/*!
This is a wasm32 browser friendly fork of [easybench]

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-wasm 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).

[easybench]: https://docs.rs/easybench
[criterion]: https://hackage.haskell.org/package/criterion

``` ignore
// example marked as "not tested" because doc tests don't run in wasm32
use easybench_wasm::{bench,bench_env};
use web_sys::console;

# fn fib(_: usize) -> usize { 0 }
#
// Simple benchmarks are performed with `bench`.
console::log_1(&format!("fib 200: {}", bench(|| fib(200) )).into());
console::log_1(&format!("fib 500: {}", bench(|| fib(500) )).into());

// If a function needs to mutate some state, use `bench_env`.
console::log_1(&format!("reverse: {}", bench_env(vec![0;100], |xs| xs.reverse() )).into());
console::log_1(&format!("sort:    {}", bench_env(vec![0;100], |xs| xs.sort()    )).into());
```

Running the above yields the following results:

```none
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](#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-wasm 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](#bonus-caveat-black-box).
* 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.

[`bench`]: fn.bench.html
[`bench_env`]: fn.bench_env.html

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`][source]. The whole library `cloc`s in at under 100 lines of code, so it's pretty easy
to read.

[source]: ../src/easybench-wasm/lib.rs.html#280-285

## 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:

``` ignore
// example marked as "not tested" because doc tests don't run in wasm32
# use easybench_wasm::{bench,bench_env};
#
# fn fib(_: usize) -> usize { 0 }
#
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
# let _ = (fib_1, fib_2, fib_3);
```

The results are a little surprising:

```none
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-wasm 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-wasm uses to trick the optimiser (`black_box`) is stolen from [bencher],
which [states]:

[bencher]: https://docs.rs/bencher/
[states]: https://docs.rs/bencher/0.1.2/bencher/fn.black_box.html

> 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.
*/

extern crate web_sys;
extern crate humantime;

use std::f64;
use std::fmt::{self,Display,Formatter};
use std::time::Duration;

// Each time we take a sample we increase the number of iterations:
//      iters = ITER_SCALE_FACTOR ^ sample_no
const ITER_SCALE_FACTOR: f64 = 1.1;

// We try to spend this many seconds (roughly) in total on each benchmark.
const BENCH_TIME_LIMIT_SECS: f64 = 1.0;

/// Statistics for a benchmark run.
#[derive(Debug, PartialEq, Clone)]
pub struct Stats {
    /// The time, in nanoseconds, per iteration. If the benchmark generated fewer than 2 samples in
    /// the allotted time then this will be NaN.
    pub ns_per_iter: f64,
    /// The coefficient of determination, R².
    ///
    /// This is an indication of how noisy the benchmark was, where 1 is good and 0 is bad. Be
    /// suspicious of values below 0.9.
    pub goodness_of_fit: f64,
    /// How many times the benchmarked code was actually run.
    pub iterations: usize,
    /// How many samples were taken (ie. how many times we allocated the environment and measured
    /// the time).
    pub samples: usize,
}

impl Display for Stats {
    fn fmt(&self, f: &mut Formatter) -> fmt::Result {
        if self.ns_per_iter.is_nan() {
            write!(f, "Only generated {} sample(s) - we can't fit a regression line to that! \
            Try making your benchmark faster.", self.samples)
        } else {
            let per_iter: humantime::Duration = Duration::from_nanos(self.ns_per_iter as u64).into();
            let per_iter = format!("{}", per_iter);
            write!(f, "{:>11} (R²={:.3}, {} iterations in {} samples)",
                per_iter, self.goodness_of_fit, self.iterations, self.samples)
        }
    }
}

/// Run a benchmark.
///
/// The return value of `f` is not used, but we trick the optimiser into thinking we're going to
/// use it. Make sure to return enough information to prevent the optimiser from eliminating code
/// from your benchmark! (See the module docs for more.)
pub fn bench<F, O>(f: F) -> Stats where F: Fn() -> O {
    bench_env_limit(BENCH_TIME_LIMIT_SECS, (), |_| f() )
}

/// Run a benchmark with an environment.
///
/// The value `env` is a clonable prototype for the "benchmark environment". Each iteration
/// receives a freshly-cloned mutable copy of this environment. The time taken to clone the
/// environment is not included in the results.
///
/// Nb: it's very possible that we will end up allocating many (>10,000) copies of `env` at the
/// same time. Probably best to keep it small.
///
/// See [bench](fn.bench.html) and the module docs for more.
///
/// ## Overhead
///
/// Every iteration, `bench_env` performs a lookup into a big vector in order to get the
/// environment for that iteration. If your benchmark is memory-intensive then this could, in the
/// worst case, amount to a systematic cache-miss (ie. this vector would have to be fetched from
/// DRAM at the start of every iteration). In this case the results could be affected by a hundred
/// nanoseconds. This is a worst-case scenario however, and I haven't actually been able to trigger
/// it in practice... but it's good to be aware of the possibility.
pub fn bench_env<F, I, O>(env: I, f: F) -> Stats where F: Fn(&mut I) -> O, I: Clone {
    bench_env_limit_ref(BENCH_TIME_LIMIT_SECS, env, f )
}

