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Merge bitcoin/bitcoin#22082: test: update nanobench from release 4.0.0 to 4.3.4

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44d05d0a69c14ed295b0a7f6c8ec4379d44155e4 test: remove sanitizer suppression for nanobench (Martin Ankerl)
e3c866e3ca85f841671a828712e6207e24d0d996 test: update nanobench from release 4.0.0 to 4.3.4 (Martin Ankerl)

Pull request description:

  This updates the third-party library nanobench with the latest release. It contains mostly minor bugfixes, a new pyperf output format, ability to suppress warnings with environment variable `NANOBENCH_SUPPRESS_WARNINGS`. Full changelog:

  v4.0.2
  * Changed `doNotOptimizeAway` to what google benchmark is doing. The old code did not work on some machines.
  * fix: display correct "total" value
  * minor Documentation updates

  v4.1.0
  * Updated link to new pyperf home
  * Adds ability to configure console output time unit
  *  Add support for environment variable `NANOBENCH_SUPPRESS_WARNINGS`
  * Nanobench is now usable with CMake's FetchContent (see documentation: https://nanobench.ankerl.com/tutorial.html#cmake-integration)

  v4.2.0
  * Ability to store and later compare results added, through `pyperf`.
  * See https://nanobench.ankerl.com/tutorial.html#pyperf-python-pyperf-module-output
  * Added lots of build targets to travis, similar to bitcoin's build.
  * Some minor API & documentation improvements

  v4.3.0
  * `ankerl::nanobench::Rng` can now return the state with `std::vector<uint64_t> Rng::state()`, and this can also be used to initialize the Rng.

  v4.3.1
  * Minor cmake improvements when integrationg as a third-party library: add alias `nanobench::nanobench`, default to C++17

  v4.3.2
  * Fixed a MSVC 2015 build problem
  * updates license to 2021.
  * build should now work with very old linux headers
  * Also disable UBSAN (bitcoin needed to add a suppression)

  v4.3.3
  * Do not use locale-dependent `std::to_string`

  v4.3.4
  * Add missing sanitizer suppression to `rotl`

ACKs for top commit:
  MarcoFalke:
    review ACK 44d05d0a69c14ed295b0a7f6c8ec4379d44155e4

