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diff --git a/src/bench/nanobench.h b/src/bench/nanobench.h new file mode 100644 index 0000000000..c5379e7fd4 --- /dev/null +++ b/src/bench/nanobench.h @@ -0,0 +1,3225 @@ +// __ _ _______ __ _ _____ ______ _______ __ _ _______ _ _ +// | \ | |_____| | \ | | | |_____] |______ | \ | | |_____| +// | \_| | | | \_| |_____| |_____] |______ | \_| |_____ | | +// +// Microbenchmark framework for C++11/14/17/20 +// https://github.com/martinus/nanobench +// +// Licensed under the MIT License <http://opensource.org/licenses/MIT>. +// SPDX-License-Identifier: MIT +// Copyright (c) 2019-2020 Martin Ankerl <martin.ankerl@gmail.com> +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files (the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions: +// +// The above copyright notice and this permission notice shall be included in all +// copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +// SOFTWARE. + +#ifndef ANKERL_NANOBENCH_H_INCLUDED +#define ANKERL_NANOBENCH_H_INCLUDED + +// see https://semver.org/ +#define ANKERL_NANOBENCH_VERSION_MAJOR 4 // incompatible API changes +#define ANKERL_NANOBENCH_VERSION_MINOR 0 // backwards-compatible changes +#define ANKERL_NANOBENCH_VERSION_PATCH 0 // backwards-compatible bug fixes + +/////////////////////////////////////////////////////////////////////////////////////////////////// +// public facing api - as minimal as possible +/////////////////////////////////////////////////////////////////////////////////////////////////// + +#include <chrono> // high_resolution_clock +#include <cstring> // memcpy +#include <iosfwd> // for std::ostream* custom output target in Config +#include <string> // all names +#include <vector> // holds all results + +#define ANKERL_NANOBENCH(x) ANKERL_NANOBENCH_PRIVATE_##x() + +#define ANKERL_NANOBENCH_PRIVATE_CXX() __cplusplus +#define ANKERL_NANOBENCH_PRIVATE_CXX98() 199711L +#define ANKERL_NANOBENCH_PRIVATE_CXX11() 201103L +#define ANKERL_NANOBENCH_PRIVATE_CXX14() 201402L +#define ANKERL_NANOBENCH_PRIVATE_CXX17() 201703L + +#if ANKERL_NANOBENCH(CXX) >= ANKERL_NANOBENCH(CXX17) +# define ANKERL_NANOBENCH_PRIVATE_NODISCARD() [[nodiscard]] +#else +# define ANKERL_NANOBENCH_PRIVATE_NODISCARD() +#endif + +#if defined(__clang__) +# define ANKERL_NANOBENCH_PRIVATE_IGNORE_PADDED_PUSH() \ + _Pragma("clang diagnostic push") _Pragma("clang diagnostic ignored \"-Wpadded\"") +# define ANKERL_NANOBENCH_PRIVATE_IGNORE_PADDED_POP() _Pragma("clang diagnostic pop") +#else +# define ANKERL_NANOBENCH_PRIVATE_IGNORE_PADDED_PUSH() +# define ANKERL_NANOBENCH_PRIVATE_IGNORE_PADDED_POP() +#endif + +#if defined(__GNUC__) +# define ANKERL_NANOBENCH_PRIVATE_IGNORE_EFFCPP_PUSH() _Pragma("GCC diagnostic push") _Pragma("GCC diagnostic ignored \"-Weffc++\"") +# define ANKERL_NANOBENCH_PRIVATE_IGNORE_EFFCPP_POP() _Pragma("GCC diagnostic pop") +#else +# define ANKERL_NANOBENCH_PRIVATE_IGNORE_EFFCPP_PUSH() +# define ANKERL_NANOBENCH_PRIVATE_IGNORE_EFFCPP_POP() +#endif + +#if defined(ANKERL_NANOBENCH_LOG_ENABLED) +# include <iostream> +# define ANKERL_NANOBENCH_LOG(x) std::cout << __FUNCTION__ << "@" << __LINE__ << ": " << x << std::endl +#else +# define ANKERL_NANOBENCH_LOG(x) +#endif + +#if defined(__linux__) && !defined(ANKERL_NANOBENCH_DISABLE_PERF_COUNTERS) +# define ANKERL_NANOBENCH_PRIVATE_PERF_COUNTERS() 1 +#else +# define ANKERL_NANOBENCH_PRIVATE_PERF_COUNTERS() 0 +#endif + +#if defined(__clang__) +# define ANKERL_NANOBENCH_NO_SANITIZE(...) __attribute__((no_sanitize(__VA_ARGS__))) +#else +# define ANKERL_NANOBENCH_NO_SANITIZE(...) +#endif + +#if defined(_MSC_VER) +# define ANKERL_NANOBENCH_PRIVATE_NOINLINE() __declspec(noinline) +#else +# define ANKERL_NANOBENCH_PRIVATE_NOINLINE() __attribute__((noinline)) +#endif + +// workaround missing "is_trivially_copyable" in g++ < 5.0 +// See https://stackoverflow.com/a/31798726/48181 +#if defined(__GNUC__) && __GNUC__ < 5 +# define ANKERL_NANOBENCH_IS_TRIVIALLY_COPYABLE(...) __has_trivial_copy(__VA_ARGS__) +#else +# define ANKERL_NANOBENCH_IS_TRIVIALLY_COPYABLE(...) std::is_trivially_copyable<__VA_ARGS__>::value +#endif + +// declarations /////////////////////////////////////////////////////////////////////////////////// + +namespace ankerl { +namespace nanobench { + +using Clock = std::conditional<std::chrono::high_resolution_clock::is_steady, std::chrono::high_resolution_clock, + std::chrono::steady_clock>::type; +class Bench; +struct Config; +class Result; +class Rng; +class BigO; + +/** + * @brief Renders output from a mustache-like template and benchmark results. + * + * The templating facility here is heavily inspired by [mustache - logic-less templates](https://mustache.github.io/). + * It adds a few more features that are necessary to get all of the captured data out of nanobench. Please read the + * excellent [mustache manual](https://mustache.github.io/mustache.5.html) to see what this is all about. + * + * nanobench output has two nested layers, *result* and *measurement*. Here is a hierarchy of the allowed tags: + * + * * `{{#result}}` Marks the begin of the result layer. Whatever comes after this will be instantiated as often as + * a benchmark result is available. Within it, you can use these tags: + * + * * `{{title}}` See Bench::title(). + * + * * `{{name}}` Benchmark name, usually directly provided with Bench::run(), but can also be set with Bench::name(). + * + * * `{{unit}}` Unit, e.g. `byte`. Defaults to `op`, see Bench::title(). + * + * * `{{batch}}` Batch size, see Bench::batch(). + * + * * `{{complexityN}}` Value used for asymptotic complexity calculation. See Bench::complexityN(). + * + * * `{{epochs}}` Number of epochs, see Bench::epochs(). + * + * * `{{clockResolution}}` Accuracy of the clock, i.e. what's the smallest time possible to measure with the clock. + * For modern systems, this can be around 20 ns. This value is automatically determined by nanobench at the first + * benchmark that is run, and used as a static variable throughout the application's runtime. + * + * * `{{clockResolutionMultiple}}` Configuration multiplier for `clockResolution`. See Bench::clockResolutionMultiple(). + * This is the target runtime for each measurement (epoch). That means the more accurate your clock is, the faster + * will be the benchmark. Basing the measurement's runtime on the clock resolution is the main reason why nanobench is so fast. + * + * * `{{maxEpochTime}}` Configuration for a maximum time each measurement (epoch) is allowed to take. Note that at least + * a single iteration will be performed, even when that takes longer than maxEpochTime. See Bench::maxEpochTime(). + * + * * `{{minEpochTime}}` Minimum epoch time, usually not set. See Bench::minEpochTime(). + * + * * `{{minEpochIterations}}` See Bench::minEpochIterations(). + * + * * `{{epochIterations}}` See Bench::epochIterations(). + * + * * `{{warmup}}` Number of iterations used before measuring starts. See Bench::warmup(). + * + * * `{{relative}}` True or false, depending on the setting you have used. See Bench::relative(). + * + * Apart from these tags, it is also possible to use some mathematical operations on the measurement data. The operations + * are of the form `{{command(name)}}`. Currently `name` can be one of `elapsed`, `iterations`. If performance counters + * are available (currently only on current Linux systems), you also have `pagefaults`, `cpucycles`, + * `contextswitches`, `instructions`, `branchinstructions`, and `branchmisses`. All the measuers (except `iterations`) are + * provided for a single iteration (so `elapsed` is the time a single iteration took). The following tags are available: + * + * * `{{median(<name>>)}}` Calculate median of a measurement data set, e.g. `{{median(elapsed)}}`. + * + * * `{{average(<name>)}}` Average (mean) calculation. + * + * * `{{medianAbsolutePercentError(<name>)}}` Calculates MdAPE, the Median Absolute Percentage Error. The MdAPE is an excellent + * metric for the variation of measurements. It is more robust to outliers than the + * [Mean absolute percentage error (M-APE)](https://en.wikipedia.org/wiki/Mean_absolute_percentage_error). + * @f[ + * \mathrm{medianAbsolutePercentError}(e) = \mathrm{median}\{| \frac{e_i - \mathrm{median}\{e\}}{e_i}| \} + * @f] + * E.g. for *elapsed*: First, @f$ \mathrm{median}\{elapsed\} @f$ is calculated. This is used to calculate the absolute percentage + * error to this median for each measurement, as in @f$ | \frac{e_i - \mathrm{median}\{e\}}{e_i}| @f$. All these results + * are sorted, and the middle value is chosen as the median absolute percent error. + * + * This measurement is a bit hard to interpret, but it is very robust against outliers. E.g. a value of 5% means that half of the + * measurements deviate less than 5% from the median, and the other deviate more than 5% from the median. + * + * * `{{sum(<name>)}}` Sums of all the measurements. E.g. `{{sum(iterations)}}` will give you the total number of iterations +* measured in this benchmark. + * + * * `{{minimum(<name>)}}` Minimum of all measurements. + * + * * `{{maximum(<name>)}}` Maximum of all measurements. + * + * * `{{sumProduct(<first>, <second>)}}` Calculates the sum of the products of corresponding measures: + * @f[ + * \mathrm{sumProduct}(a,b) = \sum_{i=1}^{n}a_i\cdot b_i + * @f] + * E.g. to calculate total runtime of the benchmark, you multiply iterations with elapsed time for each measurement, and + * sum these results up: + * `{{sumProduct(iterations, elapsed)}}`. + * + * * `{{#measurement}}` To access individual measurement results, open the begin tag for measurements. + * + * * `{{elapsed}}` Average elapsed time per iteration, in seconds. + * + * * `{{iterations}}` Number of iterations in the measurement. The number of iterations will fluctuate due + * to some applied randomness, to enhance accuracy. + * + * * `{{pagefaults}}` Average number of pagefaults per iteration. + * + * * `{{cpucycles}}` Average number of CPU cycles processed per iteration. + * + * * `{{contextswitches}}` Average number of context switches per iteration. + * + * * `{{instructions}}` Average number of retired instructions per iteration. + * + * * `{{branchinstructions}}` Average number of branches executed per iteration. + * + * * `{{branchmisses}}` Average number of branches that were missed per iteration. + * + * * `{{/measurement}}` Ends the measurement tag. + * + * * `{{/result}}` Marks the end of the result layer. This is the end marker for the template part that will be instantiated + * for each benchmark result. + * + * + * For the layer tags *result* and *measurement* you additionally can use these special markers: + * + * * ``{{#-first}}`` - Begin marker of a template that will be instantiated *only for the first* entry in the layer. Use is only + * allowed between the begin and end marker of the layer allowed. So between ``{{#result}}`` and ``{{/result}}``, or between + * ``{{#measurement}}`` and ``{{/measurement}}``. Finish the template with ``{{/-first}}``. + * + * * ``{{^-first}}`` - Begin marker of a template that will be instantiated *for each except the first* entry in the layer. This, + * this is basically the inversion of ``{{#-first}}``. Use is only allowed between the begin and end marker of the layer allowed. + * So between ``{{#result}}`` and ``{{/result}}``, or between ``{{#measurement}}`` and ``{{/measurement}}``. + * + * * ``{{/-first}}`` - End marker for either ``{{#-first}}`` or ``{{^-first}}``. + * + * * ``{{#-last}}`` - Begin marker of a template that will be instantiated *only for the last* entry in the layer. Use is only + * allowed between the begin and end marker of the layer allowed. So between ``{{#result}}`` and ``{{/result}}``, or between + * ``{{#measurement}}`` and ``{{/measurement}}``. Finish the template with ``{{/-last}}``. + * + * * ``{{^-last}}`` - Begin marker of a template that will be instantiated *for each except the last* entry in the layer. This, + * this is basically the inversion of ``{{#-last}}``. Use is only allowed between the begin and end marker of the layer allowed. + * So between ``{{#result}}`` and ``{{/result}}``, or between ``{{#measurement}}`` and ``{{/measurement}}``. + * + * * ``{{/-last}}`` - End marker for either ``{{#-last}}`` or ``{{^-last}}``. + * + @verbatim embed:rst + + For an overview of all the possible data you can get out of nanobench, please see the tutorial at :ref:`tutorial-template-json`. + + The templates that ship with nanobench are: + + * :cpp:func:`templates::csv() <ankerl::nanobench::templates::csv()>` + * :cpp:func:`templates::json() <ankerl::nanobench::templates::json()>` + * :cpp:func:`templates::htmlBoxplot() <ankerl::nanobench::templates::htmlBoxplot()>` + + @endverbatim + * + * @param mustacheTemplate The template. + * @param bench Benchmark, containing all the results. + * @param out Output for the generated output. + */ +void render(char const* mustacheTemplate, Bench const& bench, std::ostream& out); + +/** + * Same as render(char const* mustacheTemplate, Bench const& bench, std::ostream& out), but for when + * you only have results available. + * + * @param