Crate easybench

A lightweight benchmarking library which:

• uses linear regression to screen off sources of constant error;
• handles benchmarks which must mutate some state;
• has a very simple API!

It's inspired by criterion, but doesn't do as much sophisticated analysis.

use easybench::{bench,bench_env};

// Simple benchmarks are performed with `bench`.
println!("fib 200: {}", bench(|| fib(200) ));
println!("fib 500: {}", bench(|| fib(500) ));

// If a function needs to mutate some state, use `bench_env`.
println!("reverse: {}", bench_env(vec![1,2,3], |xs| xs.reverse()));
println!("sort:    {}", bench_env(vec![1,2,3], |xs| xs.sort()));

Running the above yields the following results:

fib 200:         38 ns   (R²=1.000, 26053498 iterations in 155 samples)
fib 500:        109 ns   (R²=1.000, 9131585 iterations in 144 samples)
reverse:          3 ns   (R²=0.998, 23684997 iterations in 154 samples)
sort:             3 ns   (R²=0.999, 23684997 iterations in 154 samples)

Easy! However, please read the caveats below before using.

Caveat 1: Harness overhead

TL;DR: compile with --release, and don't use bench_env if your benchmark can't tolerate a systematic cache miss.

How much overhead does easybench itself introduce? As mentioned above and explained below, we use the linear regression technique in order to eliminate any constant error involved with taking a sample. However, this technique doesn't prevent linear error from showing up - that is, if there's some work which easybench does every iteration, then it will be included in the results.

In most cases, this work should be negligable (so long as you compile with --release). In the case of bench it amounts to incrementing the loop counter. In the case of bench_env, we also do a lookup into a big vector every iteration, in order to get the environment for that iteration. This may be more of a concern, depending on the code you're benchmarking.

Caveat 2: Pure functions

TL;DR: when benchmarking pure functions, return enough information to prevent the optimiser from eliminating code from your benchmark!

Benchmarking pure functions involves a nasty gotcha which users should be aware of. Consider the following benchmarks:

let fib_1 = bench(|| fib(500) );                     // fine
let fib_2 = bench(|| { fib(500); } );                // spoiler: NOT fine
let fib_3 = bench_env(0, |x| { *x = fib(500); } );   // also fine, but ugly

The results are a little surprising:

fib_1:        110 ns   (R²=1.000, 9131585 iterations in 144 samples)
fib_2:          0 ns   (R²=1.000, 413289203 iterations in 184 samples)
fib_3:        109 ns   (R²=1.000, 9131585 iterations in 144 samples)

Oh, fib_2, why do you lie? The answer is: fib(500) is pure, and its return value is immediately thrown away, so the optimiser replaces the call with a no-op (which clocks in at 0 ns).

What about the other two? fib_1 looks very similar, with one exception: the closure which we're benchmarking returns the result of the fib(500) call. When it runs your code, easybench takes the return value and tricks the optimiser into thinking it's going to use it for something, before throwing it away. This is why fib_1 is safe from having code accidentally eliminated.

In the case of fib_3, we actually do use the return value: each iteration we take the result of fib(500) and store it in the iteration's environment. This has the desired effect, but looks a bit weird.

The benchmarking algorithm

First, let me define "sample" and "iteration". An iteration is a single execution of your code. A sample is a measurement, during which your code may be run many times. That is: taking a sample means performing some number of iterations and measuring the total time.

We begin by taking a sample and throwing away the results, in an effort to warm up some caches.

Now we start collecting data. The first sample performs only 1 iteration, but as we continue taking samples we increase the number of iterations exponentially. We stop when a time limit is reached (currently 1 second).

Next, we perform OLS regression on the resulting data. The gradient of the regression line is our measure of the time it takes to perform a single iteration of the benchmark. The R² value is a measure of how much noise there is in the data.

Structs

Stats

Statistics for a benchmark run.

Functions

bench	Run a benchmark.
bench_env	Run a benchmark with an environment.