Tree-SHA512: 3291c85057720cfc84a44bfaa305a7d0df4dc35779169d20de73d32e40d4cdbf3f005bf343f79710eca517441de2459e8118c195c5f5136f99d1f50ebd5dfd08
This commit is contained in:
MarcoFalke 2021-06-02 09:19:12 +02:00 committed by Konstantin Akimov
parent 3d2cea667b
commit 262c8b6f44
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GPG Key ID: 2176C4A5D01EA524
1 changed files with 187 additions and 52 deletions
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233
src/bench/nanobench.h
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@ -7,7 +7,7 @@
//
// Licensed under the MIT License <http://opensource.org/licenses/MIT>.
// SPDX-License-Identifier: MIT
// Copyright (c) 2019-2020 Martin Ankerl <martin.ankerl@gmail.com>
// Copyright (c) 2019-2021 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
@ -32,8 +32,8 @@
// 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
#define ANKERL_NANOBENCH_VERSION_MINOR 3 // backwards-compatible changes
#define ANKERL_NANOBENCH_VERSION_PATCH 4 // backwards-compatible bug fixes
///////////////////////////////////////////////////////////////////////////////////////////////////
// public facing api - as minimal as possible
@ -78,12 +78,20 @@
#if defined(ANKERL_NANOBENCH_LOG_ENABLED)
# include <iostream>
# define ANKERL_NANOBENCH_LOG(x) std::cout << __FUNCTION__ << "@" << __LINE__ << ": " << x << std::endl
# define ANKERL_NANOBENCH_LOG(x) \
do { \
std::cout << __FUNCTION__ << "@" << __LINE__ << ": " << x << std::endl; \
} while (0)
#else
# define ANKERL_NANOBENCH_LOG(x)
# define ANKERL_NANOBENCH_LOG(x) \
do { \
} while (0)
#endif
#if defined(__linux__) && !defined(ANKERL_NANOBENCH_DISABLE_PERF_COUNTERS)
#if defined(__linux__) && defined(PERF_EVENT_IOC_ID) && defined(PERF_COUNT_HW_REF_CPU_CYCLES) && defined(PERF_FLAG_FD_CLOEXEC) && \
!defined(ANKERL_NANOBENCH_DISABLE_PERF_COUNTERS)
// only enable perf counters on kernel 3.14 which seems to have all the necessary defines. The three PERF_... defines are not in
// kernel 2.6.32 (all others are).
# define ANKERL_NANOBENCH_PRIVATE_PERF_COUNTERS() 1
#else
# define ANKERL_NANOBENCH_PRIVATE_PERF_COUNTERS() 0
@ -173,7 +181,7 @@ class BigO;
* `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)}}`.
* * `{{median(<name>)}}` Calculate median of a measurement data set, e.g. `{{median(elapsed)}}`.
*
* * `{{average(<name>)}}` Average (mean) calculation.
*
@ -181,10 +189,11 @@ class BigO;
* 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}| \}
* \mathrm{MdAPE}(e) = \mathrm{med}\{| \frac{e_i - \mathrm{med}\{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$. All these results
* E.g. for *elapsed*: First, @f$ \mathrm{med}\{e\} @f$ calculates the median by sorting and then taking the middle element
* of all *elapsed* measurements. This is used to calculate the absolute percentage
* error to this median for each measurement, as in @f$ | \frac{e_i - \mathrm{med}\{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
@ -207,7 +216,7 @@ class BigO;
*
* * `{{#measurement}}` To access individual measurement results, open the begin tag for measurements.
*
* * `{{elapsed}}` Average elapsed time per iteration, in seconds.
* * `{{elapsed}}` Average elapsed wall clock 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.
@ -261,6 +270,7 @@ class BigO;
* :cpp:func:`templates::csv() <ankerl::nanobench::templates::csv()>`
* :cpp:func:`templates::json() <ankerl::nanobench::templates::json()>`
* :cpp:func:`templates::htmlBoxplot() <ankerl::nanobench::templates::htmlBoxplot()>`
* :cpp:func:`templates::pyperf() <ankerl::nanobench::templates::pyperf()>`
@endverbatim
*
@ -269,6 +279,7 @@ class BigO;
* @param out Output for the generated output.
*/
void render(char const* mustacheTemplate, Bench const& bench, std::ostream& out);
void render(std::string const& mustacheTemplate, Bench const& bench, std::ostream& out);
/**
* Same as render(char const* mustacheTemplate, Bench const& bench, std::ostream& out), but for when
@ -279,6 +290,7 @@ void render(char const* mustacheTemplate, Bench const& bench, std::ostream& out)
* @param out Output for the generated output.
*/
void render(char const* mustacheTemplate, std::vector<Result> const& results, std::ostream& out);
void render(std::string const& mustacheTemplate, std::vector<Result> const& results, std::ostream& out);
