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Move the async-using parts of benchmarking into a .cpp file
This keeps it out of the main include path when benchmarking is enabled, somewhat reducing the compilation-time penalty. Also moved some other functions into the .cpp file, especially helpers that could be given internal linkage, and concretized some iterator-templated code that only ever used `std::vector<double>::iterator`.
This commit is contained in:
Martin Hořeňovský
parent
b468d7cbff
commit
e640c3837a
223
include/internal/benchmark/detail/catch_stats.cpp
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223
include/internal/benchmark/detail/catch_stats.cpp
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@ -0,0 +1,223 @@
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/*
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* Created by Martin on 15/06/2019.
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* Adapted from donated nonius code.
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*
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* Distributed under the Boost Software License, Version 1.0. (See accompanying
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* file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
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*/
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// Statistical analysis tools
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#if defined(CATCH_CONFIG_ENABLE_BENCHMARKING)
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#include "catch_stats.hpp"
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#include "../../catch_compiler_capabilities.h"
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#include <cassert>
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#include <random>
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#if defined(CATCH_USE_ASYNC)
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#include <future>
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#endif
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namespace {
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double erf_inv(double x) {
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// Code accompanying the article "Approximating the erfinv function" in GPU Computing Gems, Volume 2
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double w, p;
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w = -log((1.0 - x) * (1.0 + x));
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if (w < 6.250000) {
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w = w - 3.125000;
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p = -3.6444120640178196996e-21;
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p = -1.685059138182016589e-19 + p * w;
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p = 1.2858480715256400167e-18 + p * w;
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p = 1.115787767802518096e-17 + p * w;
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p = -1.333171662854620906e-16 + p * w;
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p = 2.0972767875968561637e-17 + p * w;
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p = 6.6376381343583238325e-15 + p * w;
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p = -4.0545662729752068639e-14 + p * w;
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p = -8.1519341976054721522e-14 + p * w;
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p = 2.6335093153082322977e-12 + p * w;
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p = -1.2975133253453532498e-11 + p * w;
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p = -5.4154120542946279317e-11 + p * w;
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p = 1.051212273321532285e-09 + p * w;
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p = -4.1126339803469836976e-09 + p * w;
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p = -2.9070369957882005086e-08 + p * w;
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p = 4.2347877827932403518e-07 + p * w;
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p = -1.3654692000834678645e-06 + p * w;
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p = -1.3882523362786468719e-05 + p * w;
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p = 0.0001867342080340571352 + p * w;
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p = -0.00074070253416626697512 + p * w;
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p = -0.0060336708714301490533 + p * w;
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p = 0.24015818242558961693 + p * w;
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p = 1.6536545626831027356 + p * w;
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} else if (w < 16.000000) {
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w = sqrt(w) - 3.250000;
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p = 2.2137376921775787049e-09;
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p = 9.0756561938885390979e-08 + p * w;
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p = -2.7517406297064545428e-07 + p * w;
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p = 1.8239629214389227755e-08 + p * w;
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p = 1.5027403968909827627e-06 + p * w;
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p = -4.013867526981545969e-06 + p * w;
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p = 2.9234449089955446044e-06 + p * w;
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p = 1.2475304481671778723e-05 + p * w;
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p = -4.7318229009055733981e-05 + p * w;
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p = 6.8284851459573175448e-05 + p * w;
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p = 2.4031110387097893999e-05 + p * w;
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p = -0.0003550375203628474796 + p * w;
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p = 0.00095328937973738049703 + p * w;
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p = -0.0016882755560235047313 + p * w;
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p = 0.0024914420961078508066 + p * w;
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p = -0.0037512085075692412107 + p * w;
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p = 0.005370914553590063617 + p * w;
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p = 1.0052589676941592334 + p * w;
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p = 3.0838856104922207635 + p * w;
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} else {
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w = sqrt(w) - 5.000000;
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p = -2.7109920616438573243e-11;
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p = -2.5556418169965252055e-10 + p * w;