mustacheTemplate The template. + * @param results All the results to be used for rendering. + * @param out Output for the generated output. + */ +void render(char const* mustacheTemplate, std::vector<Result> const& results, std::ostream& out); + +// Contains mustache-like templates +namespace templates { + +/*! + @brief CSV data for the benchmark results. + + Generates a comma-separated values dataset. First line is the header, each following line is a summary of each benchmark run. + + @verbatim embed:rst + See the tutorial at :ref:`tutorial-template-csv` for an example. + @endverbatim + */ +char const* csv() noexcept; + +/*! + @brief HTML output that uses plotly to generate an interactive boxplot chart. See the tutorial for an example output. + + The output uses only the elapsed time, and displays each epoch as a single dot. + @verbatim embed:rst + See the tutorial at :ref:`tutorial-template-html` for an example. + @endverbatim + + @see ankerl::nanobench::render() + */ +char const* htmlBoxplot() noexcept; + +/*! + @brief Template to generate JSON data. + + The generated JSON data contains *all* data that has been generated. All times are as double values, in seconds. The output can get + quite large. + @verbatim embed:rst + See the tutorial at :ref:`tutorial-template-json` for an example. + @endverbatim + */ +char const* json() noexcept; + +} // namespace templates + +namespace detail { + +template <typename T> +struct PerfCountSet; + +class IterationLogic; +class PerformanceCounters; + +#if ANKERL_NANOBENCH(PERF_COUNTERS) +class LinuxPerformanceCounters; +#endif + +} // namespace detail +} // namespace nanobench +} // namespace ankerl + +// definitions //////////////////////////////////////////////////////////////////////////////////// + +namespace ankerl { +namespace nanobench { +namespace detail { + +template <typename T> +struct PerfCountSet { + T pageFaults{}; + T cpuCycles{}; + T contextSwitches{}; + T instructions{}; + T branchInstructions{}; + T branchMisses{}; +}; + +} // namespace detail + +ANKERL_NANOBENCH(IGNORE_PADDED_PUSH) +struct Config { + // actual benchmark config + std::string mBenchmarkTitle = "benchmark"; + std::string mBenchmarkName = "noname"; + std::string mUnit = "op"; + double mBatch = 1.0; + double mComplexityN = -1.0; + size_t mNumEpochs = 11; + size_t mClockResolutionMultiple = static_cast<size_t>(1000); + std::chrono::nanoseconds mMaxEpochTime = std::chrono::milliseconds(100); + std::chrono::nanoseconds mMinEpochTime{}; + uint64_t mMinEpochIterations{1}; + uint64_t mEpochIterations{0}; // If not 0, run *exactly* these number of iterations per epoch. + uint64_t mWarmup = 0; + std::ostream* mOut = nullptr; + bool mShowPerformanceCounters = true; + bool mIsRelative = false; + + Config(); + ~Config(); + Config& operator=(Config const&); + Config& operator=(Config&&); + Config(Config const&); + Config(Config&&) noexcept; +}; +ANKERL_NANOBENCH(IGNORE_PADDED_POP) + +// Result returned after a benchmark has finished. Can be used as a baseline for relative(). +ANKERL_NANOBENCH(IGNORE_PADDED_PUSH) +class Result { +public: + enum class Measure : size_t { + elapsed, + iterations, + pagefaults, + cpucycles, + contextswitches, + instructions, + branchinstructions, + branchmisses, + _size + }; + + explicit Result(Config const& benchmarkConfig); + + ~Result(); + Result& operator=(Result const&); + Result& operator=(Result&&); + Result(Result const&); + Result(Result&&) noexcept; + + // adds new measurement results + // all values are scaled by iters (except iters...) + void add(Clock::duration totalElapsed, uint64_t iters, detail::PerformanceCounters const& pc); + + ANKERL_NANOBENCH(NODISCARD) Config const& config() const noexcept; + + ANKERL_NANOBENCH(NODISCARD) double median(Measure m) const; + ANKERL_NANOBENCH(NODISCARD) double medianAbsolutePercentError(Measure m) const; + ANKERL_NANOBENCH(NODISCARD) double average(Measure m) const; + ANKERL_NANOBENCH(NODISCARD) double sum(Measure m) const noexcept; + ANKERL_NANOBENCH(NODISCARD) double sumProduct(Measure m1, Measure m2) const noexcept; + ANKERL_NANOBENCH(NODISCARD) double minimum(Measure m) const noexcept; + ANKERL_NANOBENCH(NODISCARD) double maximum(Measure m) const noexcept; + + ANKERL_NANOBENCH(NODISCARD) bool has(Measure m) const noexcept; + ANKERL_NANOBENCH(NODISCARD) double get(size_t idx, Measure m) const; + ANKERL_NANOBENCH(NODISCARD) bool empty() const noexcept; + ANKERL_NANOBENCH(NODISCARD) size_t size() const noexcept; + + // Finds string, if not found, returns _size. + static Measure fromString(std::string const& str); + +private: + Config mConfig{}; + std::vector<std::vector<double>> mNameToMeasurements{}; +}; +ANKERL_NANOBENCH(IGNORE_PADDED_POP) + +/** + * An extremely fast random generator. Currently, this implements *RomuDuoJr*, developed by Mark Overton. Source: + * http://www.romu-random.org/ + * + * RomuDuoJr is extremely fast and provides reasonable good randomness. Not enough for large jobs, but definitely + * good enough for a benchmarking framework. + * + * * Estimated capacity: @f$ 2^{51} @f$ bytes + * * Register pressure: 4 + * * State size: 128 bits + * + * This random generator is a drop-in replacement for the generators supplied by ``<random>``. It is not + * cryptographically secure. It's intended purpose is to be very fast so that benchmarks that make use + * of randomness are not distorted too much by the random generator. + * + * Rng also provides a few non-standard helpers, optimized for speed. + */ +class Rng final { +public: + /** + * @brief This RNG provides 64bit randomness. + */ + using result_type = uint64_t; + + static constexpr uint64_t(min)(); + static constexpr uint64_t(max)(); + + /** + * As a safety precausion, we don't allow copying. Copying a PRNG would mean you would have two random generators that produce the + * same sequence, which is generally not what one wants. Instead create a new rng with the default constructor Rng(), which is + * automatically seeded from `std::random_device`. If you really need a copy, use copy(). + */ + Rng(Rng const&) = delete; + + /** + * Same as Rng(Rng const&), we don't allow assignment. If you need a new Rng create one with the default constructor Rng(). + */ + Rng& operator=(Rng const&) = delete; + + // moving is ok + Rng(Rng&&) noexcept = default; + Rng& operator=(Rng&&) noexcept = default; + ~Rng() noexcept = default; + + /** + * @brief Creates a new Random generator with random seed. + * + * Instead of a default seed (as the random generators from the STD), this properly seeds the random generator from + * `std::random_device`. It guarantees correct seeding. Note that seeding can be relatively slow, depending on the source of + * randomness used. So it is best to create a Rng once and use it for all your randomness purposes. + */ + Rng(); + + /*! + Creates a new Rng that is seeded with a specific seed. Each Rng created from the same seed will produce the same randomness + sequence. This can be useful for deterministic behavior. + + @verbatim embed:rst + .. note:: + + The random algorithm might change between nanobench releases. Whenever a faster and/or better random + generator becomes available, I will switch the implementation. + @endverbatim + + As per the Romu paper, this seeds the Rng with splitMix64 algorithm and performs 10 initial rounds for further mixing up of the + internal state. + + @param seed The 64bit seed. All values are allowed, even 0. + */ + explicit Rng(uint64_t seed) noexcept; + Rng(uint64_t x, uint64_t y) noexcept; + + /** + * Creates a copy of the Rng, thus the copy provides exactly the same random sequence as the original. + */ + ANKERL_NANOBENCH(NODISCARD) Rng copy() const noexcept; + + /** + * @brief Produces a 64bit random value. This should be very fast, thus it is marked as inline. In my benchmark, this is ~46 times + * faster than `std::default_random_engine` for producing 64bit random values. It seems that the fastest std contender is + * `std::mt19937_64`. Still, this RNG is 2-3 times as fast. + * + * @return uint64_t The next 64 bit random value. + */ + inline uint64_t operator()() noexcept; + + // This is slightly biased. See + + /** + * Generates a random number between 0 and range (excluding range). + * + * The algorithm only produces 32bit numbers, and is slightly biased. The effect is quite small unless your range is close to the + * maximum value of an integer. It is possible to correct the bias with rejection sampling (see + * [here](https://lemire.me/blog/2016/06/30/fast-random-shuffling/), but this is most likely irrelevant in practices for the + * purposes of this Rng. + * + * See Daniel Lemire's blog post [A fast alternative to the modulo + * reduction](https://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/) + * + * @param range Upper exclusive range. E.g a value of 3 will generate random numbers 0, 1, 2. + * @return uint32_t Generated random values in range [0, range(. + */ + inline uint32_t bounded(uint32_t range) noexcept; + + // random double in range [0, 1( + // see http://prng.di.unimi.it/ + + /** + * Provides a random uniform double value between 0 and 1. This uses the method described in [Generating uniform doubles in the + * unit interval](http://prng.di.unimi.it/), and is extremely fast. + * + * @return double Uniformly distributed double value in range [0,1(, excluding 1. + */ + inline double uniform01() noexcept; + + /** + * Shuffles all entries in the given container. Although this has a slight bias due to the implementation of bounded(), this is + * preferable to `std::shuffle` because it is over 5 times faster. See Daniel Lemire's blog post [Fast random + * shuffling](https://lemire.me/blog/2016/06/30/fast-random-shuffling/). + * + * @param container The whole container will be shuffled. + */ + template <typename Container> + void shuffle(Container& container) noexcept; + +private: + static constexpr uint64_t rotl(uint64_t x, unsigned k) noexcept; + + uint64_t mX; + uint64_t mY; +}; + +/** + * @brief Main entry point to nanobench's benchmarking facility. + * + * It holds configuration and results from one or more benchmark runs. Usually it is used in a single line, where the object is + * constructed, configured, and then a benchmark is run. E.g. like this: + * + * ankerl::nanobench::Bench().unit("byte").batch(1000).run("random fluctuations", [&] { + * // here be the benchmark code + * }); + * + * In that example Bench() constructs the benchmark, it is then configured with unit() and batch(), and after configuration a + * benchmark is executed with run(). Once run() has finished, it prints the result to `std::cout`. It would also store the results + * in the Bench instance, but in this case the object is immediately destroyed so it's not available any more. + */ +ANKERL_NANOBENCH(IGNORE_PADDED_PUSH) +class Bench { +public: + /** + * @brief Creates a new benchmark for configuration and running of benchmarks. + */ + Bench(); + + Bench(Bench&& other); + Bench& operator=(Bench&& other); + Bench(Bench const& other); + Bench& operator=(Bench const& other); + ~Bench() noexcept; + + /*! + @brief Repeatedly calls `op()` based on the configuration, and performs measurements. + + This call is marked with `noinline` to prevent the compiler to optimize beyond different benchmarks. This can have quite a big + effect on benchmark accuracy. + + @verbatim embed:rst + .. note:: + + Each call to your lambda must have a side effect that the compiler can't possibly optimize it away. E.g. add a result to an + externally defined number (like `x` in the above example), and finally call `doNotOptimizeAway` on the variables the compiler + must not remove. You can also use :cpp:func:`ankerl::nanobench::doNotOptimizeAway` directly in the lambda, but be aware that + this has a small overhead. + + @endverbatim + + @tparam Op The code to benchmark. + */ + template <typename Op> + ANKERL_NANOBENCH(NOINLINE) + Bench& run(char const* benchmarkName, Op&& op); + + template <typename Op> + ANKERL_NANOBENCH(NOINLINE) + Bench& run(std::string const& benchmarkName, Op&& op); + + /** + * @brief Same as run(char const* benchmarkName, Op op), but instead uses the previously set name. + * @tparam Op The code to benchmark. + */ + template <typename Op> + ANKERL_NANOBENCH(NOINLINE) + Bench& run(Op&& op); + + /** + * @brief Title of the benchmark, will be shown in the table header. Changing the title will start a new markdown table. + * + * @param benchmarkTitle The title of the benchmark. + */ + Bench& title(char const* benchmarkTitle); + Bench& title(std::string const& benchmarkTitle); + ANKERL_NANOBENCH(NODISCARD) std::string const& title() const noexcept; + + /// Name of the benchmark, will be shown in the table row. + Bench& name(char const* benchmarkName); + Bench& name(std::string const& benchmarkName); + ANKERL_NANOBENCH(NODISCARD) std::string const& name() const noexcept; + + /** + * @brief Sets the batch size. + * + * E.g. number of processed byte, or some other metric for the size of the processed data in each iteration. If you benchmark + * hashing of a 1000 byte long string and want