// Contains mustache-like templates
namespace templates {
@ -297,7 +309,7 @@ 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.
The output uses only the elapsed wall clock time, and displays each epoch as a single dot.
@verbatim embed:rst
See the tutorial at :ref:`tutorial-template-html` for an example.
@endverbatim
@ -306,6 +318,14 @@ char const* csv() noexcept;
*/
char const* htmlBoxplot() noexcept;
/*!
@brief Output in pyperf compatible JSON format, which can be used for more analyzations.
@verbatim embed:rst
See the tutorial at :ref:`tutorial-template-pyperf` for an example how to further analyze the output.
@endverbatim
*/
char const* pyperf() noexcept;
/*!
@brief Template to generate JSON data.
@ -369,6 +389,8 @@ struct Config {
uint64_t mEpochIterations{0}; // If not 0, run *exactly* these number of iterations per epoch.
uint64_t mWarmup = 0;
std::ostream* mOut = nullptr;
std::chrono::duration<double> mTimeUnit = std::chrono::nanoseconds{1};
std::string mTimeUnitName = "ns";
bool mShowPerformanceCounters = true;
bool mIsRelative = false;
@ -504,6 +526,7 @@ public:
*/
explicit Rng(uint64_t seed) noexcept;
Rng(uint64_t x, uint64_t y) noexcept;
Rng(std::vector<uint64_t> const& data);
/**
* Creates a copy of the Rng, thus the copy provides exactly the same random sequence as the original.
@ -558,6 +581,14 @@ public:
template <typename Container>
void shuffle(Container& container) noexcept;
/**
* Extracts the full state of the generator, e.g. for serialization. For this RNG this is just 2 values, but to stay API compatible
* with future implementations that potentially use more state, we use a vector.
*
* @return Vector containing the full state:
*/
std::vector<uint64_t> state() const;
private:
static constexpr uint64_t rotl(uint64_t x, unsigned k) noexcept;
@ -666,6 +697,19 @@ public:
Bench& unit(std::string const& unit);
ANKERL_NANOBENCH(NODISCARD) std::string const& unit() const noexcept;
/**
* @brief Sets the time unit to be used for the default output.
*
* Nanobench defaults to using ns (nanoseconds) as output in the markdown. For some benchmarks this is too coarse, so it is
* possible to configure this. E.g. use `timeUnit(1ms, "ms")` to show `ms/op` instead of `ns/op`.
*
* @param tu Time unit to display the results in, default is 1ns.
* @param tuName Name for the time unit, default is "ns"
*/
Bench& timeUnit(std::chrono::duration<double> const& tu, std::string const& tuName);
ANKERL_NANOBENCH(NODISCARD) std::string const& timeUnitName() const noexcept;
ANKERL_NANOBENCH(NODISCARD) std::chrono::duration<double> const& timeUnit() const noexcept;
/**
* @brief Set the output stream where the resulting markdown table will be printed to.
*
@ -916,6 +960,7 @@ public:
@endverbatim
*/
Bench& render(char const* templateContent, std::ostream& os);
Bench& render(std::string const& templateContent, std::ostream& os);
Bench& config(Config const& benchmarkConfig);
ANKERL_NANOBENCH(NODISCARD) Config const& config() const noexcept;
@ -945,23 +990,24 @@ void doNotOptimizeAway(T const& val);
#else
// see folly's Benchmark.h
// These assembly magic is directly from what Google Benchmark is doing. I have previously used what facebook's folly was doing, but
// this seemd to have compilation problems in some cases. Google Benchmark seemed to be the most well tested anyways.
// see https://github.com/google/benchmark/blob/master/include/benchmark/benchmark.h#L307
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;
void doNotOptimizeAway(T const& val) {
// NOLINTNEXTLINE(hicpp-no-assembler)
asm volatile("" : : "r,m"(val) : "memory");
}
template <typename T>
typename std::enable_if<!doNotOptimizeNeedsIndirect<T>()>::type doNotOptimizeAway(T const& val) {
void doNotOptimizeAway(T& val) {
# if defined(__clang__)
// NOLINTNEXTLINE(hicpp-no-assembler)
asm volatile("" ::"r"(val));
}
template <typename T>
typename std::enable_if<doNotOptimizeNeedsIndirect<T>()>::type doNotOptimizeAway(T const& val) {
asm volatile("" : "+r,m"(val) : : "memory");
# else
// NOLINTNEXTLINE(hicpp-no-assembler)
asm volatile("" ::"m"(val) : "memory");
asm volatile("" : "+m,r"(val) : : "memory");
# endif
}
#endif
@ -1067,7 +1113,7 @@ constexpr uint64_t(Rng::max)() {
return (std::numeric_limits<uint64_t>::max)();
}
ANKERL_NANOBENCH_NO_SANITIZE("integer")
