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// Copyright 2019 Google LLC
// SPDX-License-Identifier: Apache-2.0
//
// 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
//
//
// 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 "hwy/nanobenchmark.h"
#include <stdio.h>
#include <stdlib.h>
#include <time.h> // clock_gettime
#include <algorithm> // std::sort, std::find_if
#include <numeric> // std::iota
#include <random>
#include <vector>
#include "hwy/robust_statistics.h"
#include "hwy/timer-inl.h"
#include "hwy/timer.h"
namespace hwy {
namespace {
namespace timer = hwy::HWY_NAMESPACE::timer;
static const timer::Ticks timer_resolution = platform::TimerResolution();
// Estimates the expected value of "lambda" values with a variable number of
// samples until the variability "rel_mad" is less than "max_rel_mad".
template <class Lambda>
timer::Ticks SampleUntilStable(const double max_rel_mad, double* rel_mad,
const Params& p, const Lambda& lambda) {
// Choose initial samples_per_eval based on a single estimated duration.
timer::Ticks t0 = timer::Start();
lambda();
timer::Ticks t1 = timer::Stop(); // Caller checks HaveTimerStop
timer::Ticks est = t1 - t0;
static const double ticks_per_second = platform::InvariantTicksPerSecond();
const size_t ticks_per_eval =
static_cast<size_t>(ticks_per_second * p.seconds_per_eval);
size_t samples_per_eval = est == 0
? p.min_samples_per_eval
: static_cast<size_t>(ticks_per_eval / est);
samples_per_eval = HWY_MAX(samples_per_eval, p.min_samples_per_eval);
std::vector<timer::Ticks> samples;
samples.reserve(1 + samples_per_eval);
samples.push_back(est);
// Percentage is too strict for tiny differences, so also allow a small
// absolute "median absolute deviation".
const timer::Ticks max_abs_mad = (timer_resolution + 99) / 100;
*rel_mad = 0.0; // ensure initialized
for (size_t eval = 0; eval < p.max_evals; ++eval, samples_per_eval *= 2) {
samples.reserve(samples.size() + samples_per_eval);
for (size_t i = 0; i < samples_per_eval; ++i) {
t0 = timer::Start();
lambda();
t1 = timer::Stop(); // Caller checks HaveTimerStop
samples.push_back(t1 - t0);
}
if (samples.size() >= p.min_mode_samples) {
est = robust_statistics::Mode(samples.data(), samples.size());
} else {
// For "few" (depends also on the variance) samples, Median is safer.
est = robust_statistics::Median(samples.data(), samples.size());
}
NANOBENCHMARK_CHECK(est != 0);
// Median absolute deviation (mad) is a robust measure of 'variability'.
const timer::Ticks abs_mad = robust_statistics::MedianAbsoluteDeviation(
samples.data(), samples.size(), est);
*rel_mad = static_cast<double>(abs_mad) / static_cast<double>(est);
if (*rel_mad <= max_rel_mad || abs_mad <= max_abs_mad) {
if (p.verbose) {
printf("%6d samples => %5d (abs_mad=%4d, rel_mad=%4.2f%%)\n",
static_cast<int>(samples.size()), static_cast<int>(est),
static_cast<int>(abs_mad), *rel_mad * 100.0);
}
return est;
}
}
if (p.verbose) {
printf("WARNING: rel_mad=%4.2f%% still exceeds %4.2f%% after %6d samples\n",
*rel_mad * 100.0, max_rel_mad * 100.0,
static_cast<int>(samples.size()));
}
return est;
}
using InputVec = std::vector<FuncInput>;
// Returns vector of unique input values.
InputVec UniqueInputs(const FuncInput* inputs, const size_t num_inputs) {
InputVec unique(inputs, inputs + num_inputs);
std::sort(unique.begin(), unique.end());
unique.erase(std::unique(unique.begin(), unique.end()), unique.end());
return unique;
}
// Returns how often we need to call func for sufficient precision.
size_t NumSkip(const Func func, const uint8_t* arg, const InputVec& unique,
const Params& p) {
// Min elapsed ticks for any input.
timer::Ticks min_duration = ~timer::Ticks(0);
for (const FuncInput input : unique) {
double rel_mad;
const timer::Ticks total = SampleUntilStable(
p.target_rel_mad, &rel_mad, p,
[func, arg, input]() { PreventElision(func(arg, input)); });
min_duration = HWY_MIN(min_duration, total - timer_resolution);
}
// Number of repetitions required to reach the target resolution.
const size_t max_skip = p.precision_divisor;
// Number of repetitions given the estimated duration.
const size_t num_skip =
min_duration == 0
? 0
: static_cast<size_t>((max_skip + min_duration - 1) / min_duration);
if (p.verbose) {
printf("res=%d max_skip=%d min_dur=%d num_skip=%d\n",
static_cast<int>(timer_resolution), static_cast<int>(max_skip),
static_cast<int>(min_duration), static_cast<int>(num_skip));
}
return num_skip;
}
// Replicates inputs until we can omit "num_skip" occurrences of an input.