/// Run a benchmark, specifying the run time limit.
///
/// See [bench](fn.bench.html)
pub fn bench_limit<F, O>(time_limit_secs: f64, f: F) -> Stats where F: Fn() -> O {
    bench_env_limit(time_limit_secs, (), |_| f() )
}

/// Run a benchmark with an environment, specifying the run time limit.
///
/// See [bench_env](fn.bench_env.html)
pub fn bench_env_limit<F, I, O>(time_limit_secs: f64, env: I, f: F) -> Stats where F: Fn(I) -> O, I: Clone {
    run_bench(time_limit_secs, env,
        |xs| {
            for i in xs.into_iter() {
                pretend_to_use(f(i));             // Run the code and pretend to use the output
            }
        }
    )
}

/// Run a benchmark with an environment, specifying the run time limit. The function to bench takes a mutable reference
/// to the `env` parameter instead of a struct.
///
/// See [bench_env](fn.bench_env.html)
pub fn bench_env_limit_ref<F, I, O>(time_limit_secs: f64, env: I, f: F) -> Stats where F: Fn(&mut I) -> O, I: Clone {
    run_bench(time_limit_secs, env,
        |mut xs| {
            for i in xs.iter_mut() {
                pretend_to_use(f(i));             // Run the code and pretend to use the output
            }
        }
    )
}

fn run_bench<F, I>(time_limit_secs: f64, env: I, f: F) -> Stats where F: Fn(Vec<I>), I: Clone {
    let mut data = Vec::new();
    let performance = web_sys::window()
        .expect("'Window' not available. This package should only be used in wasm32 inside a browser or headless browser")
        .performance()
        .expect("'Performance' not available. This package should only be used in wasm32 inside a browser or headless browser");

    let bench_start = performance.now(); // The time we started the benchmark (not used in results)

    // Collect data until BENCH_TIME_LIMIT_SECS is reached.
    while (performance.now() - bench_start) < (time_limit_secs * 1000_f64) {
        let iters = ITER_SCALE_FACTOR.powi(data.len() as i32).round() as usize;
        let xs = vec![env.clone();iters]; // Prepare the environments - one per iteration
        let iter_start = performance.now();      // Start the clock
        f(xs);
        let time = performance.now() - iter_start;
        data.push((iters, time));
    }

    // If the first iter in a sample is consistently slow, that's fine - that's why we do the
    // linear regression. If the first sample is slower than the rest, however, that's not fine.
    // Therefore, we discard the first sample as a cache-warming exercise.
    data.remove(0);

    // Compute some stats
    let (grad, r2) = regression(&data[..]);
    Stats {
        ns_per_iter: grad,
        goodness_of_fit: r2,
        iterations: data.iter().map(|&(x,_)| x).sum(),
        samples: data.len(),
    }
}

/// Compute the OLS linear regression line for the given data set, returning the line's gradient
/// and R². Requires at least 2 samples.
//
// Overflows:
//
// * sum(x * x): num_samples <= 0.5 * log_k (1 + 2 ^ 64 (FACTOR - 1))
fn regression(data: &[(usize, f64)]) -> (f64, f64) {
    if data.len() < 2 {
        return (f64::NAN, f64::NAN);
    }

    let data: Vec<(u64, u64)> = data.iter().map(|&(x,y)| (x as u64, as_nanos(y))).collect();
    let n = data.len() as f64;
    let nxbar  = data.iter().map(|&(x,_)| x  ).sum::<u64>(); // iter_time > 5e-11 ns
    let nybar  = data.iter().map(|&(_,y)| y  ).sum::<u64>(); // TIME_LIMIT < 2 ^ 64 ns
    let nxxbar = data.iter().map(|&(x,_)| x*x).sum::<u64>(); // num_iters < 13_000_000_000
    let nyybar = data.iter().map(|&(_,y)| y*y).sum::<u64>(); // TIME_LIMIT < 4.3 e9 ns
    let nxybar = data.iter().map(|&(x,y)| x*y).sum::<u64>();
    let ncovar = nxybar as f64 - ((nxbar * nybar) as f64 / n);
    let nxvar  = nxxbar as f64 - ((nxbar * nxbar) as f64 / n);
    let nyvar  = nyybar as f64 - ((nybar * nybar) as f64 / n);
    let gradient = ncovar / nxvar;
    let r2 = (ncovar * ncovar) / (nxvar * nyvar);
    (gradient, r2)
}

fn as_nanos(x: f64) -> u64 {
    (x * 1000_f64).round() as u64
}

// Stolen from `bencher`, where it's known as `black_box`.
//
// 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.
//
// A function that is opaque to the optimizer, to allow benchmarks to
// pretend to use outputs to assist in avoiding dead-code
// elimination.
fn pretend_to_use<T>(dummy: T) -> T {
    unsafe {
        let ret = ::std::ptr::read_volatile(&dummy);
        ::std::mem::forget(dummy);
        ret
    }
}