byte/sec as a result, you can specify 1000 as the batch size. + * + * @tparam T Any input type is internally cast to `double`. + * @param b batch size + */ + template <typename T> + Bench& batch(T b) noexcept; + ANKERL_NANOBENCH(NODISCARD) double batch() const noexcept; + + /** + * @brief Sets the operation unit. + * + * Defaults to "op". Could be e.g. "byte" for string processing. This is used for the table header, e.g. to show `ns/byte`. Use + * singular (*byte*, not *bytes*). A change clears the currently collected results. + * + * @param unit The unit name. + */ + Bench& unit(char const* unit); + Bench& unit(std::string const& unit); + ANKERL_NANOBENCH(NODISCARD) std::string const& unit() const noexcept; + + /** + * @brief Set the output stream where the resulting markdown table will be printed to. + * + * The default is `&std::cout`. You can disable all output by setting `nullptr`. + * + * @param outstream Pointer to output stream, can be `nullptr`. + */ + Bench& output(std::ostream* outstream) noexcept; + ANKERL_NANOBENCH(NODISCARD) std::ostream* output() const noexcept; + + /** + * Modern processors have a very accurate clock, being able to measure as low as 20 nanoseconds. This is the main trick nanobech to + * be so fast: we find out how accurate the clock is, then run the benchmark only so often that the clock's accuracy is good enough + * for accurate measurements. + * + * The default is to run one epoch for 1000 times the clock resolution. So for 20ns resolution and 11 epochs, this gives a total + * runtime of + * + * @f[ + * 20ns * 1000 * 11 \approx 0.2ms + * @f] + * + * To be precise, nanobench adds a 0-20% random noise to each evaluation. This is to prevent any aliasing effects, and further + * improves accuracy. + * + * Total runtime will be higher though: Some initial time is needed to find out the target number of iterations for each epoch, and + * there is some overhead involved to start & stop timers and calculate resulting statistics and writing the output. + * + * @param multiple Target number of times of clock resolution. Usually 1000 is a good compromise between runtime and accuracy. + */ + Bench& clockResolutionMultiple(size_t multiple) noexcept; + ANKERL_NANOBENCH(NODISCARD) size_t clockResolutionMultiple() const noexcept; + + /** + * @brief Controls number of epochs, the number of measurements to perform. + * + * The reported result will be the median of evaluation of each epoch. The higher you choose this, the more + * deterministic the result be and outliers will be more easily removed. Also the `err%` will be more accurate the higher this + * number is. Note that the `err%` will not necessarily decrease when number of epochs is increased. But it will be a more accurate + * representation of the benchmarked code's runtime stability. + * + * Choose the value wisely. In practice, 11 has been shown to be a reasonable choice between runtime performance and accuracy. + * This setting goes hand in hand with minEpocIterations() (or minEpochTime()). If you are more interested in *median* runtime, you + * might want to increase epochs(). If you are more interested in *mean* runtime, you might want to increase minEpochIterations() + * instead. + * + * @param numEpochs Number of epochs. + */ + Bench& epochs(size_t numEpochs) noexcept; + ANKERL_NANOBENCH(NODISCARD) size_t epochs() const noexcept; + + /** + * @brief Upper limit for the runtime of each epoch. + * + * As a safety precausion if the clock is not very accurate, we can set an upper limit for the maximum evaluation time per + * epoch. Default is 100ms. At least a single evaluation of the benchmark is performed. + * + * @see minEpochTime(), minEpochIterations() + * + * @param t Maximum target runtime for a single epoch. + */ + Bench& maxEpochTime(std::chrono::nanoseconds t) noexcept; + ANKERL_NANOBENCH(NODISCARD) std::chrono::nanoseconds maxEpochTime() const noexcept; + + /** + * @brief Minimum time each epoch should take. + * + * Default is zero, so we are fully relying on clockResolutionMultiple(). In most cases this is exactly what you want. If you see + * that the evaluation is unreliable with a high `err%`, you can increase either minEpochTime() or minEpochIterations(). + * + * @see maxEpochTime(), minEpochIterations() + * + * @param t Minimum time each epoch should take. + */ + Bench& minEpochTime(std::chrono::nanoseconds t) noexcept; + ANKERL_NANOBENCH(NODISCARD) std::chrono::nanoseconds minEpochTime() const noexcept; + + /** + * @brief Sets the minimum number of iterations each epoch should take. + * + * Default is 1, and we rely on clockResolutionMultiple(). If the `err%` is high and you want a more smooth result, you might want + * to increase the minimum number or iterations, or increase the minEpochTime(). + * + * @see minEpochTime(), maxEpochTime(), minEpochIterations() + * + * @param numIters Minimum number of iterations per epoch. + */ + Bench& minEpochIterations(uint64_t numIters) noexcept; + ANKERL_NANOBENCH(NODISCARD) uint64_t minEpochIterations() const noexcept; + + /** + * Sets exactly the number of iterations for each epoch. Ignores all other epoch limits. This forces nanobench to use exactly + * the given number of iterations for each epoch, not more and not less. Default is 0 (disabled). + * + * @param numIters Exact number of iterations to use. Set to 0 to disable. + */ + Bench& epochIterations(uint64_t numIters) noexcept; + ANKERL_NANOBENCH(NODISCARD) uint64_t epochIterations() const noexcept; + + /** + * @brief Sets a number of iterations that are initially performed without any measurements. + * + * Some benchmarks need a few evaluations to warm up caches / database / whatever access. Normally this should not be needed, since + * we show the median result so initial outliers will be filtered away automatically. If the warmup effect is large though, you + * might want to set it. Default is 0. + * + * @param numWarmupIters Number of warmup iterations. + */ + Bench& warmup(uint64_t numWarmupIters) noexcept; + ANKERL_NANOBENCH(NODISCARD) uint64_t warmup() const noexcept; + + /** + * @brief Marks the next run as the baseline. + * + * Call `relative(true)` to mark the run as the baseline. Successive runs will be compared to this run. It is calculated by + * + * @f[ + * 100\% * \frac{baseline}{runtime} + * @f] + * + * * 100% means it is exactly as fast as the baseline + * * >100% means it is faster than the baseline. E.g. 200% means the current run is twice as fast as the baseline. + * * <100% means it is slower than the baseline. E.g. 50% means it is twice as slow as the baseline. + * + * See the tutorial section "Comparing Results" for example usage. + * + * @param isRelativeEnabled True to enable processing + */ + Bench& relative(bool isRelativeEnabled) noexcept; + ANKERL_NANOBENCH(NODISCARD) bool relative() const noexcept; + + /** + * @brief Enables/disables performance counters. + * + * On Linux nanobench has a powerful feature to use performance counters. This enables counting of retired instructions, count + * number of branches, missed branches, etc. On default this is enabled, but you can disable it if you don't need that feature. + * + * @param showPerformanceCounters True to enable, false to disable. + */ + Bench& performanceCounters(bool showPerformanceCounters) noexcept; + ANKERL_NANOBENCH(NODISCARD) bool performanceCounters() const noexcept; + + /** + * @brief Retrieves all benchmark results collected by the bench object so far. + * + * Each call to run() generates a Result that is stored within the Bench instance. This is mostly for advanced users who want to + * see all the nitty gritty detials. + * + * @return All results collected so far. + */ + ANKERL_NANOBENCH(NODISCARD) std::vector<Result> const& results() const noexcept; + + /*! + @verbatim embed:rst + + Convenience shortcut to :cpp:func:`ankerl::nanobench::doNotOptimizeAway`. + + @endverbatim + */ + template <typename Arg> + Bench& doNotOptimizeAway(Arg&& arg); + + /*! + @verbatim embed:rst + + Sets N for asymptotic complexity calculation, so it becomes possible to calculate `Big O + <https://en.wikipedia.org/wiki/Big_O_notation>`_ from multiple benchmark evaluations. + + Use :cpp:func:`ankerl::nanobench::Bench::complexityBigO` when the evaluation has finished. See the tutorial + :ref:`asymptotic-complexity` for details. + + @endverbatim + + @tparam T Any type is cast to `double`. + @param b Length of N for the next benchmark run, so it is possible to calculate `bigO`. + */ + template <typename T> + Bench& complexityN(T b) noexcept; + ANKERL_NANOBENCH(NODISCARD) double complexityN() const noexcept; + + /*! + Calculates [Big O](https://en.wikipedia.org/wiki/Big_O_notation>) of the results with all preconfigured complexity functions. + Currently these complexity functions are fitted into the benchmark results: + + @f$ \mathcal{O}(1) @f$, + @f$ \mathcal{O}(n) @f$, + @f$ \mathcal{O}(\log{}n) @f$, + @f$ \mathcal{O}(n\log{}n) @f$, + @f$ \mathcal{O}(n^2) @f$, + @f$ \mathcal{O}(n^3) @f$. + + If we e.g. evaluate the complexity of `std::sort`, this is the result of `std::cout << bench.complexityBigO()`: + + ``` + | coefficient | err% | complexity + |--------------:|-------:|------------ + | 5.08935e-09 | 2.6% | O(n log n) + | 6.10608e-08 | 8.0% | O(n) + | 1.29307e-11 | 47.2% | O(n^2) + | 2.48677e-15 | 69.6% | O(n^3) + | 9.88133e-06 | 132.3% | O(log n) + | 5.98793e-05 | 162.5% | O(1) + ``` + + So in this case @f$ \mathcal{O}(n\log{}n) @f$ provides the best approximation. + + @verbatim embed:rst + See the tutorial :ref:`asymptotic-complexity` for details. + @endverbatim + @return Evaluation results, which can be printed or otherwise inspected. + */ + std::vector<BigO> complexityBigO() const; + + /** + * @brief Calculates bigO for a custom function. + * + * E.g. to calculate the mean squared error for @f$ \mathcal{O}(\log{}\log{}n) @f$, which is not part of the default set of + * complexityBigO(), you can do this: + * + * ``` + * auto logLogN = bench.complexityBigO("O(log log n)", [](double n) { + * return std::log2(std::log2(n)); + * }); + * ``` + * + * The resulting mean squared error can be printed with `std::cout << logLogN`. E.g. it prints something like this: + * + * ```text + * 2.46985e-05 * O(log log n), rms=1.48121 + * ``` + * + * @tparam Op Type of mapping operation. + * @param name Name for the function, e.g. "O(log log n)" + * @param op Op's operator() maps a `double` with the desired complexity function, e.g. `log2(log2(n))`. + * @return BigO Error calculation, which is streamable to std::cout. + */ + template <typename Op> + BigO complexityBigO(char const* name, Op op) const; + + template <typename Op> + BigO complexityBigO(std::string const& name, Op op) const; + + /*! + @verbatim embed:rst + + Convenience shortcut to :cpp:func:`ankerl::nanobench::render`. + + @endverbatim + */ + Bench& render(char const* templateContent, std::ostream& os); + + Bench& config(Config const& benchmarkConfig); + ANKERL_NANOBENCH(NODISCARD) Config const& config() const noexcept; + +private: + Config mConfig{}; + std::vector<Result> mResults{}; +}; +ANKERL_NANOBENCH(IGNORE_PADDED_POP) + +/** + * @brief Makes sure none of the given arguments are optimized away by the compiler. + * + * @tparam Arg Type of the argument that shouldn't be optimized away. + * @param arg The input that we mark as being used, even though we don't do anything with it. + */ +template <typename Arg> +void doNotOptimizeAway(Arg&& arg); + +namespace detail { + +#if defined(_MSC_VER) +void doNotOptimizeAwaySink(void const*); + +template <typename T> +void doNotOptimizeAway(T const& val); + +#else + +// see folly's Benchmark.h +template <typename T> +constexpr bool doNotOptimizeNeedsIndirect() { + using Decayed = typename std::decay<T>::type; + return !ANKERL_NANOBENCH_IS_TRIVIALLY_COPYABLE(Decayed) || sizeof(Decayed) > sizeof(long) || std::is_pointer<Decayed>::value; +} + +template <typename T> +typename std::enable_if<!doNotOptimizeNeedsIndirect<T>()>::type doNotOptimizeAway(T const& val) { + // NOLINTNEXTLINE(hicpp-no-assembler) + asm volatile("" ::"r"(val)); +} + +template <typename T> +typename std::enable_if<doNotOptimizeNeedsIndirect<T>()>::type doNotOptimizeAway(T const& val) { + // NOLINTNEXTLINE(hicpp-no-assembler) + asm volatile("" ::"m"(val) : "memory"); +} +#endif + +// internally used, but visible because run() is templated. +// Not movable/copy-able, so we simply use a pointer instead of unique_ptr. This saves us from +// having to include <memory>, and the template instantiation overhead of unique_ptr which is unfortunately quite significant. +ANKERL_NANOBENCH(IGNORE_EFFCPP_PUSH) +class IterationLogic { +public: + explicit IterationLogic(Bench const& config) noexcept; + ~IterationLogic(); + + ANKERL_NANOBENCH(NODISCARD) uint64_t numIters() const noexcept; + void add(std::chrono::nanoseconds elapsed, PerformanceCounters const& pc) noexcept; + void moveResultTo(std::vector<Result>& results) noexcept; + +private: + struct Impl; + Impl* mPimpl; +}; +ANKERL_NANOBENCH(IGNORE_EFFCPP_POP) + +ANKERL_NANOBENCH(IGNORE_PADDED_PUSH) +class PerformanceCounters { +public: + PerformanceCounters(PerformanceCounters const&) = delete; + PerformanceCounters& operator=(PerformanceCounters const&) = delete; + + PerformanceCounters(); + ~PerformanceCounters(); + + void beginMeasure(); + void endMeasure(); + void updateResults(uint64_t numIters); + + ANKERL_NANOBENCH(NODISCARD) PerfCountSet<uint64_t> const& val() const noexcept; + ANKERL_NANOBENCH(NODISCARD) PerfCountSet<bool> const& has() const noexcept; + +private: +#if ANKERL_NANOBENCH(PERF_COUNTERS) + LinuxPerformanceCounters* mPc = nullptr; +#endif + PerfCountSet<uint64_t> mVal{}; + PerfCountSet<bool> mHas{}; +}; +ANKERL_NANOBENCH(IGNORE_PADDED_POP) + +// Gets the singleton +PerformanceCounters& performanceCounters(); + +} // namespace detail + +class BigO { +public: + using RangeMeasure = std::vector<std::pair<double, double>>; + + template <typename Op> + static RangeMeasure mapRangeMeasure(RangeMeasure data, Op op) { + for (auto& rangeMeasure : data) { + rangeMeasure.first = op(rangeMeasure.first); + } + return data; + } + + static RangeMeasure collectRangeMeasure(std::vector<Result> const& results); + + template <typename Op> + BigO(char const* bigOName, RangeMeasure const& rangeMeasure, Op rangeToN) + : BigO(bigOName, mapRangeMeasure(rangeMeasure, rangeToN)) {} + + template <typename Op> + BigO(std::string const& bigOName, RangeMeasure const& rangeMeasure, Op rangeToN) + : BigO(bigOName, mapRangeMeasure(rangeMeasure, rangeToN)) {} + + BigO(char const* bigOName, RangeMeasure const& scaledRangeMeasure); + BigO(std::string const& bigOName, RangeMeasure const& scaledRangeMeasure); + ANKERL_NANOBENCH(NODISCARD) std::string const& name() const noexcept; + ANKERL_NANOBENCH(NODISCARD) double constant() const noexcept; + ANKERL_NANOBENCH(NODISCARD) double normalizedRootMeanSquare() const noexcept; + ANKERL_NANOBENCH(NODISCARD) bool operator<(BigO const& other) const noexcept; + +private: + std::string mName{}; + double mConstant{}; + double mNormalizedRootMeanSquare{}; +}; +std::ostream& operator<<(std::ostream& os, BigO const& bigO); +std::ostream& operator<<(std::ostream& os, std::vector<ankerl::nanobench::BigO> const& bigOs); + +} // namespace nanobench +} // namespace ankerl + +// implementation ///////////////////////////////////////////////////////////////////////////////// + +namespace ankerl { +namespace nanobench { + +constexpr uint64_t(Rng::min)() { + return 0; +} + +constexpr uint64_t(Rng::max)() { + return (std::numeric_limits<uint64_t>::max)(); +} + +ANKERL_NANOBENCH_NO_SANITIZE("integer") +uint64_t Rng::operator()() noexcept { + auto x = mX; + + mX = UINT64_C(15241094284759029579) * mY; + mY = rotl(mY - x, 27); + + return x; +} + +ANKERL_NANOBENCH_NO_SANITIZE("integer") +uint32_t Rng::bounded(uint32_t range) noexcept { + uint64_t r32 = static_cast<uint32_t>(operator()()); + auto multiresult = r32 * range; + return static_cast<uint32_t>(multiresult >> 32U); +} + +double Rng::uniform01() noexcept { + auto i = (UINT64_C(0x3ff) << 52U) | (operator()() >> 12U); + // can't use union in c++ here for type puning, it's undefined behavior. + // std::memcpy is optimized anyways. + double d; + std::memcpy(&d, &i, sizeof(double)); + return d - 1.0; +} + +template <typename Container> +void Rng::shuffle(Container& container) noexcept { + auto size = static_cast<uint32_t>(container.size()); + for (auto i = size; i > 1U; --i) { + using std::swap; + auto p = bounded(i); // number in [0, i) + swap(container[i - 1], container[p]); + } +} + +constexpr uint64_t Rng::rotl(uint64_t x, unsigned k) noexcept { + return (x << k) | (x >> (64U - k)); +} + +template <typename Op> +ANKERL_NANOBENCH_NO_SANITIZE("integer") +Bench& Bench::run(Op&& op) { + // It is important that this method is kept short so the compiler can do better optimizations/ inlining of op() + detail::IterationLogic iterationLogic(*this); + auto& pc = detail::performanceCounters(); + + while (auto n = iterationLogic.numIters()) { + pc.beginMeasure(); + Clock::time_point before = Clock::now(); + while (n-- > 0) { + op(); + } + Clock::time_point after = Clock::now(); + pc.endMeasure(); + pc.updateResults(iterationLogic.numIters()); + iterationLogic.add(after - before, pc); + } + iterationLogic.moveResultTo(mResults); + return *this; +} + +// Performs all evaluations. +template <typename Op> +Bench& Bench::run(char const* benchmarkName, Op&& op) { + name(benchmarkName); + return run(std::forward<Op>(op)); +} + +template <typename Op> +Bench& Bench::run(std::string const& benchmarkName, Op&& op) { + name(benchmarkName); + return run(std::forward<Op>(op)); +} + +template <typename Op> +BigO Bench::complexityBigO(char const* benchmarkName, Op op) const { + return BigO(benchmarkName, BigO::collectRangeMeasure(mResults), op); +} + +template <typename Op> +BigO Bench::complexityBigO(std::string const& benchmarkName, Op op) const { + return BigO(benchmarkName, BigO::collectRangeMeasure(mResults), op); +} + +// Set the batch size, e.g. number of processed bytes, or some other metric for the size of the processed data in each iteration. +// Any argument is cast to double. +template <typename T> +Bench& Bench::batch(T b) noexcept { + mConfig.mBatch = static_cast<double>(b); + return *this; +} + +// Sets the computation complexity of the next run. Any argument is cast to double. +template <typename T> +Bench& Bench::complexityN(T n) noexcept { + mConfig.mComplexityN = static_cast<double>(n); + return *this; +} + +// Convenience: makes sure none of the given arguments are optimized away by the compiler. +template <typename Arg> +Bench& Bench::doNotOptimizeAway(Arg&& arg) { + detail::doNotOptimizeAway(std::forward<Arg>(arg)); + return *this; +} + +// Makes sure none of the given arguments are optimized away by the compiler. +template <typename Arg> +void doNotOptimizeAway(Arg&& arg) { + detail::doNotOptimizeAway(std::forward<Arg>(arg)); +} + +namespace detail { + +#if defined(_MSC_VER) +template <typename T> +void doNotOptimizeAway(T const& val) { + doNotOptimizeAwaySink(&val); +} + +#endif + +} // namespace detail +} // namespace nanobench +} // namespace ankerl + +#if defined(ANKERL_NANOBENCH_IMPLEMENT) + +/////////////////////////////////////////////////////////////////////////////////////////////////// +// implementation part - only visible in .cpp +/////////////////////////////////////////////////////////////////////////////////////////////////// + +# include <algorithm> // sort, reverse +# include <atomic> // compare_exchange_strong in loop overhead +# include <cstdlib> // getenv +# include <cstring> // strstr, strncmp +# include <fstream> // ifstream to parse proc files +# include <iomanip> // setw, setprecision +# include <iostream> // cout +# include <numeric> // accumulate +# include <random> // random_device +# include <sstream> // to_s in Number +# include <stdexcept> // throw for rendering templates +# include <tuple> // std::tie +# if defined(__linux__) +# include <unistd.h> //sysconf +# endif +# if ANKERL_NANOBENCH(PERF_COUNTERS) +# include <map> // map + +# include <linux/perf_event.h> +# include <sys/ioctl.h> +# include <sys/syscall.h> +# include <unistd.h> +# endif + +// declarations /////////////////////////////////////////////////////////////////////////////////// + +namespace ankerl { +namespace nanobench { + +// helper stuff that is only intended to be used internally +namespace detail { + +struct TableInfo; + +// formatting utilities +namespace fmt { + +class NumSep; +class StreamStateRestorer; +class Number; +class MarkDownColumn; +class MarkDownCode; + +} // namespace fmt +} // namespace detail +} // namespace nanobench +} // namespace ankerl + +// definitions //////////////////////////////////////////////////////////////////////////////////// + +namespace ankerl { +namespace nanobench { + +uint64_t splitMix64(uint64_t& state) noexcept; + +namespace detail { + +// helpers to get double values +template <typename T> +inline double d(T t) noexcept { + return static_cast<double>(t); +} +inline double d(Clock::duration duration) noexcept { + return std::chrono::duration_cast<std::chrono::duration<double>>(duration).count(); +} + +// Calculates clock resolution once, and remembers the result +inline Clock::duration clockResolution() noexcept; + +} // namespace detail + +namespace templates { + +char const* csv() noexcept { + return R"DELIM("title";"name";"unit";"batch";"elapsed";"error %";"instructions";"branches";"branch misses";"total" +{{#result}}"{{title}}";"{{name}}";"{{unit}}";{{batch}};{{median(elapsed)}};{{medianAbsolutePercentError(elapsed)}};{{median(instructions)}};{{median(branchinstructions)}};{{median(branchmisses)}};{{sumProduct(iterations, elapsed)}} +{{/result}})DELIM"; +} + +char const* htmlBoxplot() noexcept { + return R"DELIM(<html> + +<head> + <script src="https://cdn.plot.ly/plotly-latest.min.js"></script> +</head> + +<body> + <div id="myDiv"></div> + <script> + var data = [ + {{#result}}{ + name: '{{name}}', + y: [{{#measurement}}{{elapsed}}{{^-last}}, {{/last}}{{/measurement}}], + }, + {{/result}} + ]; + var title = '{{title}}'; + + data = data.map(a => Object.assign(a, { boxpoints: 'all', pointpos: 0, type: 'box' })); + var layout = { title: { text: title }, showlegend: false, yaxis: { title: 'time per unit', rangemode: 'tozero', autorange: true } }; Plotly.newPlot('myDiv', data, layout, {responsive: true}); + </script> +</body> + +</html>)DELIM"; +} + +char const* json() noexcept { + return R"DELIM({ + "results": [ +{{#result}} { + "title": "{{title}}", + "name": "{{name}}", + "unit": "{{unit}}", + "batch": {{batch}}, + "complexityN": {{complexityN}}, + "epochs": {{epochs}}, + "clockResolution": {{clockResolution}}, + "clockResolutionMultiple": {{clockResolutionMultiple}}, + "maxEpochTime": {{maxEpochTime}}, + "minEpochTime": {{minEpochTime}}, + "minEpochIterations": {{minEpochIterations}}, + "epochIterations": {{epochIterations}}, + "warmup": {{warmup}}, + "relative": {{relative}}, + "median(elapsed)": {{median(elapsed)}}, + "medianAbsolutePercentError(elapsed)": {{medianAbsolutePercentError(elapsed)}}, + "median(instructions)": {{median(instructions)}}, + "medianAbsolutePercentError(instructions)": {{medianAbsolutePercentError(instructions)}}, + "median(cpucycles)": {{median(cpucycles)}}, + "median(contextswitches)": {{median(contextswitches)}}, + "median(pagefaults)": {{median(pagefaults)}}, + "median(branchinstructions)": {{median(branchinstructions)}}, + "median(branchmisses)": {{median(branchmisses)}}, + "totalTime": {{sumProduct(iterations, elapsed)}}, + "measurements": [ +{{#measurement}} { + "iterations": {{iterations}}, + "elapsed": {{elapsed}}, + "pagefaults": {{pagefaults}}, + "cpucycles": {{cpucycles}}, + "contextswitches": {{contextswitches}}, + "instructions": {{instructions}}, + "branchinstructions": {{branchinstructions}}, + "branchmisses": {{branchmisses}} + }{{^-last}},{{/-last}} +{{/measurement}} ] + }{{^-last}},{{/-last}} +{{/result}} ] +})DELIM"; +} + +ANKERL_NANOBENCH(IGNORE_PADDED_PUSH) +struct Node { + enum class Type { tag, content, section, inverted_section }; + + char const* begin; + char const* end; + std::vector<Node> children; + Type type; + + template <size_t N> + // NOLINTNEXTLINE(hicpp-avoid-c-arrays,modernize-avoid-c-arrays,cppcoreguidelines-avoid-c-arrays) + bool operator==(char const (&str)[N]) const noexcept { + return static_cast<size_t>(std::distance(begin, end) + 1) == N && 0 == strncmp(str, begin, N - 1); + } +}; +ANKERL_NANOBENCH(IGNORE_PADDED_POP) + +static std::vector<Node> parseMustacheTemplate(char const** tpl) { + std::vector<Node> nodes; + + while (true) { + auto begin = std::strstr(*tpl, "{{"); + auto end = begin; + if (begin != nullptr) { + begin += 2; + end = std::strstr(begin, "}}"); + } + + if (begin == nullptr || end == nullptr) { + // nothing found, finish node + nodes.emplace_back(Node{*tpl, *tpl + std::strlen(*tpl), std::vector<Node>{}, Node::Type::content}); + return nodes; + } + + nodes.emplace_back(Node{*tpl, begin - 2, std::vector<Node>{}, Node::Type::content}); + + // we found a tag + *tpl = end + 2; + switch (*begin) { + case '/': + // finished! bail out + return nodes; + + case '#': + nodes.emplace_back(Node{begin + 1, end, parseMustacheTemplate(tpl), Node::Type::section}); + break; + + case '^': + nodes.emplace_back(Node{begin + 1, end, parseMustacheTemplate(tpl), Node::Type::inverted_section}); + break; + + default: + nodes.emplace_back(Node{begin, end, std::vector<Node>{}, Node::Type::tag}); + break; + } + } +} + +static bool generateFirstLast(Node const& n, size_t idx, size_t size, std::ostream& out) { + bool matchFirst = n == "-first"; + bool matchLast = n == "-last"; + if (!matchFirst && !matchLast) { + return false; + } + + bool doWrite = false; + if (n.type == Node::Type::section) { + doWrite = (matchFirst && idx == 0) || (matchLast && idx == size - 1); + } else if (n.type == Node::Type::inverted_section) { + doWrite = (matchFirst && idx != 0) || (matchLast && idx != size - 1); + } + + if (doWrite) { + for (auto const& child : n.children) { + if (child.type == Node::Type::content) { + out.write(child.begin, std::distance(child.begin, child.end)); + } + } + } + return