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//! A statistics-driven micro-benchmarking library written in Rust. //! //! # Features //! //! - Can benchmark native (Rust) programs and also foreign (C, Python, Go, etc) programs //! - Easily benchmark a program under several inputs //! - Easy migration from `std::test::Bencher` to `criterion::Bencher` //! - Plots! #![deny(missing_docs)] #![feature(test)] #[macro_use] extern crate log; extern crate itertools; extern crate rustc_serialize; extern crate criterion_plot as simplot; extern crate criterion_stats as stats; extern crate test; mod analysis; mod estimate; mod format; mod fs; mod kde; mod plot; mod program; mod report; mod routine; use std::default::Default; use std::iter::IntoIterator; use std::process::Command; use std::time::{Duration, Instant}; use std::{fmt, mem}; use rustc_serialize::json; use std::fs::File; use std::io::Read; use std::path::Path; use estimate::{Distributions, Estimates}; /// Representing a function to benchmark together with a name of that function. /// Used together with `bench_functions` to represent one out of multiple functions /// under benchmark. pub struct Fun<I: fmt::Display> { n: String, f: Box<FnMut(&mut Bencher, &I)>, } impl<I> Fun<I> where I: fmt::Display { /// Create a new `Fun` given a name and a closure pub fn new<F>(name: &str, f: F) -> Fun<I> where F: FnMut(&mut Bencher, &I) + 'static { Fun { n: name.to_owned(), f: Box::new(f), } } } /// 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. #[derive(Clone, Copy)] pub struct Bencher { iters: u64, elapsed: Duration, } impl Bencher { /// 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 /// /// ```rust,no_run /// # use std::time::Instant; /// # fn routine() {} /// # let iters = 4_000_000; /// 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`. /// /// ```text /// elapsed = Instant::now + iters * (routine + mem::drop(O) + Range::next) /// ``` /// /// NOTE `Bencher` will choose `iters` to make `Instant::now` negligible compared to the product /// on the RHS. pub fn iter<O, R>(&mut self, mut routine: R) where R: FnMut() -> O, { let start = Instant::now(); for _ in 0..self.iters { test::black_box(routine()); } self.elapsed = start.elapsed(); } /// 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 /// /// ```rust,no_run /// extern crate criterion; /// /// use criterion::Bencher; /// /// fn create_scrambled_data() -> Vec<u64> { /// # vec![] /// // ... /// } /// /// // 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)) /// } /// /// # fn main() {} /// ``` /// /// # Timing loop /// /// ```rust,no_run /// # use std::time::{Instant, Duration}; /// # use std::mem; /// # fn setup() {} /// # fn routine(input: ()) {} /// # let iters = 4_000_000; /// 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 /// /// Note that `Bencher` also times the `Instant::now` function. Criterion will warn you (NOTE /// not yet implemented) if the runtime of `routine` is small or comparable to the runtime of /// `Instant::now` as this indicates that the measurement is useless. /// /// ``` text /// elapsed = iters * (Instant::now + routine) /// ``` pub fn iter_with_setup<I, O, S, R>(&mut self, mut setup: S, mut routine: R) where S: FnMut() -> I, R: FnMut(I) -> O { self.elapsed = Duration::from_secs(0); for _ in 0..self.iters { let input = setup(); let start = Instant::now(); let output = test::black_box(routine(test::black_box(input))); let elapsed = start.elapsed(); mem::drop(output); self.elapsed += elapsed; } } /// 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 /// /// ```rust,no_run /// # use std::mem; /// # use std::time::Instant; /// # let iters = 4_000_000; /// # fn routine() {} /// 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 /// /// ``` text /// elapsed = Instant::now + iters * (routine + Vec::push + Range::next) /// ``` /// /// NOTE `Bencher` will pick an `iters` that makes `Instant::now` negligible compared to the /// product on the