b.iter_batched(|| data.clone(), |mut data| sort(&mut data), BatchSize::SmallInput)
    });
}

criterion_group!(benches, bench);
criterion_main!(benches);