true; +} + +static bool matchCmdArgs(std::string const& str, std::vector<std::string>& matchResult) { + matchResult.clear(); + auto idxOpen = str.find('('); + auto idxClose = str.find(')', idxOpen); + if (idxClose == std::string::npos) { + return false; + } + + matchResult.emplace_back(str.substr(0, idxOpen)); + + // split by comma + matchResult.emplace_back(std::string{}); + for (size_t i = idxOpen + 1; i != idxClose; ++i) { + if (str[i] == ' ' || str[i] == '\t') { + // skip whitespace + continue; + } + if (str[i] == ',') { + // got a comma => new string + matchResult.emplace_back(std::string{}); + continue; + } + // no whitespace no comma, append + matchResult.back() += str[i]; + } + return true; +} + +static bool generateConfigTag(Node const& n, Config const& config, std::ostream& out) { + using detail::d; + + if (n == "title") { + out << config.mBenchmarkTitle; + return true; + } else if (n == "name") { + out << config.mBenchmarkName; + return true; + } else if (n == "unit") { + out << config.mUnit; + return true; + } else if (n == "batch") { + out << config.mBatch; + return true; + } else if (n == "complexityN") { + out << config.mComplexityN; + return true; + } else if (n == "epochs") { + out << config.mNumEpochs; + return true; + } else if (n == "clockResolution") { + out << d(detail::clockResolution()); + return true; + } else if (n == "clockResolutionMultiple") { + out << config.mClockResolutionMultiple; + return true; + } else if (n == "maxEpochTime") { + out << d(config.mMaxEpochTime); + return true; + } else if (n == "minEpochTime") { + out << d(config.mMinEpochTime); + return true; + } else if (n == "minEpochIterations") { + out << config.mMinEpochIterations; + return true; + } else if (n == "epochIterations") { + out << config.mEpochIterations; + return true; + } else if (n == "warmup") { + out << config.mWarmup; + return true; + } else if (n == "relative") { + out << config.mIsRelative; + return true; + } + return false; +} + +static std::ostream& generateResultTag(Node const& n, Result const& r, std::ostream& out) { + if (generateConfigTag(n, r.config(), out)) { + return out; + } + // match e.g. "median(elapsed)" + // g++ 4.8 doesn't implement std::regex :( + // static std::regex const regOpArg1("^([a-zA-Z]+)\\(([a-zA-Z]*)\\)$"); + // std::cmatch matchResult; + // if (std::regex_match(n.begin, n.end, matchResult, regOpArg1)) { + std::vector<std::string> matchResult; + if (matchCmdArgs(std::string(n.begin, n.end), matchResult)) { + if (matchResult.size() == 2) { + auto m = Result::fromString(matchResult[1]); + if (m == Result::Measure::_size) { + return out << 0.0; + } + + if (matchResult[0] == "median") { + return out << r.median(m); + } + if (matchResult[0] == "average") { + return out << r.average(m); + } + if (matchResult[0] == "medianAbsolutePercentError") { + return out << r.medianAbsolutePercentError(m); + } + if (matchResult[0] == "sum") { + return out << r.sum(m); + } + if (matchResult[0] == "minimum") { + return out << r.minimum(m); + } + if (matchResult[0] == "maximum") { + return out << r.maximum(m); + } + } else if (matchResult.size() == 3) { + auto m1 = Result::fromString(matchResult[1]); + auto m2 = Result::fromString(matchResult[2]); + if (m1 == Result::Measure::_size || m2 == Result::Measure::_size) { + return out << 0.0; + } + + if (matchResult[0] == "sumProduct") { + return out << r.sumProduct(m1, m2); + } + } + } + + // match e.g. "sumProduct(elapsed, iterations)" + // static std::regex const regOpArg2("^([a-zA-Z]+)\\(([a-zA-Z]*)\\s*,\\s+([a-zA-Z]*)\\)$"); + + // nothing matches :( + throw std::runtime_error("command '" + std::string(n.begin, n.end) + "' not understood"); +} + +static void generateResultMeasurement(std::vector<Node> const& nodes, size_t idx, Result const& r, std::ostream& out) { + for (auto const& n : nodes) { + if (!generateFirstLast(n, idx, r.size(), out)) { + ANKERL_NANOBENCH_LOG("n.type=" << static_cast<int>(n.type)); + switch (n.type) { + case Node::Type::content: + out.write(n.begin, std::distance(n.begin, n.end)); + break; + + case Node::Type::inverted_section: + throw std::runtime_error("got a inverted section inside measurement"); + + case Node::Type::section: + throw std::runtime_error("got a section inside measurement"); + + case Node::Type::tag: { + auto m = Result::fromString(std::string(n.begin, n.end)); + if (m == Result::Measure::_size || !r.has(m)) { + out << 0.0; + } else { + out << r.get(idx, m); + } + break; + } + } + } + } +} + +static void generateResult(std::vector<Node> const& nodes, size_t idx, std::vector<Result> const& results, std::ostream& out) { + auto const& r = results[idx]; + for (auto const& n : nodes) { + if (!generateFirstLast(n, idx, results.size(), out)) { + ANKERL_NANOBENCH_LOG("n.type=" << static_cast<int>(n.type)); + switch (n.type) { + case Node::Type::content: + out.write(n.begin, std::distance(n.begin, n.end)); + break; + + case Node::Type::inverted_section: + throw std::runtime_error("got a inverted section inside result"); + + case Node::Type::section: + if (n == "measurement") { + for (size_t i = 0; i < r.size(); ++i) { + generateResultMeasurement(n.children, i, r, out); + } + } else { + throw std::runtime_error("got a section inside result"); + } + break; + + case Node::Type::tag: + generateResultTag(n, r, out); + break; + } + } + } +} + +} // namespace templates + +// helper stuff that only intended to be used internally +namespace detail { + +char const* getEnv(char const* name); +bool isEndlessRunning(std::string const& name); + +template <typename T> +T parseFile(std::string const& filename); + +void gatherStabilityInformation(std::vector<std::string>& warnings, std::vector<std::string>& recommendations); +void printStabilityInformationOnce(std::ostream* os); + +// remembers the last table settings used. When it changes, a new table header is automatically written for the new entry. +uint64_t& singletonHeaderHash() noexcept; + +// determines resolution of the given clock. This is done by measuring multiple times and returning the minimum time difference. +Clock::duration calcClockResolution(size_t numEvaluations) noexcept; + +// formatting utilities +namespace fmt { + +// adds thousands separator to numbers +ANKERL_NANOBENCH(IGNORE_PADDED_PUSH) +class NumSep : public std::numpunct<char> { +public: + explicit NumSep(char sep); + char do_thousands_sep() const override; + std::string do_grouping() const override; + +private: + char mSep; +}; +ANKERL_NANOBENCH(IGNORE_PADDED_POP) + +// RAII to save & restore a stream's state +ANKERL_NANOBENCH(IGNORE_PADDED_PUSH) +class StreamStateRestorer { +public: + explicit StreamStateRestorer(std::ostream& s); + ~StreamStateRestorer(); + + // sets back all stream info that we remembered at construction + void restore(); + + // don't allow copying / moving + StreamStateRestorer(StreamStateRestorer const&) = delete; + StreamStateRestorer& operator=(StreamStateRestorer const&) = delete; + StreamStateRestorer(StreamStateRestorer&&) = delete; + StreamStateRestorer& operator=(StreamStateRestorer&&) = delete; + +private: + std::ostream& mStream; + std::locale mLocale; + std::streamsize const mPrecision; + std::streamsize const mWidth; + std::ostream::char_type const mFill; + std::ostream::fmtflags const mFmtFlags; +}; +ANKERL_NANOBENCH(IGNORE_PADDED_POP) + +// Number formatter +class Number { +public: + Number(int width, int precision, double value); + Number(int width, int precision, int64_t value); + std::string to_s() const; + +private: + friend std::ostream& operator<<(std::ostream& os, Number const& n); + std::ostream& write(std::ostream& os) const; + + int mWidth; + int mPrecision; + double mValue; +}; + +// helper replacement for std::to_string of signed/unsigned numbers so we are locale independent +std::string to_s(uint64_t s); + +std::ostream& operator<<(std::ostream& os, Number const& n); + +class MarkDownColumn { +public: + MarkDownColumn(int w, int prec, std::string const& tit, std::string const& suff, double val); + std::string title() const; + std::string separator() const; + std::string invalid() const; + std::string value() const; + +private: + int mWidth; + int mPrecision; + std::string mTitle; + std::string mSuffix; + double mValue; +}; + +// Formats any text as markdown code, escaping backticks. +class MarkDownCode { +public: + explicit MarkDownCode(std::string const& what); + +private: + friend std::ostream& operator<<(std::ostream& os, MarkDownCode const& mdCode); + std::ostream& write(std::ostream& os) const; + + std::string mWhat{}; +}; + +std::ostream& operator<<(std::ostream& os, MarkDownCode const& mdCode); + +} // namespace fmt +} // namespace detail +} // namespace nanobench +} // namespace ankerl + +// implementation ///////////////////////////////////////////////////////////////////////////////// + +namespace ankerl { +namespace nanobench { + +void render(char const* mustacheTemplate, std::vector<Result> const& results, std::ostream& out) { + detail::fmt::StreamStateRestorer restorer(out); + + out.precision(std::numeric_limits<double>::digits10); + auto nodes = templates::parseMustacheTemplate(&mustacheTemplate); + + for (auto const& n : nodes) { + ANKERL_NANOBENCH_LOG("n.type=" << static_cast<int>(n.type)); + switch (n.type) { + case templates::Node::Type::content: + out.write(n.begin, std::distance(n.begin, n.end)); + break; + + case templates::Node::Type::inverted_section: + throw std::runtime_error("unknown list '" + std::string(n.begin, n.end) + "'"); + + case templates::Node::Type::section: + if (n == "result") { + const size_t nbResults = results.size(); + for (size_t i = 0; i < nbResults; ++i) { + generateResult(n.children, i, results, out); + } + } else { + throw std::runtime_error("unknown section '" + std::string(n.begin, n.end) + "'"); + } + break; + + case templates::Node::Type::tag: + // This just uses the last result's config. + if (!generateConfigTag(n, results.back().config(), out)) { + throw std::runtime_error("unknown tag '" + std::string(n.begin, n.end) + "'"); + } + break; + } + } +} + +void render(char const* mustacheTemplate, const Bench& bench, std::ostream& out) { + render(mustacheTemplate, bench.results(), out); +} + +namespace detail { + +PerformanceCounters& performanceCounters() { +# if defined(__clang__) +# pragma clang diagnostic push +# pragma clang diagnostic ignored "-Wexit-time-destructors" +# endif + static PerformanceCounters pc; +# if defined(__clang__) +# pragma clang diagnostic pop +# endif + return pc; +} + +// Windows version of doNotOptimizeAway +// see https://github.com/google/benchmark/blob/master/include/benchmark/benchmark.h#L307 +// see https://github.com/facebook/folly/blob/master/folly/Benchmark.h#L280 +// see https://docs.microsoft.com/en-us/cpp/preprocessor/optimize +# if defined(_MSC_VER) +# pragma optimize("", off) +void doNotOptimizeAwaySink(void const*) {} +# pragma optimize("", on) +# endif + +template <typename T> +T parseFile(std::string const& filename) { + std::ifstream fin(filename); + T num{}; + fin >> num; + return num; +} + +char const* getEnv(char const* name) { +# if defined(_MSC_VER) +# pragma warning(push) +# pragma warning(disable : 4996) // getenv': This function or variable may be unsafe. +# endif + return std::getenv(name); +# if defined(_MSC_VER) +# pragma warning(pop) +# endif +} + +bool isEndlessRunning(std::string const& name) { + auto endless = getEnv("NANOBENCH_ENDLESS"); + return nullptr != endless && endless == name; +} + +void gatherStabilityInformation(std::vector<std::string>& warnings, std::vector<std::string>& recommendations) { + warnings.clear(); + recommendations.clear(); + + bool recommendCheckFlags = false; + +# if defined(DEBUG) + warnings.emplace_back("DEBUG defined"); + recommendCheckFlags = true; +# endif + + bool recommendPyPerf = false; +# if defined(__linux__) + auto nprocs = sysconf(_SC_NPROCESSORS_CONF); + if (nprocs <= 0) { + warnings.emplace_back("couldn't figure out number of processors - no governor, turbo check possible"); + } else { + + // check frequency scaling + for (long id = 0; id < nprocs; ++id) { + auto idStr = detail::fmt::to_s(static_cast<uint64_t>(id)); + auto sysCpu = "/sys/devices/system/cpu/cpu" + idStr; + auto minFreq = parseFile<int64_t>(sysCpu + "/cpufreq/scaling_min_freq"); + auto maxFreq = parseFile<int64_t>(sysCpu + "/cpufreq/scaling_max_freq"); + if (minFreq != maxFreq) { + auto minMHz = static_cast<double>(minFreq) / 1000.0; + auto maxMHz = static_cast<double>(maxFreq) / 1000.0; + warnings.emplace_back("CPU frequency scaling enabled: CPU " + idStr + " between " + + detail::fmt::Number(1, 1, minMHz).to_s() + " and " + detail::fmt::Number(1, 1, maxMHz).to_s() + + " MHz"); + recommendPyPerf = true; + break; + } + } + + auto currentGovernor = parseFile<std::string>("/sys/devices/system/cpu/cpu0/cpufreq/scaling_governor"); + if ("performance" != currentGovernor) { + warnings.emplace_back("CPU governor is '" + currentGovernor + "' but should be 'performance'"); + recommendPyPerf = true; + } + + if (0 == parseFile<int>("/sys/devices/system/cpu/intel_pstate/no_turbo")) { + warnings.emplace_back("Turbo