RHS. `Vec::push` will never incur in a re-allocation because its capacity is /// pre-allocated. pub fn iter_with_large_drop<O, R>(&mut self, mut routine: R) where R: FnMut() -> O { let mut outputs = Vec::with_capacity(self.iters as usize); let start = Instant::now(); for _ in 0..self.iters { outputs.push(test::black_box(routine())); } self.elapsed = start.elapsed(); mem::drop(outputs); } /// 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 /// /// ```rust,no_run /// # use std::time::Instant; /// # fn setup() {} /// # fn routine(input: ()) {} /// # let iters = 4_000_000; /// let inputs: Vec<()> = (0..iters).map(|_| setup()).collect(); /// let start = Instant::now(); /// /// for input in inputs { /// routine(input); /// } /// /// let elapsed = start.elapsed(); /// ``` /// /// # Timing model /// /// ``` text /// elapsed = Instant::now + iters * (routine + vec::IntoIter::next) /// ``` pub fn iter_with_large_setup<I, S, R>(&mut self, mut setup: S, mut routine: R) where S: FnMut() -> I, R: FnMut(I) { let inputs = (0..self.iters).map(|_| setup()).collect::<Vec<_>>(); let start = Instant::now(); for input in inputs { routine(test::black_box(input)); } self.elapsed = start.elapsed(); } } /// The benchmark manager /// /// `Criterion` lets you configure and execute benchmarks /// /// Each benchmark consists of four phases: /// /// - **Warm-up**: The routine is repeatedly executed, to let the CPU/OS/JIT/interpreter adapt to /// the new load /// - **Measurement**: The routine is repeatedly executed, and timing information is collected into /// a sample /// - **Analysis**: The sample is analyzed and distiled into meaningful statistics that get /// reported to stdout, stored in files, and plotted /// - **Comparison**: The current sample is compared with the sample obtained in the previous /// benchmark. If a significant regression in performance is spotted, `Criterion` will trigger a /// task panic pub struct Criterion { confidence_level: f64, measurement_time: Duration, noise_threshold: f64, nresamples: usize, plotting: Plotting, sample_size: usize, significance_level: f64, warm_up_time: Duration, } impl Default for Criterion { /// Creates a benchmark manager with the following default settings: /// /// - Sample size: 100 measurements /// - Warm-up time: 1 s /// - Measurement time: 1 s /// - Bootstrap size: 100 000 resamples /// - Noise threshold: 0.01 (1%) /// - Confidence level: 0.95 /// - Significance level: 0.05 /// - Plotting: enabled (if gnuplot is available) fn default() -> Criterion { let plotting = if simplot::version().is_ok() { Plotting::Enabled } else { println!("Gnuplot not found, disabling plotting"); Plotting::NotAvailable }; Criterion { confidence_level: 0.95, measurement_time: Duration::new(5, 0), noise_threshold: 0.01, nresamples: 100_000, sample_size: 100, plotting: plotting, significance_level: 0.05, warm_up_time: Duration::new(3, 0), } } } impl Criterion { /// Changes the size of the sample /// /// A bigger sample should yield more accurate results, if paired with a "sufficiently" large /// measurement time, on the other hand, it also increases the analysis time /// /// # Panics /// /// Panics if set to zero pub fn sample_size(&mut self, n: usize) -> &mut Criterion { assert!(n > 0); self.sample_size = n; self } /// Changes the warm up time /// /// # Panics /// /// Panics if the input duration is zero pub fn warm_up_time(&mut self, dur: Duration) -> &mut Criterion { assert!(dur.to_nanos() > 0); self.warm_up_time = dur; self } /// Changes the measurement time /// /// With a longer time, the measurement will become more resilient to transitory peak loads /// caused by external programs /// /// **Note**: If the measurement time is too "low", Criterion will automatically increase it /// /// # Panics /// /// Panics if the input duration in zero pub fn measurement_time(&mut self, dur: Duration) -> &mut Criterion { assert!