ANKERL_NANOBENCH_NO_SANITIZE("integer", "undefined")
uint64_t Rng::operator()() noexcept {
auto x = mX;
@ -1077,7 +1123,7 @@ uint64_t Rng::operator()() noexcept {
return x;
}
ANKERL_NANOBENCH_NO_SANITIZE("integer")
ANKERL_NANOBENCH_NO_SANITIZE("integer", "undefined")
uint32_t Rng::bounded(uint32_t range) noexcept {
uint64_t r32 = static_cast<uint32_t>(operator()());
auto multiresult = r32 * range;
@ -1103,6 +1149,7 @@ void Rng::shuffle(Container& container) noexcept {
}
}
ANKERL_NANOBENCH_NO_SANITIZE("integer", "undefined")
constexpr uint64_t Rng::rotl(uint64_t x, unsigned k) noexcept {
return (x << k) | (x >> (64U - k));
}
@ -1306,6 +1353,30 @@ char const* htmlBoxplot() noexcept {
</html>)DELIM";
}
char const* pyperf() noexcept {
return R"DELIM({
"benchmarks": [
{
"runs": [
{
"values": [
{{#measurement}} {{elapsed}}{{^-last}},
{{/last}}{{/measurement}}
]
}
]
}
],
"metadata": {
"loops": {{sum(iterations)}},
"inner_loops": {{batch}},
"name": "{{title}}",
"unit": "second"
},
"version": "1.0"
})DELIM";
}
char const* json() noexcept {
return R"DELIM({
"results": [
@ -1410,6 +1481,7 @@ static std::vector<Node> parseMustacheTemplate(char const** tpl) {
}
static bool generateFirstLast(Node const& n, size_t idx, size_t size, std::ostream& out) {
ANKERL_NANOBENCH_LOG("n.type=" << static_cast<int>(n.type));
bool matchFirst = n == "-first";
bool matchLast = n == "-last";
if (!matchFirst && !matchLast) {
@ -1632,6 +1704,7 @@ namespace detail {
char const* getEnv(char const* name);
bool isEndlessRunning(std::string const& name);
bool isWarningsEnabled();
template <typename T>
T parseFile(std::string const& filename);
@ -1770,25 +1843,49 @@ void render(char const* mustacheTemplate, std::vector<Result> const& results, st
for (size_t i = 0; i < nbResults; ++i) {
generateResult(n.children, i, results, out);
}
} else if (n == "measurement") {
if (results.size() != 1) {
throw std::runtime_error(
"render: can only use section 'measurement' here if there is a single result, but there are " +
detail::fmt::to_s(results.size()));
}
// when we only have a single result, we can immediately go into its measurement.
auto const& r = results.front();
for (size_t i = 0; i < r.size(); ++i) {
generateResultMeasurement(n.children, i, r, out);
}
} else {
throw std::runtime_error("unknown section '" + std::string(n.begin, n.end) + "'");
throw std::runtime_error("render: unknown section '" + std::string(n.begin, n.end) + "'");
}
break;
case templates::Node::Type::tag:
if (results.size() == 1) {
// result & config are both supported there
generateResultTag(n, results.front(), out);
} else {
// 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(std::string const& mustacheTemplate, std::vector<Result> const& results, std::ostream& out) {
render(mustacheTemplate.c_str(), results, out);
}
void render(char const* mustacheTemplate, const Bench& bench, std::ostream& out) {
render(mustacheTemplate, bench.results(), out);
}
void render(std::string const& mustacheTemplate, const Bench& bench, std::ostream& out) {
render(mustacheTemplate.c_str(), bench.results(), out);
}
namespace detail {
PerformanceCounters& performanceCounters() {
@ -1837,6 +1934,12 @@ bool isEndlessRunning(std::string const& name) {
return nullptr != endless && endless == name;
}
// True when environment variable NANOBENCH_SUPPRESS_WARNINGS is either not set at all, or set to "0"
bool isWarningsEnabled() {
auto suppression = getEnv("NANOBENCH_SUPPRESS_WARNINGS");
return nullptr == suppression || suppression == std::string("0");
}
void gatherStabilityInformation(std::vector<std::string>& warnings, std::vector<std::string>& recommendations) {
warnings.clear();
recommendations.clear();
@ -1889,13 +1992,13 @@ void gatherStabilityInformation(std::vector<std::string>& warnings, std::vector<
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");
recommendations.emplace_back("Use 'pyperf system tune' before benchmarking. See https://github.com/psf/pyperf");
}
}
void printStabilityInformationOnce(std::ostream* outStream) {
static bool shouldPrint = true;
if (shouldPrint && outStream) {
if (shouldPrint && outStream && isWarningsEnabled()) {
auto& os = *outStream;
shouldPrint = false;
std::vector<std::string> warnings;
@ -1923,16 +2026,7 @@ uint64_t& singletonHeaderHash() noexcept {