is enabled, CPU frequency will fluctuate"); + recommendPyPerf = true; + } + } +# endif + + if (recommendCheckFlags) { + recommendations.emplace_back("Make sure you compile for Release"); + } + if (recommendPyPerf) { + recommendations.emplace_back("Use 'pyperf system tune' before benchmarking. See https://github.com/vstinner/pyperf"); + } +} + +void printStabilityInformationOnce(std::ostream* outStream) { + static bool shouldPrint = true; + if (shouldPrint && outStream) { + auto& os = *outStream; + shouldPrint = false; + std::vector<std::string> warnings; + std::vector<std::string> recommendations; + gatherStabilityInformation(warnings, recommendations); + if (warnings.empty()) { + return; + } + + os << "Warning, results might be unstable:" << std::endl; + for (auto const& w : warnings) { + os << "* " << w << std::endl; + } + + os << std::endl << "Recommendations" << std::endl; + for (auto const& r : recommendations) { + os << "* " << r << std::endl; + } + } +} + +// remembers the last table settings used. When it changes, a new table header is automatically written for the new entry. +uint64_t& singletonHeaderHash() noexcept { + static uint64_t sHeaderHash{}; + return sHeaderHash; +} + +ANKERL_NANOBENCH_NO_SANITIZE("integer") +inline uint64_t fnv1a(std::string const& str) noexcept { + auto val = UINT64_C(14695981039346656037); + for (auto c : str) { + val = (val ^ static_cast<uint8_t>(c)) * UINT64_C(1099511628211); + } + return val; +} + +ANKERL_NANOBENCH_NO_SANITIZE("integer") +inline uint64_t hash_combine(uint64_t seed, uint64_t val) { + return seed ^ (val + UINT64_C(0x9e3779b9) + (seed << 6U) + (seed >> 2U)); +} + +// determines resolution of the given clock. This is done by measuring multiple times and returning the minimum time difference. +Clock::duration calcClockResolution(size_t numEvaluations) noexcept { + auto bestDuration = Clock::duration::max(); + Clock::time_point tBegin; + Clock::time_point tEnd; + for (size_t i = 0; i < numEvaluations; ++i) { + tBegin = Clock::now(); + do { + tEnd = Clock::now(); + } while (tBegin == tEnd); + bestDuration = (std::min)(bestDuration, tEnd - tBegin); + } + return bestDuration; +} + +// Calculates clock resolution once, and remembers the result +Clock::duration clockResolution() noexcept { + static Clock::duration sResolution = calcClockResolution(20); + return sResolution; +} + +ANKERL_NANOBENCH(IGNORE_PADDED_PUSH) +struct IterationLogic::Impl { + enum class State { warmup, upscaling_runtime, measuring, endless }; + + explicit Impl(Bench const& bench) + : mBench(bench) + , mResult(bench.config()) { + printStabilityInformationOnce(mBench.output()); + + // determine target runtime per epoch + mTargetRuntimePerEpoch = detail::clockResolution() * mBench.clockResolutionMultiple(); + if (mTargetRuntimePerEpoch > mBench.maxEpochTime()) { + mTargetRuntimePerEpoch = mBench.maxEpochTime(); + } + if (mTargetRuntimePerEpoch < mBench.minEpochTime()) { + mTargetRuntimePerEpoch = mBench.minEpochTime(); + } + + if (isEndlessRunning(mBench.name())) { + std::cerr << "NANOBENCH_ENDLESS set: running '" << mBench.name() << "' endlessly" << std::endl; + mNumIters = (std::numeric_limits<uint64_t>::max)(); + mState = State::endless; + } else if (0 != mBench.warmup()) { + mNumIters = mBench.warmup(); + mState = State::warmup; + } else if (0 != mBench.epochIterations()) { + // exact number of iterations + mNumIters = mBench.epochIterations(); + mState = State::measuring; + } else { + mNumIters = mBench.minEpochIterations(); + mState = State::upscaling_runtime; + } + } + + // directly calculates new iters based on elapsed&iters, and adds a 10% noise. Makes sure we don't underflow. + ANKERL_NANOBENCH(NODISCARD) uint64_t calcBestNumIters(std::chrono::nanoseconds elapsed, uint64_t iters) noexcept { + auto doubleElapsed = d(elapsed); + auto doubleTargetRuntimePerEpoch = d(mTargetRuntimePerEpoch); + auto doubleNewIters = doubleTargetRuntimePerEpoch / doubleElapsed * d(iters); + + auto doubleMinEpochIters = d(mBench.minEpochIterations()); + if (doubleNewIters < doubleMinEpochIters) { + doubleNewIters = doubleMinEpochIters; + } + doubleNewIters *= 1.0 + 0.2 * mRng.uniform01(); + + // +0.5 for correct rounding when casting + // NOLINTNEXTLINE(bugprone-incorrect-roundings) + return static_cast<uint64_t>(doubleNewIters + 0.5); + } + + ANKERL_NANOBENCH_NO_SANITIZE("integer") void upscale(std::chrono::nanoseconds elapsed) { + if (elapsed * 10 < mTargetRuntimePerEpoch) { + // we are far below the target runtime. Multiply iterations by 10 (with overflow check) + if (mNumIters * 10 < mNumIters) { + // overflow :-( + showResult("iterations overflow. Maybe your code got optimized away?"); + mNumIters = 0; + return; + } + mNumIters *= 10; + } else { + mNumIters = calcBestNumIters(elapsed, mNumIters); + } + } + + void add(std::chrono::nanoseconds elapsed, PerformanceCounters const& pc) noexcept { +# if defined(ANKERL_NANOBENCH_LOG_ENABLED) + auto oldIters = mNumIters; +# endif + + switch (mState) { + case State::warmup: + if (isCloseEnoughForMeasurements(elapsed)) { + // if elapsed is close enough, we can skip upscaling and go right to measurements + // still, we don't add the result to the measurements. + mState = State::measuring; + mNumIters = calcBestNumIters(elapsed, mNumIters); + } else { + // not close enough: switch to upscaling + mState = State::upscaling_runtime; + upscale(elapsed); + } + break; + + case State::upscaling_runtime: + if (isCloseEnoughForMeasurements(elapsed)) { + // if we are close enough, add measurement and switch to always measuring + mState = State::measuring; + mTotalElapsed += elapsed; + mTotalNumIters += mNumIters; + mResult.add(elapsed, mNumIters, pc); + mNumIters = calcBestNumIters(mTotalElapsed, mTotalNumIters); + } else { + upscale(elapsed); + } + break; + + case State::measuring: + // just add measurements - no questions asked. Even when runtime is low. But we can't ignore + // that fluctuation, or else we would bias the result + mTotalElapsed += elapsed; + mTotalNumIters += mNumIters; + mResult.add(elapsed, mNumIters, pc); + if (0 != mBench.epochIterations()) { + mNumIters = mBench.epochIterations(); + } else { + mNumIters = calcBestNumIters(mTotalElapsed, mTotalNumIters); + } + break; + + case State::endless: + mNumIters = (std::numeric_limits<uint64_t>::max)(); + break; + } + + if (static_cast<uint64_t>(mResult.size()) == mBench.epochs()) { + // we got all the results that we need, finish it + showResult(""); + mNumIters = 0; + } + + ANKERL_NANOBENCH_LOG(mBench.name() << ": " << detail::fmt::Number(20, 3, static_cast<double>(elapsed.count())) << " elapsed, " + << detail::fmt::Number(20, 3, static_cast<double>(mTargetRuntimePerEpoch.count())) + << " target. oldIters=" << oldIters << ", mNumIters=" << mNumIters + << ", mState=" << static_cast<int>(mState)); + } + + void showResult(std::string const& errorMessage) const { + ANKERL_NANOBENCH_LOG(errorMessage); + + if (mBench.output() != nullptr) { + // prepare column data /////// + std::vector<fmt::MarkDownColumn> columns; + + auto rMedian = mResult.median(Result::Measure::elapsed); + + if (mBench.relative()) { + double d = 100.0; + if (!mBench.results().empty()) { + d = rMedian <= 0.0 ? 0.0 : mBench.results().front().median(Result::Measure::elapsed) / rMedian * 100.0; + } + columns.emplace_back(11, 1, "relative", "%", d); + } + + if (mBench.complexityN() > 0) { + columns.emplace_back(14, 0, "complexityN", "", mBench.complexityN()); + } + + columns.emplace_back(22, 2, "ns/" + mBench.unit(), "", 1e9 * rMedian / mBench.batch()); + columns.emplace_back(22, 2, mBench.unit() + "/s", "", rMedian <= 0.0 ? 0.0 : mBench.batch() / rMedian); + + double rErrorMedian = mResult.medianAbsolutePercentError(Result::Measure::elapsed); + columns.emplace_back(10, 1, "err%", "%", rErrorMedian * 100.0); + + double rInsMedian = -1.0; + if (mResult.has(Result::Measure::instructions)) { + rInsMedian = mResult.median(Result::Measure::instructions); + columns.emplace_back(18, 2, "ins/" + mBench.unit(), "", rInsMedian / mBench.batch()); + } + + double rCycMedian = -1.0; + if (mResult.has(Result::Measure::cpucycles)) { + rCycMedian = mResult.median(Result::Measure::cpucycles); + columns.emplace_back(18, 2, "cyc/" + mBench.unit(), "", rCycMedian / mBench.batch()); + } + if (rInsMedian > 0.0 && rCycMedian > 0.0) { + columns.emplace_back(9, 3, "IPC", "", rCycMedian <= 0.0 ? 0.0 : rInsMedian / rCycMedian); + } + if (mResult.has(Result::Measure::branchinstructions)) { + double rBraMedian = mResult.median(Result::Measure::branchinstructions); + columns.emplace_back(17, 2, "bra/" + mBench.unit(), "", rBraMedian / mBench.batch()); + if (mResult.has(Result::Measure::branchmisses)) { + double p = 0.0; + if (rBraMedian >= 1e-9) { + p = 100.0 * mResult.median(Result::Measure::branchmisses) / rBraMedian; + } + columns.emplace_back(10, 1, "miss%", "%", p); + } + } + + columns.emplace_back(12, 2, "total", "", mResult.sum(Result::Measure::elapsed)); + + // write everything + auto& os = *mBench.output(); + + uint64_t hash = 0; + hash = hash_combine(fnv1a(mBench.unit()), hash); + hash = hash_combine(fnv1a(mBench.title()), hash); + hash = hash_combine(mBench.relative(), hash); + hash = hash_combine(mBench.performanceCounters(), hash); + + if (hash != singletonHeaderHash()) { + singletonHeaderHash() = hash; + + // no result yet, print header + os << std::endl; + for (auto const& col : columns) { + os << col.title(); + } + os << "| " << mBench.title() << std::endl; + + for (auto const& col : columns) { + os << col.separator(); + } + os << "|:" << std::string(mBench.title().size() + 1U, '-') << std::endl; + } + + if (!errorMessage.empty()) { + for (auto const& col : columns) { + os << col.invalid(); + } + os << "| :boom: " << fmt::MarkDownCode(mBench.name()) << " (" << errorMessage << ')' << std::endl; + } else { + for (auto const& col : columns) { + os << col.value(); + } + os << "| "; + auto showUnstable = rErrorMedian >= 0.05; + if (showUnstable) { + os << ":wavy_dash: "; + } + os << fmt::MarkDownCode(mBench.name()); + if (showUnstable) { + auto avgIters = static_cast<double>(mTotalNumIters) / static_cast<double>(mBench.epochs()); + // NOLINTNEXTLINE(bugprone-incorrect-roundings) + auto suggestedIters = static_cast<uint64_t>(avgIters * 10 + 0.5); + + os << " (Unstable with ~" << detail::fmt::Number(1, 1, avgIters) + << " iters. Increase `minEpochIterations` to e.g. " << suggestedIters << ")"; + } + os << std::endl; + } + } + } + + ANKERL_NANOBENCH(NODISCARD) bool isCloseEnoughForMeasurements(std::chrono::nanoseconds elapsed) const noexcept { + return elapsed * 3 >= mTargetRuntimePerEpoch * 2; + } + + uint64_t mNumIters = 1; + Bench const& mBench; + std::chrono::nanoseconds mTargetRuntimePerEpoch{}; + Result mResult; + Rng mRng{123}; + std::chrono::nanoseconds mTotalElapsed{}; + uint64_t mTotalNumIters = 0; + + State mState = State::upscaling_runtime; +}; +ANKERL_NANOBENCH(IGNORE_PADDED_POP) + +IterationLogic::IterationLogic(Bench const& bench) noexcept + : mPimpl(new Impl(bench)) {} + +IterationLogic::~IterationLogic() { + if (mPimpl) { + delete mPimpl; + } +} + +uint64_t IterationLogic::numIters() const noexcept { + ANKERL_NANOBENCH_LOG(mPimpl->mBench.name() << ": mNumIters=" << mPimpl->mNumIters); + return mPimpl->mNumIters; +} + +void IterationLogic::add(std::chrono::nanoseconds elapsed, PerformanceCounters const& pc) noexcept { + mPimpl->add(elapsed, pc); +} + +void IterationLogic::moveResultTo(std::vector<Result>& results) noexcept { + results.emplace_back(std::move(mPimpl->mResult)); +} + +# if ANKERL_NANOBENCH(PERF_COUNTERS) + +ANKERL_NANOBENCH(IGNORE_PADDED_PUSH) +class LinuxPerformanceCounters { +public: + struct Target { + Target(uint64_t* targetValue_, bool correctMeasuringOverhead_, bool correctLoopOverhead_) + : targetValue(targetValue_) + , correctMeasuringOverhead(correctMeasuringOverhead_) + , correctLoopOverhead(correctLoopOverhead_) {} + + uint64_t* targetValue{}; + bool correctMeasuringOverhead{}; + bool correctLoopOverhead{}; + }; + + ~LinuxPerformanceCounters(); + + // quick operation + inline void start() {} + + inline void stop() {} + + bool monitor(perf_sw_ids swId, Target target); + bool monitor(perf_hw_id hwId, Target target); + + bool hasError() const noexcept { + return mHasError; + } + + // Just reading data is faster than enable & disabling. + // we subtract data ourselves. + inline void beginMeasure() { + if (mHasError) { + return; + } + + // NOLINTNEXTLINE(hicpp-signed-bitwise) + mHasError = -1 == ioctl(mFd, PERF_EVENT_IOC_RESET, PERF_IOC_FLAG_GROUP); + if (mHasError) { + return; + } + + // NOLINTNEXTLINE(hicpp-signed-bitwise) + mHasError = -1 == ioctl(mFd, PERF_EVENT_IOC_ENABLE, PERF_IOC_FLAG_GROUP); + } + + inline void endMeasure() { + if (mHasError) { + return; + } + + // NOLINTNEXTLINE(hicpp-signed-bitwise) + mHasError = (-1 == ioctl(mFd, PERF_EVENT_IOC_DISABLE, PERF_IOC_FLAG_GROUP)); + if (mHasError) { + return; + } + + auto const numBytes = sizeof(uint64_t) * mCounters.size(); + auto ret = read(mFd, mCounters.data(), numBytes); + mHasError = ret != static_cast<ssize_t>(numBytes); + } + + void