(dur.to_nanos() > 0); self.measurement_time = dur; self } /// Changes the number of resamples /// /// Number of resamples to use for the /// [bootstrap](http://en.wikipedia.org/wiki/Bootstrapping_(statistics)#Case_resampling) /// /// A larger number of resamples reduces the random sampling errors, which are inherent to the /// bootstrap method, but also increases the analysis time /// /// # Panics /// /// Panics if the number of resamples is set to zero pub fn nresamples(&mut self, n: usize) -> &mut Criterion { assert!(n > 0); self.nresamples = n; self } /// Changes the noise threshold /// /// This threshold is used to decide if an increase of `X%` in the execution time is considered /// significant or should be flagged as noise /// /// *Note:* A value of `0.02` is equivalent to `2%` /// /// # Panics /// /// Panics is the threshold is set to a negative value pub fn noise_threshold(&mut self, threshold: f64) -> &mut Criterion { assert!(threshold >= 0.0); self.noise_threshold = threshold; self } /// Changes the confidence level /// /// The confidence level is used to calculate the /// [confidence intervals](https://en.wikipedia.org/wiki/Confidence_interval) of the estimated /// statistics /// /// # Panics /// /// Panics if the confidence level is set to a value outside the `(0, 1)` range pub fn confidence_level(&mut self, cl: f64) -> &mut Criterion { assert!(cl > 0.0 && cl < 1.0); self.confidence_level = cl; self } /// Changes the [significance level](https://en.wikipedia.org/wiki/Statistical_significance) /// /// The significance level is used for /// [hypothesis testing](https://en.wikipedia.org/wiki/Statistical_hypothesis_testing) /// /// # Panics /// /// Panics if the significance level is set to a value outside the `(0, 1)` range pub fn significance_level(&mut self, sl: f64) -> &mut Criterion { assert!(sl > 0.0 && sl < 1.0); self.significance_level = sl; self } /// Enables plotting pub fn with_plots(&mut self) -> &mut Criterion { match self.plotting { Plotting::NotAvailable => {}, _ => self.plotting = Plotting::Enabled, } self } /// Disabled plotting pub fn without_plots(&mut self) -> &mut Criterion { match self.plotting { Plotting::NotAvailable => {}, _ => self.plotting = Plotting::Disabled, } self } /// Checks if plotting is possible pub fn can_plot(&self) -> bool { match self.plotting { Plotting::NotAvailable => false, _ => true, } } /// Benchmarks a function /// /// The function under test must follow the setup - bench - teardown pattern: /// /// ```rust,no_run /// use self::criterion::{Bencher, Criterion}; /// /// fn routine(b: &mut Bencher) { /// // Setup (construct data, allocate memory, etc) /// /// b.iter(|| { /// // Code to benchmark goes here /// }) /// /// // Teardown (free resources) /// } /// /// Criterion::default().bench_function("routine", routine); /// ``` pub fn bench_function<F>(&mut self, id: &str, f: F) -> &mut Criterion where F: FnMut(&mut Bencher), { analysis::function(id, f, self); self } /// Benchmarks multiple functions /// /// All functions get the same input and are compared with the other implementations. /// Works similar to `bench`, but with multiple functions. /// /// ``` rust,no_run /// # use self::criterion::{Bencher, Criterion, Fun}; /// # fn seq_fib(i: &u32) {} /// # fn par_fib(i: &u32) {} /// /// fn bench_seq_fib(b: &mut Bencher, i: &u32) { /// b.iter(|| { /// seq_fib(i); /// }); /// } /// /// fn bench_par_fib(b: &mut Bencher, i: &u32) { /// b.iter(|| { /// par_fib(i); /// }); /// } /// /// let sequential_fib = Fun::new("Sequential", bench_seq_fib); /// let parallel_fib = Fun::new("Parallel", bench_par_fib); /// let funs = vec![sequential_fib, parallel_fib]; /// /// Criterion::default().bench_functions("Fibonacci", funs, &14); /// ``` pub fn bench_functions<I>(&mut self, id: &str, funs: Vec<Fun<I>>, input: &I) -> &mut Criterion where I: fmt::Display { analysis::functions(id, funs, input, self); self } /// Benchmarks a function under various inputs /// /// This is a convenience method to execute several related