InputVec ReplicateInputs(const FuncInput* inputs, const size_t num_inputs,
const size_t num_unique, const size_t num_skip,
const Params& p) {
InputVec full;
if (num_unique == 1) {
full.assign(p.subset_ratio * num_skip, inputs[0]);
return full;
}
full.reserve(p.subset_ratio * num_skip * num_inputs);
for (size_t i = 0; i < p.subset_ratio * num_skip; ++i) {
full.insert(full.end(), inputs, inputs + num_inputs);
}
std::mt19937 rng;
std::shuffle(full.begin(), full.end(), rng);
return full;
}
// Copies the "full" to "subset" in the same order, but with "num_skip"
// randomly selected occurrences of "input_to_skip" removed.
void FillSubset(const InputVec& full, const FuncInput input_to_skip,
const size_t num_skip, InputVec* subset) {
const size_t count =
static_cast<size_t>(std::count(full.begin(), full.end(), input_to_skip));
// Generate num_skip random indices: which occurrence to skip.
std::vector<uint32_t> omit(count);
std::iota(omit.begin(), omit.end(), 0);
// omit[] is the same on every call, but that's OK because they identify the
// Nth instance of input_to_skip, so the position within full[] differs.
std::mt19937 rng;
std::shuffle(omit.begin(), omit.end(), rng);
omit.resize(num_skip);
std::sort(omit.begin(), omit.end());
uint32_t occurrence = ~0u; // 0 after preincrement
size_t idx_omit = 0; // cursor within omit[]
size_t idx_subset = 0; // cursor within *subset
for (const FuncInput next : full) {
if (next == input_to_skip) {
++occurrence;
// Haven't removed enough already
if (idx_omit < num_skip) {
// This one is up for removal
if (occurrence == omit[idx_omit]) {
++idx_omit;
continue;
}
}
}
if (idx_subset < subset->size()) {
(*subset)[idx_subset++] = next;
}
}
NANOBENCHMARK_CHECK(idx_subset == subset->size());
NANOBENCHMARK_CHECK(idx_omit == omit.size());
NANOBENCHMARK_CHECK(occurrence == count - 1);
}
// Returns total ticks elapsed for all inputs.
timer::Ticks TotalDuration(const Func func, const uint8_t* arg,
const InputVec* inputs, const Params& p,
double* max_rel_mad) {
double rel_mad;
const timer::Ticks duration =
SampleUntilStable(p.target_rel_mad, &rel_mad, p, [func, arg, inputs]() {
for (const FuncInput input : *inputs) {
PreventElision(func(arg, input));
}
});
*max_rel_mad = HWY_MAX(*max_rel_mad, rel_mad);
return duration;
}
// (Nearly) empty Func for measuring timer overhead/resolution.
HWY_NOINLINE FuncOutput EmptyFunc(const void* /*arg*/, const FuncInput input) {
return input;
}
// Returns overhead of accessing inputs[] and calling a function; this will
// be deducted from future TotalDuration return values.
timer::Ticks Overhead(const uint8_t* arg, const InputVec* inputs,
const Params& p) {
double rel_mad;
// Zero tolerance because repeatability is crucial and EmptyFunc is fast.
return SampleUntilStable(0.0, &rel_mad, p, [arg, inputs]() {
for (const FuncInput input : *inputs) {
PreventElision(EmptyFunc(arg, input));
}
});
}
} // namespace
HWY_DLLEXPORT int Unpredictable1() { return timer::Start() != ~0ULL; }
HWY_DLLEXPORT size_t Measure(const Func func, const uint8_t* arg,
const FuncInput* inputs, const size_t num_inputs,
Result* results, const Params& p) {
NANOBENCHMARK_CHECK(num_inputs != 0);
char cpu100[100];
if (!platform::HaveTimerStop(cpu100)) {
fprintf(stderr, "CPU '%s' does not support RDTSCP, skipping benchmark.\n",
cpu100);
return 0;
}
const InputVec& unique = UniqueInputs(inputs, num_inputs);
const size_t num_skip = NumSkip(func, arg, unique, p); // never 0
if (num_skip == 0) return 0; // NumSkip already printed error message
// (slightly less work on x86 to cast from signed integer)
const float mul = 1.0f / static_cast<float>(static_cast<int>(num_skip));
const InputVec& full =
ReplicateInputs(inputs, num_inputs, unique.size(), num_skip, p);
InputVec subset(full.size() - num_skip);
const timer::Ticks overhead = Overhead(arg, &full, p);
const timer::Ticks overhead_skip = Overhead(arg, &subset, p);
if (overhead < overhead_skip) {
fprintf(stderr, "Measurement failed: overhead %d < %d\n",
static_cast<int>(overhead), static_cast<int>(overhead_skip));
return 0;
}
if (p.verbose) {
printf("#inputs=%5d,%5d overhead=%5d,%5d\n", static_cast<int>(full.size()),
static_cast<int>(subset.size()), static_cast<int>(overhead),
static_cast<int>(overhead_skip));
}
double max_rel_mad = 0.0;
const timer::Ticks total = TotalDuration(func, arg, &full, p, &max_rel_mad);
for (size_t i = 0; i < unique.size(); ++i) {
FillSubset(full, unique[i], num_skip, &subset);
const timer::Ticks total_skip =
TotalDuration(func, arg, &subset, p, &max_rel_mad);
if (total < total_skip) {
fprintf(stderr, "Measurement failed: total %f < %f\n",
static_cast<double>(total), static_cast<double>(total_skip));
return 0;
}
const timer::Ticks duration =
(total - overhead) - (total_skip - overhead_skip);
results[i].input = unique[i];
results[i].ticks = static_cast<float>(duration) * mul;
results[i].variability = static_cast<float>(max_rel_mad);
}
return unique.size();
}
} // namespace hwy