updateResults(uint64_t numIters); + + // rounded integer division + template <typename T> + static inline T divRounded(T a, T divisor) { + return (a + divisor / 2) / divisor; + } + + template <typename Op> + ANKERL_NANOBENCH_NO_SANITIZE("integer") + void calibrate(Op&& op) { + // clear current calibration data, + for (auto& v : mCalibratedOverhead) { + v = UINT64_C(0); + } + + // create new calibration data + auto newCalibration = mCalibratedOverhead; + for (auto& v : newCalibration) { + v = (std::numeric_limits<uint64_t>::max)(); + } + for (size_t iter = 0; iter < 100; ++iter) { + beginMeasure(); + op(); + endMeasure(); + if (mHasError) { + return; + } + + for (size_t i = 0; i < newCalibration.size(); ++i) { + auto diff = mCounters[i]; + if (newCalibration[i] > diff) { + newCalibration[i] = diff; + } + } + } + + mCalibratedOverhead = std::move(newCalibration); + + { + // calibrate loop overhead. For branches & instructions this makes sense, not so much for everything else like cycles. + // marsaglia's xorshift: mov, sal/shr, xor. Times 3. + // This has the nice property that the compiler doesn't seem to be able to optimize multiple calls any further. + // see https://godbolt.org/z/49RVQ5 + uint64_t const numIters = 100000U + (std::random_device{}() & 3); + uint64_t n = numIters; + uint32_t x = 1234567; + auto fn = [&]() { + x ^= x << 13; + x ^= x >> 17; + x ^= x << 5; + }; + + beginMeasure(); + while (n-- > 0) { + fn(); + } + endMeasure(); + detail::doNotOptimizeAway(x); + auto measure1 = mCounters; + + n = numIters; + beginMeasure(); + while (n-- > 0) { + // we now run *twice* so we can easily calculate the overhead + fn(); + fn(); + } + endMeasure(); + detail::doNotOptimizeAway(x); + auto measure2 = mCounters; + + for (size_t i = 0; i < mCounters.size(); ++i) { + // factor 2 because we have two instructions per loop + auto m1 = measure1[i] > mCalibratedOverhead[i] ? measure1[i] - mCalibratedOverhead[i] : 0; + auto m2 = measure2[i] > mCalibratedOverhead[i] ? measure2[i] - mCalibratedOverhead[i] : 0; + auto overhead = m1 * 2 > m2 ? m1 * 2 - m2 : 0; + + mLoopOverhead[i] = divRounded(overhead, numIters); + } + } + } + +private: + bool monitor(uint32_t type, uint64_t eventid, Target target); + + std::map<uint64_t, Target> mIdToTarget{}; + + // start with minimum size of 3 for read_format + std::vector<uint64_t> mCounters{3}; + std::vector<uint64_t> mCalibratedOverhead{3}; + std::vector<uint64_t> mLoopOverhead{3}; + + uint64_t mTimeEnabledNanos = 0; + uint64_t mTimeRunningNanos = 0; + int mFd = -1; + bool mHasError = false; +}; +ANKERL_NANOBENCH(IGNORE_PADDED_POP) + +LinuxPerformanceCounters::~LinuxPerformanceCounters() { + if (-1 != mFd) { + close(mFd); + } +} + +bool LinuxPerformanceCounters::monitor(perf_sw_ids swId, LinuxPerformanceCounters::Target target) { + return monitor(PERF_TYPE_SOFTWARE, swId, target); +} + +bool LinuxPerformanceCounters::monitor(perf_hw_id hwId, LinuxPerformanceCounters::Target target) { + return monitor(PERF_TYPE_HARDWARE, hwId, target); +} + +// overflow is ok, it's checked +ANKERL_NANOBENCH_NO_SANITIZE("integer") +void LinuxPerformanceCounters::updateResults(uint64_t numIters) { + // clear old data + for (auto& id_value : mIdToTarget) { + *id_value.second.targetValue = UINT64_C(0); + } + + if (mHasError) { + return; + } + + mTimeEnabledNanos = mCounters[1] - mCalibratedOverhead[1]; + mTimeRunningNanos = mCounters[2] - mCalibratedOverhead[2]; + + for (uint64_t i = 0; i < mCounters[0]; ++i) { + auto idx = static_cast<size_t>(3 + i * 2 + 0); + auto id = mCounters[idx + 1U]; + + auto it = mIdToTarget.find(id); + if (it != mIdToTarget.end()) { + + auto& tgt = it->second; + *tgt.targetValue = mCounters[idx]; + if (tgt.correctMeasuringOverhead) { + if (*tgt.targetValue >= mCalibratedOverhead[idx]) { + *tgt.targetValue -= mCalibratedOverhead[idx]; + } else { + *tgt.targetValue = 0U; + } + } + if (tgt.correctLoopOverhead) { + auto correctionVal = mLoopOverhead[idx] * numIters; + if (*tgt.targetValue >= correctionVal) { + *tgt.targetValue -= correctionVal; + } else { + *tgt.targetValue = 0U; + } + } + } + } +} + +bool LinuxPerformanceCounters::monitor(uint32_t type, uint64_t eventid, Target target) { + *target.targetValue = (std::numeric_limits<uint64_t>::max)(); + if (mHasError) { + return false; + } + + auto pea = perf_event_attr(); + std::memset(&pea, 0, sizeof(perf_event_attr)); + pea.type = type; + pea.size = sizeof(perf_event_attr); + pea.config = eventid; + pea.disabled = 1; // start counter as disabled + pea.exclude_kernel = 1; + pea.exclude_hv = 1; + + // NOLINTNEXTLINE(hicpp-signed-bitwise) + pea.read_format = PERF_FORMAT_GROUP | PERF_FORMAT_ID | PERF_FORMAT_TOTAL_TIME_ENABLED | PERF_FORMAT_TOTAL_TIME_RUNNING; + + const int pid = 0; // the current process + const int cpu = -1; // all CPUs +# if defined(PERF_FLAG_FD_CLOEXEC) // since Linux 3.14 + const unsigned long flags = PERF_FLAG_FD_CLOEXEC; +# else + const unsigned long flags = 0; +# endif + + auto fd = static_cast<int>(syscall(__NR_perf_event_open, &pea, pid, cpu, mFd, flags)); + if (-1 == fd) { + return false; + } + if (-1 == mFd) { + // first call: set to fd, and use this from now on + mFd = fd; + } + uint64_t id = 0; + // NOLINTNEXTLINE(hicpp-signed-bitwise) + if (-1 == ioctl(fd, PERF_EVENT_IOC_ID, &id)) { + // couldn't get id + return false; + } + + // insert into map, rely on the fact that map's references are constant. + mIdToTarget.emplace(id, target); + + // prepare readformat with the correct size (after the insert) + auto size = 3 + 2 * mIdToTarget.size(); + mCounters.resize(size); + mCalibratedOverhead.resize(size); + mLoopOverhead.resize(size); + + return true; +} + +PerformanceCounters::PerformanceCounters() + : mPc(new LinuxPerformanceCounters()) + , mVal() + , mHas() { + + mHas.pageFaults = mPc->monitor(PERF_COUNT_SW_PAGE_FAULTS, LinuxPerformanceCounters::Target(&mVal.pageFaults, true, false)); + mHas.cpuCycles = mPc->monitor(PERF_COUNT_HW_REF_CPU_CYCLES, LinuxPerformanceCounters::Target(&mVal.cpuCycles, true, false)); + mHas.contextSwitches = + mPc->monitor(PERF_COUNT_SW_CONTEXT_SWITCHES, LinuxPerformanceCounters::Target(&mVal.contextSwitches, true, false)); + mHas.instructions = mPc->monitor(PERF_COUNT_HW_INSTRUCTIONS, LinuxPerformanceCounters::Target(&mVal.instructions, true, true)); + mHas.branchInstructions = + mPc->monitor(PERF_COUNT_HW_BRANCH_INSTRUCTIONS, LinuxPerformanceCounters::Target(&mVal.branchInstructions, true, false)); + mHas.branchMisses = mPc->monitor(PERF_COUNT_HW_BRANCH_MISSES, LinuxPerformanceCounters::Target(&mVal.branchMisses, true, false)); + // mHas.branchMisses = false; + + mPc->start(); + mPc->calibrate([] { + auto before = ankerl::nanobench::Clock::now(); + auto after = ankerl::nanobench::Clock::now(); + (void)before; + (void)after; + }); + + if (mPc->hasError()) { + // something failed, don't monitor anything. + mHas = PerfCountSet<bool>{}; + } +} + +PerformanceCounters::~PerformanceCounters() { + if (nullptr != mPc) { + delete mPc; + } +} + +void PerformanceCounters::beginMeasure() { + mPc->beginMeasure(); +} + +void PerformanceCounters::endMeasure() { + mPc->endMeasure(); +} + +void PerformanceCounters::updateResults(uint64_t numIters) { + mPc->updateResults(numIters); +} + +# else + +PerformanceCounters::PerformanceCounters() = default; +PerformanceCounters::~PerformanceCounters() = default; +void PerformanceCounters::beginMeasure() {} +void PerformanceCounters::endMeasure() {} +void PerformanceCounters::updateResults(uint64_t) {} + +# endif + +ANKERL_NANOBENCH(NODISCARD) PerfCountSet<uint64_t> const& PerformanceCounters::val() const noexcept { + return mVal; +} +ANKERL_NANOBENCH(NODISCARD) PerfCountSet<bool> const& PerformanceCounters::has() const noexcept { + return mHas; +} + +// formatting utilities +namespace fmt { + +// adds thousands separator to numbers +NumSep::NumSep(char sep) + : mSep(sep) {} + +char NumSep::do_thousands_sep() const { + return mSep; +} + +std::string NumSep::do_grouping() const { + return "\003"; +} + +// RAII to save & restore a stream's state +StreamStateRestorer::StreamStateRestorer(std::ostream& s) + : mStream(s) + , mLocale(s.getloc()) + , mPrecision(s.precision()) + , mWidth(s.width()) + , mFill(s.fill()) + , mFmtFlags(s.flags()) {} + +StreamStateRestorer::~StreamStateRestorer() { + restore(); +} + +// sets back all stream info that we remembered at construction +void StreamStateRestorer::restore() { + mStream.imbue(mLocale); + mStream.precision(mPrecision); + mStream.width(mWidth); + mStream.fill(mFill); + mStream.flags(mFmtFlags); +} + +Number::Number(int width, int precision, int64_t value) + : mWidth(width) + , mPrecision(precision) + , mValue(static_cast<double>(value)) {} + +Number::Number(int width, int precision, double value) + : mWidth(width) + , mPrecision(precision) + , mValue(value) {} + +std::ostream& Number::write(std::ostream& os) const { + StreamStateRestorer restorer(os); + os.imbue(std::locale(os.getloc(), new NumSep(','))); + os << std::setw(mWidth) << std::setprecision(mPrecision) << std::fixed << mValue; + return os; +} + +std::string Number::to_s() const { + std::stringstream ss; + write(ss); + return ss.str(); +} + +std::string to_s(uint64_t n) { + std::string str; + do { + str += static_cast<char>('0' + static_cast<char>(n % 10)); + n /= 10; + } while (n != 0); + std::reverse(str.begin(), str.end()); + return str; +} + +std::ostream& operator<<(std::ostream& os, Number const& n) { + return n.write(os); +} + +MarkDownColumn::MarkDownColumn(int w, int prec, std::string const& tit, std::string const& suff, double val) + : mWidth(w) + , mPrecision(prec) + , mTitle(tit) + , mSuffix(suff) + , mValue(val) {} + +std::string MarkDownColumn::title() const { + std::stringstream ss; + ss << '|' << std::setw(mWidth - 2) << std::right << mTitle << ' '; + return ss.str(); +} + +std::string MarkDownColumn::separator() const { + std::string sep(static_cast<size_t>(mWidth), '-'); + sep.front() = '|'; + sep.back() = ':'; + return sep; +} + +std::string MarkDownColumn::invalid() const { + std::string sep(static_cast<size_t>(mWidth), ' '); + sep.front() = '|'; + sep[sep.size() - 2] = '-'; + return sep; +} + +std::string MarkDownColumn::value() const { + std::stringstream ss; + auto width = mWidth - 2 - static_cast<int>(mSuffix.size()); + ss << '|' << Number(width, mPrecision, mValue) << mSuffix << ' '; + return ss.str(); +} + +// Formats any text as markdown code, escaping backticks. +MarkDownCode::MarkDownCode(std::string const& what) { + mWhat.reserve(what.size() + 2); + mWhat.push_back('`'); + for (char c : what) { + mWhat.push_back(c); + if ('`' == c) { + mWhat.push_back('`'); + } + } + mWhat.push_back('`'); +} + +std::ostream& MarkDownCode::write(std::ostream& os) const { + return os << mWhat; +} + +std::ostream& operator<<(std::ostream& os, MarkDownCode const& mdCode) { + return mdCode.write(os); +} +} // namespace fmt +} // namespace detail + +// provide implementation here so it's only generated once +Config::Config() = default; +Config::~Config() = default; +Config& Config::operator=(Config const&) = default; +Config& Config::operator=(Config&&) = default; +Config::Config(Config const&) = default; +Config::Config(Config&&) noexcept = default; + +// provide implementation here so it's only generated once +Result::~Result() = default; +Result& Result::operator=(Result const&) = default; +Result& Result::operator=(Result&&) = default; +Result::Result(Result const&) = default; +Result::Result(Result&&) noexcept = default; + +namespace detail { +template <typename T> +inline constexpr typename std::underlying_type<T>::type u(T val) noexcept { + return static_cast<typename std::underlying_type<T>::type>(val); +} +} // namespace detail + +// Result returned after a benchmark has finished. Can be used as a baseline for relative(). +Result::Result(Config const& benchmarkConfig) + : mConfig(benchmarkConfig) + , mNameToMeasurements{detail::u(Result::Measure::_size)} {} + +void Result::add(Clock::duration totalElapsed, uint64_t iters, detail::PerformanceCounters const& pc) { + using detail::d; + using detail::u; + + double dIters = d(iters); + mNameToMeasurements[u(Result::Measure::iterations)].push_back(dIters); + + mNameToMeasurements[u(Result::Measure::elapsed)].push_back(d(totalElapsed) / dIters); + if (pc.has().pageFaults) { + mNameToMeasurements[u(Result::Measure::pagefaults)].push_back(d(pc.val().pageFaults) / dIters); + } + if (pc.has().cpuCycles) { + mNameToMeasurements[u(Result::Measure::cpucycles)].push_back(d(pc.val().cpuCycles) / dIters); + } + if (pc.has().contextSwitches) { + mNameToMeasurements[u(Result::Measure::contextswitches)].push_back(d(pc.val().contextSwitches) / dIters); + } + if (pc.has().instructions) { + mNameToMeasurements[u(Result::Measure::instructions)].push_back(d(pc.val().instructions) / dIters); + } + if (pc.has().branchInstructions) { + double branchInstructions = 0.0; + // correcting branches: remove branch introduced by the while (...) loop for each iteration. + if (pc.val().branchInstructions > iters + 1U) { + branchInstructions = d(pc.val().branchInstructions - (iters + 