# Crate easybench[−][src]

A lightweight micro-benchmarking library which:

- uses linear regression to screen off constant error;
- handles benchmarks which mutate state;
- is very easy to use!

Easybench is designed for benchmarks with a running time in the range `1 ns < x < 1 ms`

- results
may be unreliable if benchmarks are very quick or very slow. It's inspired by criterion, but
doesn't do as much sophisticated analysis (no outlier detection, no HTML output).

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![0;100], |xs| xs.reverse() )); println!("sort: {}", bench_env(vec![0;100], |xs| xs.sort() ));

Running the above yields the following results:

```
fib 200: 38 ns (R²=1.000, 26053497 iterations in 154 samples)
fib 500: 110 ns (R²=1.000, 9131584 iterations in 143 samples)
reverse: 54 ns (R²=0.999, 5669992 iterations in 138 samples)
sort: 93 ns (R²=1.000, 4685942 iterations in 136 samples)
```

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

# Benchmarking algorithm

An *iteration* is a single execution of your code. A *sample* is a measurement, during which your
code may be run many times. In other words: taking a sample means performing some number of
iterations and measuring the total time.

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

If a benchmark must mutate some state while running, before taking a sample `n`

copies of the
initial state are prepared, where `n`

is the number of iterations in that sample.

Once we have the data, we perform OLS linear regression to find out how the sample time varies with the number of iterations in the sample. The gradient of the regression line tells us how long 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.

# Caveats

## Caveat 1: Harness overhead

**TL;DR: Compile with --release; the overhead is likely to be within the noise of your
benchmark.**

Any work which easybench does once-per-sample is ignored (this is the purpose of the linear
regression technique described above). However, work which is done once-per-iteration *will* be
counted in the final times.

- In the case of
`bench`

this amounts to incrementing the loop counter and copying the return value. - In the case of
`bench_env`

, we also do a lookup into a big vector in order to get the environment for that iteration. - If you compile your program unoptimised, there may be additional overhead.

The cost of the above operations depend on the details of your benchmark; namely: (1) how large is the return value? and (2) does the benchmark evict the environment vector from the CPU cache? In practice, these criteria are only satisfied by longer-running benchmarks, making these effects hard to measure.

If you have concerns about the results you're seeing, please take a look at the inner loop of
`bench_env`

. The whole library `cloc`

s in at under 100 lines of code, so it's pretty easy
to read.

## Caveat 2: Sufficient data

**TL;DR: Measurements are unreliable when code takes too long (> 1 ms) to run.**

Each benchmark collects data for 1 second. This means that in order to collect a statistically significant amount of data, your code should run much faster than this.

When inspecting the results, make sure things look statistically significant. In particular:

- Make sure the number of samples is big enough. More than 100 is probably OK.
- Make sure the R² isn't suspiciously low. It's easy to achieve a high R² value when the number of samples is small, so unfortunately the definition of "suspiciously low" depends on how many samples were taken. As a rule of thumb, expect values greater than 0.99.

## Caveat 3: Pure functions

**TL;DR: 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.

## Bonus caveat: Black box

The function which easybench uses to trick the optimiser (`black_box`

) is stolen from bencher,
which states:

NOTE: We don't have a proper black box in stable Rust. This is a workaround implementation, that may have a too big performance overhead, depending on operation, or it may fail to properly avoid having code optimized out. It is good enough that it is used by default.

## Structs

Stats |
Statistics for a benchmark run. |

## Functions

bench |
Run a benchmark. |

bench_env |
Run a benchmark with an environment. |