benchmarks. Each benchmark will /// receive the id: `${id}/${input}`. /// /// ```rust,no_run /// use self::criterion::{Bencher, Criterion}; /// /// Criterion::default() /// .bench_function_over_inputs("from_elem", |b: &mut Bencher, &&size: &&usize| { /// b.iter(|| vec![0u8; size]); /// }, &[1024, 2048, 4096]); /// ``` pub fn bench_function_over_inputs<I, F>( &mut self, id: &str, f: F, inputs: I, ) -> &mut Criterion where I: IntoIterator, I::Item: fmt::Display, F: FnMut(&mut Bencher, &I::Item), { analysis::function_over_inputs(id, f, inputs, self); self } /// Benchmarks an external program /// /// The external program must conform to the following specification: /// /// ```rust,no_run /// # use std::io::{self, BufRead}; /// # use std::time::Instant; /// # use std::time::Duration; /// # trait DurationExt { fn to_nanos(&self) -> u64 { 0 } } /// # impl DurationExt for Duration {} /// /// fn main() { /// let stdin = io::stdin(); /// let ref mut stdin = stdin.lock(); /// /// // For each line in stdin /// for line in stdin.lines() { /// // Parse line as the number of iterations /// let iters: u64 = line.unwrap().trim().parse().unwrap(); /// /// // Setup /// /// // Benchmark /// let start = Instant::now(); /// // Execute the routine "iters" times /// for _ in 0..iters { /// // Code to benchmark goes here /// } /// let elapsed = start.elapsed(); /// /// // Teardown /// /// // Report elapsed time in nanoseconds to stdout /// println!("{}", elapsed.to_nanos()); /// } /// } /// ``` pub fn bench_program(&mut self, id: &str, mut program: Command) -> &mut Criterion { analysis::program(id, &mut program, self); self } /// Benchmarks an external program under various inputs /// /// This is a convenience method to execute several related benchmarks. Each benchmark will /// receive the id: `${id}/${input}`. pub fn bench_program_over_inputs<I, F>( &mut self, id: &str, program: F, inputs: I, ) -> &mut Criterion where F: FnMut() -> Command, I: IntoIterator, I::Item: fmt::Display, { analysis::program_over_inputs(id, program, inputs, self); self } /// Summarize the results stored under the `.criterion/${id}` folder /// /// *Note:* The `bench_with_inputs` and `bench_program_with_inputs` functions internally call /// the `summarize` method pub fn summarize(&mut self, id: &str) -> &mut Criterion { analysis::summarize(id, self); self } } enum Plotting { Disabled, Enabled, NotAvailable, } impl Plotting { fn is_enabled(&self) -> bool { match *self { Plotting::Enabled => true, _ => false, } } } trait DurationExt { fn to_nanos(&self) -> u64; } const NANOS_PER_SEC: u64 = 1_000_000_000; impl DurationExt for Duration { fn to_nanos(&self) -> u64 { self.as_secs() * NANOS_PER_SEC + u64::from(self.subsec_nanos()) } } // TODO make private again #[doc(hidden)] #[derive(Clone, Copy, PartialEq, RustcDecodable, RustcEncodable)] pub struct ConfidenceInterval { confidence_level: f64, lower_bound: f64, upper_bound: f64, } // TODO make private again #[doc(hidden)] #[derive(Clone, Copy, PartialEq, RustcDecodable, RustcEncodable)] pub struct Estimate { /// The confidence interval for this estimate pub confidence_interval: ConfidenceInterval, /// pub point_estimate: f64, /// The standard error of this estimate pub standard_error: f64, } impl Estimate { fn new(distributions: &Distributions, points: &[f64], cl: f64) -> Estimates { distributions.iter().zip(points.iter()).map(|((&statistic, distribution), &point)| { let (lb, ub) = distribution.confidence_interval(cl); (statistic, Estimate { confidence_interval: ConfidenceInterval { confidence_level: cl, lower_bound: lb, upper_bound: ub, }, point_estimate: point, standard_error: distribution.std_dev(None), }) }).collect() } fn load(path: &Path) -> Option<Estimates> { let mut string = String::new(); match File::open(path) { Err(_) => None, Ok(mut f) => match f.read_to_string(&mut string) { Err(_) => None, Ok(_) => match json::decode(&string) { Err(_) => None, Ok(estimates) => Some(estimates), }, } } } }