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")
ANKERL_NANOBENCH_NO_SANITIZE("integer", "undefined")
inline uint64_t hash_combine(uint64_t seed, uint64_t val) {
return seed ^ (val + UINT64_C(0x9e3779b9) + (seed << 6U) + (seed >> 2U));
}
@ -2010,7 +2104,7 @@ struct IterationLogic::Impl {
return static_cast<uint64_t>(doubleNewIters + 0.5);
}
ANKERL_NANOBENCH_NO_SANITIZE("integer") void upscale(std::chrono::nanoseconds elapsed) {
ANKERL_NANOBENCH_NO_SANITIZE("integer", "undefined") 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) {
@ -2108,7 +2202,8 @@ struct IterationLogic::Impl {
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.timeUnitName() + "/" + mBench.unit(), "",
rMedian / (mBench.timeUnit().count() * 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);
@ -2140,16 +2235,19 @@ struct IterationLogic::Impl {
}
}
columns.emplace_back(12, 2, "total", "", mResult.sum(Result::Measure::elapsed));
columns.emplace_back(12, 2, "total", "", mResult.sumProduct(Result::Measure::iterations, Result::Measure::elapsed));
// write everything
auto& os = *mBench.output();
// combine all elements that are relevant for printing the header
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);
hash = hash_combine(std::hash<std::string>{}(mBench.unit()), hash);
hash = hash_combine(std::hash<std::string>{}(mBench.title()), hash);
hash = hash_combine(std::hash<std::string>{}(mBench.timeUnitName()), hash);
hash = hash_combine(std::hash<double>{}(mBench.timeUnit().count()), hash);
hash = hash_combine(std::hash<bool>{}(mBench.relative()), hash);
hash = hash_combine(std::hash<bool>{}(mBench.performanceCounters()), hash);
if (hash != singletonHeaderHash()) {
singletonHeaderHash() = hash;
@ -2177,7 +2275,7 @@ struct IterationLogic::Impl {
os << col.value();
}
os << "| ";
auto showUnstable = rErrorMedian >= 0.05;
auto showUnstable = isWarningsEnabled() && rErrorMedian >= 0.05;
if (showUnstable) {
os << ":wavy_dash: ";
}
@ -2305,7 +2403,7 @@ public:
}
template <typename Op>
ANKERL_NANOBENCH_NO_SANITIZE("integer")
ANKERL_NANOBENCH_NO_SANITIZE("integer", "undefined")
void calibrate(Op&& op) {
// clear current calibration data,
for (auto& v : mCalibratedOverhead) {
@ -2411,7 +2509,7 @@ bool LinuxPerformanceCounters::monitor(perf_hw_id hwId, LinuxPerformanceCounters
}
// overflow is ok, it's checked
ANKERL_NANOBENCH_NO_SANITIZE("integer")
ANKERL_NANOBENCH_NO_SANITIZE("integer", "undefined")
void LinuxPerformanceCounters::updateResults(uint64_t numIters) {
// clear old data
for (auto& id_value : mIdToTarget) {
@ -2963,6 +3061,20 @@ std::string const& Bench::unit() const noexcept {
return mConfig.mUnit;
}
Bench& Bench::timeUnit(std::chrono::duration<double> const& tu, std::string const& tuName) {
mConfig.mTimeUnit = tu;
mConfig.mTimeUnitName = tuName;
return *this;
}
std::string const& Bench::timeUnitName() const noexcept {
return mConfig.mTimeUnitName;
}
std::chrono::duration<double> const& Bench::timeUnit() const noexcept {
return mConfig.mTimeUnit;
}
// If benchmarkTitle differs from currently set title, the stored results will be cleared.
Bench& Bench::title(const char* benchmarkTitle) {
if (benchmarkTitle != mConfig.mBenchmarkTitle) {
@ -3083,6 +3195,11 @@ Bench& Bench::render(char const* templateContent, std::ostream& os) {
return *this;
}
Bench& Bench::render(std::string 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);
@ -3119,7 +3236,7 @@ Rng::Rng()
} while (mX == 0 && mY == 0);
}
ANKERL_NANOBENCH_NO_SANITIZE("integer")
ANKERL_NANOBENCH_NO_SANITIZE("integer", "undefined")
uint64_t splitMix64(uint64_t& state) noexcept {
uint64_t z = (state += UINT64_C(0x9e3779b97f4a7c15));
z = (z ^ (z >> 30U)) * UINT64_C(0xbf58476d1ce4e5b9);
@ -3145,6 +3262,24 @@ Rng Rng::copy() const noexcept {
return Rng{mX, mY};
}
Rng::Rng(std::vector<uint64_t> const& data)
: mX(0)
, mY(0) {
if (data.size() != 2) {
throw std::runtime_error("ankerl::nanobench::Rng::Rng: needed exactly 2 entries in data, but got " +
detail::fmt::to_s(data.size()));
}
mX = data[0];
mY = data[1];
}
std::vector<uint64_t> Rng::state() const {
std::vector<uint64_t> data(2);
data[0] = mX;
data[1] = mY;
return data;
}
BigO::RangeMeasure BigO::collectRangeMeasure(std::vector<Result> const& results) {
BigO::RangeMeasure rangeMeasure;
for (auto const& result : results) {