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p = 1.5076572693500548083e-09 + p * w;
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p = -3.7894654401267369937e-09 + p * w;
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p = 7.6157012080783393804e-09 + p * w;
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p = -1.4960026627149240478e-08 + p * w;
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p = 2.9147953450901080826e-08 + p * w;
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p = -6.7711997758452339498e-08 + p * w;
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p = 2.2900482228026654717e-07 + p * w;
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p = -9.9298272942317002539e-07 + p * w;
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p = 4.5260625972231537039e-06 + p * w;
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p = -1.9681778105531670567e-05 + p * w;
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p = 7.5995277030017761139e-05 + p * w;
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p = -0.00021503011930044477347 + p * w;
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p = -0.00013871931833623122026 + p * w;
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p = 1.0103004648645343977 + p * w;
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p = 4.8499064014085844221 + p * w;
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}
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return p * x;
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}
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double standard_deviation(std::vector<double>::iterator first, std::vector<double>::iterator last) {
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auto m = Catch::Benchmark::Detail::mean(first, last);
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double variance = std::accumulate(first, last, 0., [m](double a, double b) {
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double diff = b - m;
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return a + diff * diff;
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}) / (last - first);
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return std::sqrt(variance);
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}
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}
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namespace Catch {
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namespace Benchmark {
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namespace Detail {
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double weighted_average_quantile(int k, int q, std::vector<double>::iterator first, std::vector<double>::iterator last) {
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auto count = last - first;
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double idx = (count - 1) * k / static_cast<double>(q);
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int j = static_cast<int>(idx);
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double g = idx - j;
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std::nth_element(first, first + j, last);
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auto xj = first[j];
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if (g == 0) return xj;
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auto xj1 = *std::min_element(first + (j + 1), last);
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return xj + g * (xj1 - xj);
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}
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double erfc_inv(double x) {
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return erf_inv(1.0 - x);
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}
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double normal_quantile(double p) {
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static const double ROOT_TWO = std::sqrt(2.0);
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double result = 0.0;
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assert(p >= 0 && p <= 1);
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if (p < 0 || p > 1) {
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return result;
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}
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result = -erfc_inv(2.0 * p);
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// result *= normal distribution standard deviation (1.0) * sqrt(2)
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result *= /*sd * */ ROOT_TWO;
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// result += normal disttribution mean (0)
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return result;
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}
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double outlier_variance(Estimate<double> mean, Estimate<double> stddev, int n) {
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double sb = stddev.point;
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double mn = mean.point / n;
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double mg_min = mn / 2.;
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double sg = std::min(mg_min / 4., sb / std::sqrt(n));
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double sg2 = sg * sg;
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double sb2 = sb * sb;
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auto c_max = [n, mn, sb2, sg2](double x) -> double {
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double k = mn - x;
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double d = k * k;
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double nd = n * d;
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double k0 = -n * nd;
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double k1 = sb2 - n * sg2 + nd;
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double det = k1 * k1 - 4 * sg2 * k0;
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return (int)(-2. * k0 / (k1 + std::sqrt(det)));
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};
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auto var_out = [n, sb2, sg2](double c) {
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double nc = n - c;
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return (nc / n) * (sb2 - nc * sg2);
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};
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return std::min(var_out(1), var_out(std::min(c_max(0.), c_max(mg_min)))) / sb2;
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}
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bootstrap_analysis analyse_samples(double confidence_level, int n_resamples, std::vector<double>::iterator first, std::vector<double>::iterator last) {
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CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS
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static std::random_device entropy;
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CATCH_INTERNAL_UNSUPPRESS_GLOBALS_WARNINGS
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auto n = static_cast<int>(last - first); // seriously, one can't use integral types without hell in C++
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auto mean = &Detail::mean<std::vector<double>::iterator>;
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auto stddev = &standard_deviation;
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#ifdef CATCH_USE_ASYNC
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auto Estimate = [=](double(*f)(std::vector<double>::iterator, std::vector<double>::iterator)) {
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auto seed = entropy();
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return std::async(std::launch::async, [=] {
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std::mt19937 rng(seed);
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auto resampled = resample(rng, n_resamples, first, last, f);
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return bootstrap(confidence_level, first, last, resampled, f);
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});
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};
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auto mean_future = Estimate(mean);
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auto stddev_future = Estimate(stddev);
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auto mean_estimate = mean_future.get();
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auto stddev_estimate = stddev_future.get();
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#else
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auto Estimate = [=](double(*f)(std::vector<double>::iterator, std::vector<double>::iterator)) {
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auto seed = entropy();
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std::mt19937 rng(seed);
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auto resampled = resample(rng, n_resamples, first, last, f);
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return bootstrap(confidence_level, first, last, resampled, f);
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};
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auto mean_estimate = Estimate(mean);
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auto stddev_estimate = Estimate(stddev);
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#endif // CATCH_USE_ASYNC
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double outlier_variance = Detail::outlier_variance(mean_estimate, stddev_estimate, n);
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return { mean_estimate, stddev_estimate, outlier_variance };
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}
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} // namespace Detail
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} // namespace Benchmark
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} // namespace Catch
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#endif // CATCH_CONFIG_ENABLE_BENCHMARKING
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@ -16,40 +16,20 @@
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#include "../catch_outlier_classification.hpp"
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#include <algorithm>
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#include <cassert>
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#include <functional>
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#include <iterator>
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#include <vector>
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#include <array>
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#include <random>
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#include <numeric>
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#include <tuple>
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#include <cmath>
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#include <utility>
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#include <cstddef>
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#ifdef CATCH_USE_ASYNC
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#include <future>
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#endif
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namespace Catch {
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namespace Benchmark {
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namespace Detail {
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using sample = std::vector<double>;
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template <typename Iterator>
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double weighted_average_quantile(int k, int q, Iterator first, Iterator last) {
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auto count = last - first;
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double idx = (count - 1) * k / static_cast<double>(q);
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int j = static_cast<int>(idx);
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double g = idx - j;
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std::nth_element(first, first + j, last);
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auto xj = first[j];
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if (g == 0) return xj;
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auto xj1 = *std::min_element(first + (j + 1), last);
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return xj + g * (xj1 - xj);
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}
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double weighted_average_quantile(int k, int q, std::vector<double>::iterator first, std::vector<double>::iterator last);
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template <typename Iterator>
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OutlierClassification classify_outliers(Iterator first, Iterator last) {
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@ -82,16 +62,6 @@ namespace Catch {
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return sum / count;
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}
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template <typename Iterator>
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double standard_deviation(Iterator first, Iterator last) {
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auto m = mean(first, last);
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double variance = std::accumulate(first, last, 0., [m](double a, double b) {
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double diff = b - m;