1U)); + } + mNameToMeasurements[u(Result::Measure::branchinstructions)].push_back(branchInstructions / dIters); + + if (pc.has().branchMisses) { + // correcting branch misses + double branchMisses = d(pc.val().branchMisses); + if (branchMisses > branchInstructions) { + // can't have branch misses when there were branches... + branchMisses = branchInstructions; + } + + // assuming at least one missed branch for the loop + branchMisses -= 1.0; + if (branchMisses < 1.0) { + branchMisses = 1.0; + } + mNameToMeasurements[u(Result::Measure::branchmisses)].push_back(branchMisses / dIters); + } + } +} + +Config const& Result::config() const noexcept { + return mConfig; +} + +inline double calcMedian(std::vector<double>& data) { + if (data.empty()) { + return 0.0; + } + std::sort(data.begin(), data.end()); + + auto midIdx = data.size() / 2U; + if (1U == (data.size() & 1U)) { + return data[midIdx]; + } + return (data[midIdx - 1U] + data[midIdx]) / 2U; +} + +double Result::median(Measure m) const { + // create a copy so we can sort + auto data = mNameToMeasurements[detail::u(m)]; + return calcMedian(data); +} + +double Result::average(Measure m) const { + using detail::d; + auto const& data = mNameToMeasurements[detail::u(m)]; + if (data.empty()) { + return 0.0; + } + + // create a copy so we can sort + return sum(m) / d(data.size()); +} + +double Result::medianAbsolutePercentError(Measure m) const { + // create copy + auto data = mNameToMeasurements[detail::u(m)]; + + // calculates MdAPE which is the median of percentage error + // see https://www.spiderfinancial.com/support/documentation/numxl/reference-manual/forecasting-performance/mdape + auto med = calcMedian(data); + + // transform the data to absolute error + for (auto& x : data) { + x = (x - med) / x; + if (x < 0) { + x = -x; + } + } + return calcMedian(data); +} + +double Result::sum(Measure m) const noexcept { + auto const& data = mNameToMeasurements[detail::u(m)]; + return std::accumulate(data.begin(), data.end(), 0.0); +} + +double Result::sumProduct(Measure m1, Measure m2) const noexcept { + auto const& data1 = mNameToMeasurements[detail::u(m1)]; + auto const& data2 = mNameToMeasurements[detail::u(m2)]; + + if (data1.size() != data2.size()) { + return 0.0; + } + + double result = 0.0; + for (size_t i = 0, s = data1.size(); i != s; ++i) { + result += data1[i] * data2[i]; + } + return result; +} + +bool Result::has(Measure m) const noexcept { + return !mNameToMeasurements[detail::u(m)].empty(); +} + +double Result::get(size_t idx, Measure m) const { + auto const& data = mNameToMeasurements[detail::u(m)]; + return data.at(idx); +} + +bool Result::empty() const noexcept { + return 0U == size(); +} + +size_t Result::size() const noexcept { + auto const& data = mNameToMeasurements[detail::u(Measure::elapsed)]; + return data.size(); +} + +double Result::minimum(Measure m) const noexcept { + auto const& data = mNameToMeasurements[detail::u(m)]; + if (data.empty()) { + return 0.0; + } + + // here its save to assume that at least one element is there + return *std::min_element(data.begin(), data.end()); +} + +double Result::maximum(Measure m) const noexcept { + auto const& data = mNameToMeasurements[detail::u(m)]; + if (data.empty()) { + return 0.0; + } + + // here its save to assume that at least one element is there + return *std::max_element(data.begin(), data.end()); +} + +Result::Measure Result::fromString(std::string const& str) { + if (str == "elapsed") { + return Measure::elapsed; + } else if (str == "iterations") { + return Measure::iterations; + } else if (str == "pagefaults") { + return Measure::pagefaults; + } else if (str == "cpucycles") { + return Measure::cpucycles; + } else if (str == "contextswitches") { + return Measure::contextswitches; + } else if (str == "instructions") { + return Measure::instructions; + } else if (str == "branchinstructions") { + return Measure::branchinstructions; + } else if (str == "branchmisses") { + return Measure::branchmisses; + } else { + // not found, return _size + return Measure::_size; + } +} + +// Configuration of a microbenchmark. +Bench::Bench() { + mConfig.mOut = &std::cout; +} + +Bench::Bench(Bench&&) = default; +Bench& Bench::operator=(Bench&&) = default; +Bench::Bench(Bench const&) = default; +Bench& Bench::operator=(Bench const&) = default; +Bench::~Bench() noexcept = default; + +double Bench::batch() const noexcept { + return mConfig.mBatch; +} + +double Bench::complexityN() const noexcept { + return mConfig.mComplexityN; +} + +// Set a baseline to compare it to. 100% it is exactly as fast as the baseline, >100% means it is faster than the baseline, <100% +// means it is slower than the baseline. +Bench& Bench::relative(bool isRelativeEnabled) noexcept { + mConfig.mIsRelative = isRelativeEnabled; + return *this; +} +bool Bench::relative() const noexcept { + return mConfig.mIsRelative; +} + +Bench& Bench::performanceCounters(bool showPerformanceCounters) noexcept { + mConfig.mShowPerformanceCounters = showPerformanceCounters; + return *this; +} +bool Bench::performanceCounters() const noexcept { + return mConfig.mShowPerformanceCounters; +} + +// Operation unit. Defaults to "op", could be e.g. "byte" for string processing. +// If u differs from currently set unit, the stored results will be cleared. +// Use singular (byte, not bytes). +Bench& Bench::unit(char const* u) { + if (u != mConfig.mUnit) { + mResults.clear(); + } + mConfig.mUnit = u; + return *this; +} + +Bench& Bench::unit(std::string const& u) { + return unit(u.c_str()); +} + +std::string const& Bench::unit() const noexcept { + return mConfig.mUnit; +} + +// If benchmarkTitle differs from currently set title, the stored results will be cleared. +Bench& Bench::title(const char* benchmarkTitle) { + if (benchmarkTitle != mConfig.mBenchmarkTitle) { + mResults.clear(); + } + mConfig.mBenchmarkTitle = benchmarkTitle; + return *this; +} +Bench& Bench::title(std::string const& benchmarkTitle) { + if (benchmarkTitle != mConfig.mBenchmarkTitle) { + mResults.clear(); + } + mConfig.mBenchmarkTitle = benchmarkTitle; + return *this; +} + +std::string const& Bench::title() const noexcept { + return mConfig.mBenchmarkTitle; +} + +Bench& Bench::name(const char* benchmarkName) { + mConfig.mBenchmarkName = benchmarkName; + return *this; +} + +Bench& Bench::name(std::string const& benchmarkName) { + mConfig.mBenchmarkName = benchmarkName; + return *this; +} + +std::string const& Bench::name() const noexcept { + return mConfig.mBenchmarkName; +} + +// Number of epochs to evaluate. The reported result will be the median of evaluation of each epoch. +Bench& Bench::epochs(size_t numEpochs) noexcept { + mConfig.mNumEpochs = numEpochs; + return *this; +} +size_t Bench::epochs() const noexcept { + return mConfig.mNumEpochs; +} + +// Desired evaluation time is a multiple of clock resolution. Default is to be 1000 times above this measurement precision. +Bench& Bench::clockResolutionMultiple(size_t multiple) noexcept { + mConfig.mClockResolutionMultiple = multiple; + return *this; +} +size_t Bench::clockResolutionMultiple() const noexcept { + return mConfig.mClockResolutionMultiple; +} + +// Sets the maximum time each epoch should take. Default is 100ms. +Bench& Bench::maxEpochTime(std::chrono::nanoseconds t) noexcept { + mConfig.mMaxEpochTime = t; + return *this; +} +std::chrono::nanoseconds Bench::maxEpochTime() const noexcept { + return mConfig.mMaxEpochTime; +} + +// Sets the maximum time each epoch should take. Default is 100ms. +Bench& Bench::minEpochTime(std::chrono::nanoseconds t) noexcept { + mConfig.mMinEpochTime = t; + return *this; +} +std::chrono::nanoseconds Bench::minEpochTime() const noexcept { + return mConfig.mMinEpochTime; +} + +Bench& Bench::minEpochIterations(uint64_t numIters) noexcept { + mConfig.mMinEpochIterations = (numIters == 0) ? 1 : numIters; + return *this; +} +uint64_t Bench::minEpochIterations() const noexcept { + return mConfig.mMinEpochIterations; +} + +Bench& Bench::epochIterations(uint64_t numIters) noexcept { + mConfig.mEpochIterations = numIters; + return *this; +} +uint64_t Bench::epochIterations() const noexcept { + return mConfig.mEpochIterations; +} + +Bench& Bench::warmup(uint64_t numWarmupIters) noexcept { + mConfig.mWarmup = numWarmupIters; + return *this; +} +uint64_t Bench::warmup() const noexcept { + return mConfig.mWarmup; +} + +Bench& Bench::config(Config const& benchmarkConfig) { + mConfig = benchmarkConfig; + return *this; +} +Config const& Bench::config() const noexcept { + return mConfig; +} + +Bench& Bench::output(std::ostream* outstream) noexcept { + mConfig.mOut = outstream; + return *this; +} + +ANKERL_NANOBENCH(NODISCARD) std::ostream* Bench::output() const noexcept { + return mConfig.mOut; +} + +std::vector<Result> const& Bench::results() const noexcept { + return mResults; +} + +Bench& Bench::render(char const* templateContent, std::ostream& os) { + ::ankerl::nanobench::render(templateContent, *this, os); + return *this; +} + +std::vector<BigO> Bench::complexityBigO() const { + std::vector<BigO> bigOs; + auto rangeMeasure = BigO::collectRangeMeasure(mResults); + bigOs.emplace_back("O(1)", rangeMeasure, [](double) { + return 1.0; + }); + bigOs.emplace_back("O(n)", rangeMeasure, [](double n) { + return n; + }); + bigOs.emplace_back("O(log n)", rangeMeasure, [](double n) { + return std::log2(n); + }); + bigOs.emplace_back("O(n log n)", rangeMeasure, [](double n) { + return n * std::log2(n); + }); + bigOs.emplace_back("O(n^2)", rangeMeasure, [](double n) { + return n * n; + }); + bigOs.emplace_back("O(n^3)", rangeMeasure, [](double n) { + return n * n * n; + }); + std::sort(bigOs.begin(), bigOs.end()); + return bigOs; +} + +Rng::Rng() + : mX(0) + , mY(0) { + std::random_device rd; + std::uniform_int_distribution<uint64_t> dist; + do { + mX = dist(rd); + mY = dist(rd); + } while (mX == 0 && mY == 0); +} + +ANKERL_NANOBENCH_NO_SANITIZE("integer") +uint64_t splitMix64(uint64_t& state) noexcept { + uint64_t z = (state += UINT64_C(0x9e3779b97f4a7c15)); + z = (z ^ (z >> 30U)) * UINT64_C(0xbf58476d1ce4e5b9); + z = (z ^ (z >> 27U)) * UINT64_C(0x94d049bb133111eb); + return z ^ (z >> 31U); +} + +// Seeded as described in romu paper (update april 2020) +Rng::Rng(uint64_t seed) noexcept + : mX(splitMix64(seed)) + , mY(splitMix64(seed)) { + for (size_t i = 0; i < 10; ++i) { + operator()(); + } +} + +// only internally used to copy the RNG. +Rng::Rng(uint64_t x, uint64_t y) noexcept + : mX(x) + , mY(y) {} + +Rng Rng::copy() const noexcept { + return Rng{mX, mY}; +} + +BigO::RangeMeasure BigO::collectRangeMeasure(std::vector<Result> const& results) { + BigO::RangeMeasure rangeMeasure; + for (auto const& result : results) { + if (result.config().mComplexityN > 0.0) { + rangeMeasure.emplace_back(result.config().mComplexityN, result.median(Result::Measure::elapsed)); + } + } + return rangeMeasure; +} + +BigO::BigO(std::string const& bigOName, RangeMeasure const& rangeMeasure) + : mName(bigOName) { + + // estimate the constant factor + double sumRangeMeasure = 0.0; + double sumRangeRange = 0.0; + + for (size_t i = 0; i < rangeMeasure.size(); ++i) { + sumRangeMeasure += rangeMeasure[i].first * rangeMeasure[i].second; + sumRangeRange += rangeMeasure[i].first * rangeMeasure[i].first; + } + mConstant = sumRangeMeasure / sumRangeRange; + + // calculate root mean square + double err = 0.0; + double sumMeasure = 0.0; + for (size_t i = 0; i < rangeMeasure.size(); ++i) { + auto diff = mConstant * rangeMeasure[i].first - rangeMeasure[i].second; + err += diff * diff; + + sumMeasure += rangeMeasure[i].second; + } + + auto n = static_cast<double>(rangeMeasure.size()); + auto mean = sumMeasure / n; + mNormalizedRootMeanSquare = std::sqrt(err / n) / mean; +} + +BigO::BigO(const char* bigOName, RangeMeasure const& rangeMeasure) + : BigO(std::string(bigOName), rangeMeasure) {} + +std::string const& BigO::name() const noexcept { + return mName; +} + +double BigO::constant() const noexcept { + return mConstant; +} + +double BigO::normalizedRootMeanSquare() const noexcept { + return mNormalizedRootMeanSquare; +} + +bool BigO::operator<(BigO const& other) const noexcept { + return std::tie(mNormalizedRootMeanSquare, mName) < std::tie(other.mNormalizedRootMeanSquare, other.mName); +} + +std::ostream& operator<<(std::ostream& os, BigO const& bigO) { + return os << bigO.constant() << " * " << bigO.name() << ", rms=" << bigO.normalizedRootMeanSquare(); +} + +std::ostream& operator<<(std::ostream& os, std::vector<ankerl::nanobench::BigO> const& bigOs) { + detail::fmt::StreamStateRestorer restorer(os); + os << std::endl << "| coefficient | err% | complexity" << std::endl << "|--------------:|-------:|------------" << std::endl; + for (auto const& bigO : bigOs) { + os << "|" << std::setw(14) << std::setprecision(7) << std::scientific << bigO.constant() << " "; + os << "|" << detail::fmt::Number(6, 1, bigO.normalizedRootMeanSquare() * 100.0) << "% "; + os << "| " << bigO.name(); + os << std::endl; + } + return os; +} + +} // namespace nanobench +} // namespace ankerl + +#endif // ANKERL_NANOBENCH_IMPLEMENT +#endif // ANKERL_NANOBENCH_H_INCLUDED |