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// Copyright 2016 Kyle Mayes // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. //! A micro-benchmarking library. //! //! # Overview //! //! `microbench` uses linear regression to estimate the execution time of code //! segments. For example, the following table might represent data collected by //! `microbench` about a code segment. //! //! | Iterations | Time (ns) | //! |------------|-----------| //! | 1 | 19 | //! | 2 | 25 | //! | 3 | 37 | //! | 4 | 47 | //! | 5 | 56 | //! //! `microbench` of course takes many more than 5 samples and the number of //! iterations grows geometrically rather than linearly, but the idea remains //! the same. After collecting data like this, `microbench` uses ordinary least //! squares (OLS) linear regression to estimate the actual execution time of the //! code segment. Using OLS with the above data would yield an estimated //! execution time of `9.6` nanoseconds with a goodness of fit (R²) of `0.992`. //! //! # Example //! //! ``` //! use microbench::{self, Options}; //! //! fn fibonacci_iterative(n: u64) -> u64 { //! let (mut x, mut y, mut z) = (0, 1, 1); //! for _ in 0..n { x = y; y = z; z = x + y; } //! x //! } //! //! fn fibonacci_recursive(n: u64) -> u64 { //! if n < 2 { //! n //! } else { //! fibonacci_recursive(n - 2) + fibonacci_recursive(n - 1) //! } //! } //! //! let options = Options::default(); //! microbench::bench(&options, "iterative_16", || fibonacci_iterative(16)); //! microbench::bench(&options, "recursive_16", || fibonacci_recursive(16)); //! ``` //! //! Example output: //! //! ```console //! iterative_16 (5.0s) ... 281.733 ns/iter (0.998 R²) //! recursive_16 (5.0s) ... 9_407.020 ns/iter (0.997 R²) //! ``` #![cfg_attr(feature="nightly", feature(test))] #![warn(missing_copy_implementations, missing_debug_implementations, missing_docs)] mod utility; pub mod statistics; pub mod time; use std::cmp; use std::mem; use std::time::{Duration}; use crate::statistics::{Model}; use crate::time::{Nanoseconds, Stopwatch}; use crate::utility::{GeometricSequence, black_box, format_number}; /// The maximum number of benchmark sample iterations. const ITERATIONS: u64 = 1_000_000_000_000_000; /// A number of bytes. #[derive(Copy, Clone, Debug, PartialEq, Eq, PartialOrd, Ord)] pub struct Bytes(pub u64); impl Bytes { /// Returns the number of bytes in the supplied number of kibibytes (2¹⁰ bytes). pub fn kibibytes(kibibytes: u64) -> Self { Bytes(kibibytes * 1024) } /// Returns the number of bytes in the supplied number of mebibytes (2²⁰ bytes). pub fn mebibytes(mebibytes: u64) -> Self { Bytes(mebibytes * 1024 * 1024) } /// Returns the number of bytes in the supplied number of gibibytes (2³⁰ bytes). pub fn gibibytes(gibibytes: u64) -> Self { Bytes(gibibytes * 1024 * 1024 * 1024) } } /// A set of benchmarking options. #[derive(Copy, Clone, Debug)] pub struct Options { factor: f64, memory: Bytes, time: Nanoseconds<u64>, } impl Options { /// Sets the geometric growth factor for benchmark sample iterations. /// /// **Default:** `1.01` pub fn factor(mut self, factor: f64) -> Self { self.factor = factor; self } /// Sets the maximum amount of memory benchmarks will allocate. /// /// **Default:** `Bytes::mebibytes(512)` pub fn memory(mut self, memory: Bytes) -> Self { self.memory = memory; self } /// Sets the maximum amount of time benchmarks will run for. /// /// **Default:** `Duration::new(5, 0)` pub fn time(mut self, time: Duration) -> Self { self.time = time.into(); self } } impl Default for Options { fn default() -> Self { let factor = 1.01; let memory = Bytes::mebibytes(512); let time = Duration::new(5, 0).into(); Options { factor, memory, time } } } /// A sample of the execution time of a function. #[derive(Copy, Clone, Debug)] pub struct Sample { /// The number of times the function was executed. pub iterations: u64, /// The number of nanoseconds that elapsed while executing the function. pub elapsed: Nanoseconds<u64>, } /// A statistical analysis of a set of execution time samples. #[derive(Copy, Clone, Debug)] pub struct Analysis { /// The y-intercept of the simple linear regression model function. pub alpha: Nanoseconds<f64>, /// The slope of the simple linear regression model function. pub beta: Nanoseconds<f64>, /// The goodness of fit of the simple linear regression model function. pub r2: f64, } impl Analysis { /// Returns a new analysis for the supplied samples. fn new(samples: &[Sample]) -> Self { let Model { alpha, beta, r2 } = samples.iter() .map(|m| (m.iterations as f64, m.elapsed.0 as f64)) .collect::<Model>(); Self { alpha: Nanoseconds(alpha), beta: Nanoseconds(beta), r2 } } } /// Benchmarks the supplied function and prints the results. pub