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return a + diff * diff;
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}) / (last - first);
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return std::sqrt(variance);
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}
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template <typename URng, typename Iterator, typename Estimator>
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sample resample(URng& rng, int resamples, Iterator first, Iterator last, Estimator& estimator) {
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auto n = last - first;
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@ -128,100 +98,9 @@ namespace Catch {
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return std::erfc(-x / std::sqrt(2.0)) / 2.0;
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}
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inline double erf_inv(double x) {
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// Code accompanying the article "Approximating the erfinv function" in GPU Computing Gems, Volume 2
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double w, p;
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double erfc_inv(double x);
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w = -log((1.0 - x)*(1.0 + x));
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if (w < 6.250000) {
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w = w - 3.125000;
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p = -3.6444120640178196996e-21;
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p = -1.685059138182016589e-19 + p * w;
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p = 1.2858480715256400167e-18 + p * w;
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p = 1.115787767802518096e-17 + p * w;
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p = -1.333171662854620906e-16 + p * w;
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p = 2.0972767875968561637e-17 + p * w;
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p = 6.6376381343583238325e-15 + p * w;
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p = -4.0545662729752068639e-14 + p * w;
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p = -8.1519341976054721522e-14 + p * w;
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p = 2.6335093153082322977e-12 + p * w;
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p = -1.2975133253453532498e-11 + p * w;
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p = -5.4154120542946279317e-11 + p * w;
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p = 1.051212273321532285e-09 + p * w;
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p = -4.1126339803469836976e-09 + p * w;
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p = -2.9070369957882005086e-08 + p * w;
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p = 4.2347877827932403518e-07 + p * w;
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p = -1.3654692000834678645e-06 + p * w;
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p = -1.3882523362786468719e-05 + p * w;
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p = 0.0001867342080340571352 + p * w;
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p = -0.00074070253416626697512 + p * w;
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p = -0.0060336708714301490533 + p * w;
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p = 0.24015818242558961693 + p * w;
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p = 1.6536545626831027356 + p * w;
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} else if (w < 16.000000) {
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w = sqrt(w) - 3.250000;
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p = 2.2137376921775787049e-09;
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p = 9.0756561938885390979e-08 + p * w;
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p = -2.7517406297064545428e-07 + p * w;
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p = 1.8239629214389227755e-08 + p * w;
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p = 1.5027403968909827627e-06 + p * w;
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p = -4.013867526981545969e-06 + p * w;
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p = 2.9234449089955446044e-06 + p * w;
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p = 1.2475304481671778723e-05 + p * w;
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p = -4.7318229009055733981e-05 + p * w;
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p = 6.8284851459573175448e-05 + p * w;
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p = 2.4031110387097893999e-05 + p * w;
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p = -0.0003550375203628474796 + p * w;
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p = 0.00095328937973738049703 + p * w;
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p = -0.0016882755560235047313 + p * w;
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p = 0.0024914420961078508066 + p * w;
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p = -0.0037512085075692412107 + p * w;
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p = 0.005370914553590063617 + p * w;
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p = 1.0052589676941592334 + p * w;
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p = 3.0838856104922207635 + p * w;
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} else {
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w = sqrt(w) - 5.000000;
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p = -2.7109920616438573243e-11;
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p = -2.5556418169965252055e-10 + p * w;
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p = 1.5076572693500548083e-09 + p * w;
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p = -3.7894654401267369937e-09 + p * w;
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p = 7.6157012080783393804e-09 + p * w;
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p = -1.4960026627149240478e-08 + p * w;
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p = 2.9147953450901080826e-08 + p * w;
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p = -6.7711997758452339498e-08 + p * w;
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p = 2.2900482228026654717e-07 + p * w;
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p = -9.9298272942317002539e-07 + p * w;
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p = 4.5260625972231537039e-06 + p * w;
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p = -1.9681778105531670567e-05 + p * w;
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p = 7.5995277030017761139e-05 + p * w;
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p = -0.00021503011930044477347 + p * w;
