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// Copyright 2016 Ismael Jimenez Martinez. All rights reserved.
// Copyright 2017 Roman Lebedev. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "statistics.h"
#include <algorithm>
#include <cmath>
#include <numeric>
#include <string>
#include <vector>
#include "benchmark/benchmark.h"
#include "check.h"
namespace benchmark {
auto StatisticsSum = [](const std::vector<double>& v) {
return std::accumulate(v.begin(), v.end(), 0.0);
};
double StatisticsMean(const std::vector<double>& v) {
if (v.empty()) return 0.0;
return StatisticsSum(v) * (1.0 / v.size());
}
double StatisticsMedian(const std::vector<double>& v) {
if (v.size() < 3) return StatisticsMean(v);
std::vector<double> copy(v);
auto center = copy.begin() + v.size() / 2;
std::nth_element(copy.begin(), center, copy.end());
// did we have an odd number of samples?
// if yes, then center is the median
// it no, then we are looking for the average between center and the value
// before
if (v.size() % 2 == 1) return *center;
auto center2 = copy.begin() + v.size() / 2 - 1;
std::nth_element(copy.begin(), center2, copy.end());
return (*center + *center2) / 2.0;
}
// Return the sum of the squares of this sample set
auto SumSquares = [](const std::vector<double>& v) {
return std::inner_product(v.begin(), v.end(), v.begin(), 0.0);
};
auto Sqr = [](const double dat) { return dat * dat; };
auto Sqrt = [](const double dat) {
// Avoid NaN due to imprecision in the calculations
if (dat < 0.0) return 0.0;
return std::sqrt(dat);
};
double StatisticsStdDev(const std::vector<double>& v) {
const auto mean = StatisticsMean(v);
if (v.empty()) return mean;
// Sample standard deviation is undefined for n = 1
if (v.size() == 1) return 0.0;
const double avg_squares = SumSquares(v) * (1.0 / v.size());
return Sqrt(v.size() / (v.size() - 1.0) * (avg_squares - Sqr(mean)));
}
double StatisticsCV(const std::vector<double>& v) {
if (v.size() < 2) return 0.0;
const auto stddev = StatisticsStdDev(v);
const auto mean = StatisticsMean(v);
return stddev / mean;
}
std::vector<BenchmarkReporter::Run> ComputeStats(
const std::vector<BenchmarkReporter::Run>& reports) {
typedef BenchmarkReporter::Run Run;
std::vector<Run> results;
auto error_count =
std::count_if(reports.begin(), reports.end(),
[](Run const& run) { return run.error_occurred; });
if (reports.size() - error_count < 2) {
// We don't report aggregated data if there was a single run.
return results;
}
// Accumulators.
std::vector<double> real_accumulated_time_stat;
std::vector<double> cpu_accumulated_time_stat;
real_accumulated_time_stat.reserve(reports.size());
cpu_accumulated_time_stat.reserve(reports.size());
// All repetitions should be run with the same number of iterations so we
// can take this information from the first benchmark.
const IterationCount run_iterations = reports.front().iterations;
// create stats for user counters
struct CounterStat {
Counter c;
std::vector<double> s;
};
std::map<std::string, CounterStat> counter_stats;
for (Run const& r : reports) {
for (auto const& cnt : r.counters) {
auto it = counter_stats.find(cnt.first);
if (it == counter_stats.end()) {
it = counter_stats
.emplace(cnt.first,
CounterStat{cnt.second, std::vector<double>{}})
.first;
it->second.s.reserve(reports.size());
} else {
BM_CHECK_EQ(it->second.c.flags, cnt.second.flags);
}
}
}
// Populate the accumulators.
for (Run const& run : reports) {
BM_CHECK_EQ(reports[0].benchmark_name(), run.benchmark_name());
BM_CHECK_EQ(run_iterations, run.iterations);
if (run.error_occurred) continue;
real_accumulated_time_stat.emplace_back(run.real_accumulated_time);
cpu_accumulated_time_stat.emplace_back(run.cpu_accumulated_time);
// user counters
for (auto const& cnt : run.counters) {
auto it = counter_stats.find(cnt.first);
BM_CHECK_NE(it, counter_stats.end());
it->second.s.emplace_back(cnt.second);
}
}
// Only add label if it is same for all runs
std::string report_label = reports[0].report_label;
for (std::size_t i = 1; i < reports.size(); i++) {
if (reports[i].report_label != report_label) {
report_label = "";
break;
}
}
const double iteration_rescale_factor =
double(reports.size()) / double(run_iterations);
for (const auto& Stat : *reports[0].statistics) {
// Get the data from the accumulator to BenchmarkReporter::Run's.
Run data;
data.run_name = reports[0].run_name;
data.family_index = reports[0].family_index;
data.per_family_instance_index = reports[0].per_family_instance_index;
data.run_type = BenchmarkReporter::Run::RT_Aggregate;
data.threads = reports[0].threads;
data.repetitions = reports[0].repetitions;
data.repetition_index = Run::no_repetition_index;
data.aggregate_name = Stat.name_;
data.aggregate_unit = Stat.unit_;
data.report_label = report_label;
// It is incorrect to say that an aggregate is computed over
// run's iterations, because those iterations already got averaged.
// Similarly, if there are N repetitions with 1 iterations each,
// an aggregate will be computed over N measurements, not 1.
// Thus it is best to simply use the count of separate reports.
data.iterations = reports.size();
data.real_accumulated_time = Stat.compute_(real_accumulated_time_stat);
data.cpu_accumulated_time = Stat.compute_(cpu_accumulated_time_stat);
if (data.aggregate_unit == StatisticUnit::kTime) {
// We will divide these times by data.iterations when reporting, but the
// data.iterations is not necessarily the scale of these measurements,
// because in each repetition, these timers are sum over all the iters.
// And if we want to say that the stats are over N repetitions and not
// M iterations, we need to multiply these by (N/M).
data.real_accumulated_time *= iteration_rescale_factor;
data.cpu_accumulated_time *= iteration_rescale_factor;
}
data.time_unit = reports[0].time_unit;
// user counters
for (auto const& kv : counter_stats) {
// Do *NOT* rescale the custom counters. They are already properly scaled.
const auto uc_stat = Stat.compute_(kv.second.s);
auto c = Counter(uc_stat, counter_stats[kv.first].c.flags,
counter_stats[kv.first].c.oneK);
data.counters[kv.first] = c;
}
results.push_back(data);
}
return results;
}
} // end namespace benchmark
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