fn bench<T>(options: &Options, name: &str, f: impl FnMut() -> T) { bench_impl(name, move || measure(options, f)); } /// Benchmarks the supplied function ignoring drop time and prints the results. /// /// See [`measure_drop`](fn.measure_drop.html) for more information. pub fn bench_drop<T>(options: &Options, name: &str, f: impl FnMut() -> T) { bench_impl(name, move || measure_drop(options, f)); } /// Benchmarks the supplied function ignoring setup time and prints the results. /// /// See [`measure_setup`](fn.measure_setup.html) for more information. pub fn bench_setup<I, T>( options: &Options, name: &str, setup: impl FnMut() -> I, f: impl FnMut(I) -> T, ) { bench_impl(name, move || measure_setup(options, setup, f)); } /// Measures the execution time of the supplied function. pub fn measure<T>( options: &Options, mut f: impl FnMut() -> T ) -> Vec<Sample> { measure_impl(options, |iterations| { let stopwatch = Stopwatch::default(); for _ in 0..iterations { retain(f()); } Some(stopwatch.elapsed()) }) } /// Measures the execution time of the supplied function ignoring drop time. /// /// This function does not include the time it takes to drop the values returned /// by the supplied function in the measurements. This can be useful when you /// want to exclude the running time of a slow implementation of `Drop` from /// your benchmark. However, it should be noted that this function introduces a /// very small amount of overhead which will be reflected in the measurements /// (typically of the order of a few nanoseconds). /// /// **Warning:** This function can potentially allocate very large amounts of /// memory. The `memory` option controls the maximum amount of memory this /// function is allowed to allocate. pub fn measure_drop<T>( options: &Options, mut f: impl FnMut() -> T ) -> Vec<Sample> { measure_impl(options, |iterations| { let size = cmp::max(1, mem::size_of::<T>() as u64); if options.memory < Bytes(iterations * size) { return None; } let mut outputs = Vec::with_capacity(iterations as usize); let stopwatch = Stopwatch::default(); for _ in 0..iterations { outputs.push(f()); } let elapsed = stopwatch.elapsed(); mem::drop(outputs); Some(elapsed) }) } /// Measures the execution time of the supplied function ignoring setup time. /// /// This function does not include the time it takes to execute the setup /// function in the measurements. This can be useful when you want to exclude /// the running time of some non-trivial setup which is needed for every /// execution of the supplied function. However, it should be noted that this /// function introduces a very small amount of overhead which will be reflected /// in the measurements (typically of the order of a few nanoseconds). /// /// **Warning:** This function can potentially allocate very large amounts of /// memory. The `memory` option controls the maximum amount of memory this /// function is allowed to allocate. pub fn measure_setup<I, T>( options: &Options, mut setup: impl FnMut() -> I, mut f: impl FnMut(I) -> T, ) -> Vec<Sample> { measure_impl(options, |iterations| { let size = cmp::max(1, mem::size_of::<I>() as u64); if options.memory < Bytes(iterations * size) { return None; } let inputs = retain((0..iterations).map(|_| setup()).collect::<Vec<_>>()); let stopwatch = Stopwatch::default(); for input in inputs { retain(f(input)); } Some(stopwatch.elapsed()) }) } /// A function that prevents the optimizer from eliminating the supplied value. /// /// This function may not operate correctly or may have poor performance on the /// stable and beta channels of Rust. If you are using a nightly release of /// Rust, enable the `nightly` crate feature to enable a superior implementation /// of this function. pub fn retain<T>(value: T) -> T { black_box(value) } /// Prints an analysis of the samples produced by the supplied function. fn bench_impl(name: &str, f: impl FnOnce() -> Vec<Sample>) { let stopwatch = Stopwatch::default(); let samples = f(); let elapsed = stopwatch.elapsed(); let analysis = Analysis::new(&samples); let prefix = format!("{} ({}) ...", name, elapsed); if samples.len() < 2 || analysis.beta.0 < 0.0 { println!("{:<32} {:>15}", prefix, " not enough samples"); } else { let beta = format_number(analysis.beta.0, 3, '_'); println!("{:<32} {:>15} ns/iter ({:.3} R²)", prefix, beta, analysis.r2); } } /// Collects samples produced by the supplied function. fn measure_impl( options: &Options, mut f: impl FnMut(u64) -> Option<Nanoseconds<u64>> ) -> Vec<Sample> { let stopwatch = Stopwatch::default(); GeometricSequence::new(1, options.factor) .take_while(|i| *i <= ITERATIONS && stopwatch.elapsed() < options.time) .filter_map(|i| Some(Sample { iterations: i, elapsed: f(i)? })) .collect() }