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p = -0.00013871931833623122026 + p * w;
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p = 1.0103004648645343977 + p * w;
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p = 4.8499064014085844221 + p * w;
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}
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return p * x;
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}
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inline double erfc_inv(double x) {
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return erf_inv(1.0 - x);
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}
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inline double normal_quantile(double p) {
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static const double ROOT_TWO = std::sqrt(2.0);
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double result = 0.0;
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assert(p >= 0 && p <= 1);
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if (p < 0 || p > 1) {
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return result;
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}
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result = -erfc_inv(2.0 * p);
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// result *= normal distribution standard deviation (1.0) * sqrt(2)
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result *= /*sd * */ ROOT_TWO;
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// result += normal disttribution mean (0)
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return result;
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}
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double normal_quantile(double p);
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template <typename Iterator, typename Estimator>
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Estimate<double> bootstrap(double confidence_level, Iterator first, Iterator last, sample const& resample, Estimator&& estimator) {
|
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@ -263,31 +142,7 @@ namespace Catch {
|
||||
return { point, resample[lo], resample[hi], confidence_level };
|
||||
}
|
||||
|
||||
inline double outlier_variance(Estimate<double> mean, Estimate<double> stddev, int n) {
|
||||
double sb = stddev.point;
|
||||
double mn = mean.point / n;
|
||||
double mg_min = mn / 2.;
|
||||
double sg = std::min(mg_min / 4., sb / std::sqrt(n));
|
||||
double sg2 = sg * sg;
|
||||
double sb2 = sb * sb;
|
||||
|
||||
auto c_max = [n, mn, sb2, sg2](double x) -> double {
|
||||
double k = mn - x;
|
||||
double d = k * k;
|
||||
double nd = n * d;
|
||||
double k0 = -n * nd;
|
||||
double k1 = sb2 - n * sg2 + nd;
|
||||
double det = k1 * k1 - 4 * sg2 * k0;
|
||||
return (int)(-2. * k0 / (k1 + std::sqrt(det)));
|
||||
};
|
||||
|
||||
auto var_out = [n, sb2, sg2](double c) {
|
||||
double nc = n - c;
|
||||
return (nc / n) * (sb2 - nc * sg2);
|
||||
};
|
||||
|
||||
return std::min(var_out(1), var_out(std::min(c_max(0.), c_max(mg_min)))) / sb2;
|
||||
}
|
||||
double outlier_variance(Estimate<double> mean, Estimate<double> stddev, int n);
|
||||
|
||||
struct bootstrap_analysis {
|
||||
Estimate<double> mean;
|
||||
@ -295,46 +150,7 @@ namespace Catch {
|
||||
double outlier_variance;
|
||||
};
|
||||
|
||||
template <typename Iterator>
|
||||
bootstrap_analysis analyse_samples(double confidence_level, int n_resamples, Iterator first, Iterator last) {
|
||||
static std::random_device entropy;
|
||||
|
||||
auto n = static_cast<int>(last - first); // seriously, one can't use integral types without hell in C++
|
||||
|
||||
auto mean = &Detail::mean<Iterator>;
|
||||
auto stddev = &Detail::standard_deviation<Iterator>;
|
||||
|
||||
#ifdef CATCH_USE_ASYNC
|
||||
auto Estimate = [=](double(*f)(Iterator, Iterator)) {
|
||||
auto seed = entropy();
|
||||
return std::async(std::launch::async, [=] {
|
||||
std::mt19937 rng(seed);
|
||||
auto resampled = resample(rng, n_resamples, first, last, f);
|
||||
return bootstrap(confidence_level, first, last, resampled, f);
|
||||
});
|
||||
};
|
||||
|
||||
auto mean_future = Estimate(mean);
|
||||
auto stddev_future = Estimate(stddev);
|
||||
|
||||
auto mean_estimate = mean_future.get();
|
||||
auto stddev_estimate = stddev_future.get();
|
||||
#else
|
||||
auto Estimate = [=](double(*f)(Iterator, Iterator)) {
|
||||
auto seed = entropy();
|
||||
std::mt19937 rng(seed);
|
||||
auto resampled = resample(rng, n_resamples, first, last, f);
|
||||
return bootstrap(confidence_level, first, last, resampled, f);
|
||||
};
|
||||
|
||||
auto mean_estimate = Estimate(mean);
|
||||
auto stddev_estimate = Estimate(stddev);
|
||||
#endif // CATCH_USE_ASYNC
|
||||
|
||||
double outlier_variance = Detail::outlier_variance(mean_estimate, stddev_estimate, n);
|
||||
|
||||
return { mean_estimate, stddev_estimate, outlier_variance };
|
||||
}
|
||||
bootstrap_analysis analyse_samples(double confidence_level, int n_resamples, std::vector<double>::iterator first, std::vector<double>::iterator last);
|
||||
} // namespace Detail
|
||||
} // namespace Benchmark
|
||||
} // namespace Catch
|
||||
|
||||
@ -101,7 +101,12 @@ set(BENCHMARK_HEADERS
|
||||
${HEADER_DIR}/internal/benchmark/detail/catch_stats.hpp
|
||||
${HEADER_DIR}/internal/benchmark/detail/catch_timing.hpp
|
||||
)
|
||||
SOURCE_GROUP("benchmark" FILES ${BENCHMARK_HEADERS})
|
||||
set(BENCHMARK_SOURCES
|
||||
${HEADER_DIR}/internal/benchmark/detail/catch_stats.cpp
|
||||
)
|
||||
|
||||
SOURCE_GROUP("benchmark" FILES ${BENCHMARK_HEADERS} ${BENCHMARK_SOURCES})
|
||||
|
||||
set(INTERNAL_HEADERS
|
||||
${HEADER_DIR}/internal/catch_approx.h
|
||||
${HEADER_DIR}/internal/catch_assertionhandler.h
|
||||
@ -291,7 +296,8 @@ set(HEADERS
|
||||
${INTERNAL_HEADERS}
|
||||
${REPORTER_HEADERS}
|
||||
${BENCHMARK_HEADERS}
|
||||
)
|
||||
${BENCHMARK_SOURCES}
|
||||
)
|
||||
|
||||
# Provide some groupings for IDEs
|
||||
SOURCE_GROUP("Tests" FILES ${TEST_SOURCES})
|
||||
@ -328,7 +334,7 @@ endif()
|
||||
if ( CMAKE_CXX_COMPILER_ID MATCHES "Clang|AppleClang|GNU" )
|
||||
target_compile_options( SelfTest PRIVATE -Wall -Wextra -Wunreachable-code -Wpedantic -Wmissing-declarations )
|
||||
if (CATCH_ENABLE_WERROR)
|
||||
target_compile_options( SelfTest PRIVATE -Werror)
|
||||
target_compile_options( SelfTest PRIVATE -Werror )
|
||||
endif()
|
||||
endif()
|
||||
# Clang specific options go here
|
||||
|
||||
@ -53,7 +53,7 @@ def generate(v):
|
||||
out.write( line )
|
||||
|
||||
def insertCpps():
|
||||
dirs = [os.path.join( rootPath, s) for s in ['', 'internal', 'reporters']]
|
||||
dirs = [os.path.join( rootPath, s) for s in ['', 'internal', 'reporters', 'internal/benchmark', 'internal/benchmark/detail']]
|
||||
cppFiles = []
|
||||
for dir in dirs:
|
||||
cppFiles += glob(os.path.join(dir, '*.cpp'))
|
||||
|
||||
Loading…
Reference in New Issue
Block a user