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// Copyright 2019 The Abseil Authors.
//
// 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
//
// https://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 "absl/profiling/internal/exponential_biased.h"
#include <stdint.h>
#include <algorithm>
#include <atomic>
#include <cmath>
#include <limits>
#include "absl/base/attributes.h"
#include "absl/base/optimization.h"
namespace absl {
ABSL_NAMESPACE_BEGIN
namespace profiling_internal {
// The algorithm generates a random number between 0 and 1 and applies the
// inverse cumulative distribution function for an exponential. Specifically:
// Let m be the inverse of the sample period, then the probability
// distribution function is m*exp(-mx) so the CDF is
// p = 1 - exp(-mx), so
// q = 1 - p = exp(-mx)
// log_e(q) = -mx
// -log_e(q)/m = x
// log_2(q) * (-log_e(2) * 1/m) = x
// In the code, q is actually in the range 1 to 2**26, hence the -26 below
int64_t ExponentialBiased::GetSkipCount(int64_t mean) {
if (ABSL_PREDICT_FALSE(!initialized_)) {
Initialize();
}
uint64_t rng = NextRandom(rng_);
rng_ = rng;
// Take the top 26 bits as the random number
// (This plus the 1<<58 sampling bound give a max possible step of
// 5194297183973780480 bytes.)
// The uint32_t cast is to prevent a (hard-to-reproduce) NAN
// under piii debug for some binaries.
double q = static_cast<uint32_t>(rng >> (kPrngNumBits - 26)) + 1.0;
// Put the computed p-value through the CDF of a geometric.
double interval = bias_ + (std::log2(q) - 26) * (-std::log(2.0) * mean);
// Very large values of interval overflow int64_t. To avoid that, we will
// cheat and clamp any huge values to (int64_t max)/2. This is a potential
// source of bias, but the mean would need to be such a large value that it's
// not likely to come up. For example, with a mean of 1e18, the probability of
// hitting this condition is about 1/1000. For a mean of 1e17, standard
// calculators claim that this event won't happen.
if (interval > static_cast<double>(std::numeric_limits<int64_t>::max() / 2)) {
// Assume huge values are bias neutral, retain bias for next call.
return std::numeric_limits<int64_t>::max() / 2;
}
double value = std::rint(interval);
bias_ = interval - value;
return value;
}
int64_t ExponentialBiased::GetStride(int64_t mean) {
return GetSkipCount(mean - 1) + 1;
}
void ExponentialBiased::Initialize() {
// We don't get well distributed numbers from `this` so we call NextRandom() a
// bunch to mush the bits around. We use a global_rand to handle the case
// where the same thread (by memory address) gets created and destroyed
// repeatedly.
ABSL_CONST_INIT static std::atomic<uint32_t> global_rand(0);
uint64_t r = reinterpret_cast<uint64_t>(this) +
global_rand.fetch_add(1, std::memory_order_relaxed);
for (int i = 0; i < 20; ++i) {
r = NextRandom(r);
}
rng_ = r;
initialized_ = true;
}
} // namespace profiling_internal
ABSL_NAMESPACE_END
} // namespace absl

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// Copyright 2019 The Abseil Authors.
//
// 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
//
// https://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.
#ifndef ABSL_PROFILING_INTERNAL_EXPONENTIAL_BIASED_H_
#define ABSL_PROFILING_INTERNAL_EXPONENTIAL_BIASED_H_
#include <stdint.h>
#include "absl/base/config.h"
#include "absl/base/macros.h"
namespace absl {
ABSL_NAMESPACE_BEGIN
namespace profiling_internal {
// ExponentialBiased provides a small and fast random number generator for a
// rounded exponential distribution. This generator manages very little state,
// and imposes no synchronization overhead. This makes it useful in specialized
// scenarios requiring minimum overhead, such as stride based periodic sampling.
//
// ExponentialBiased provides two closely related functions, GetSkipCount() and
// GetStride(), both returning a rounded integer defining a number of events
// required before some event with a given mean probability occurs.
//
// The distribution is useful to generate a random wait time or some periodic
// event with a given mean probability. For example, if an action is supposed to
// happen on average once every 'N' events, then we can get a random 'stride'
// counting down how long before the event to happen. For example, if we'd want
// to sample one in every 1000 'Frobber' calls, our code could look like this:
//
// Frobber::Frobber() {
// stride_ = exponential_biased_.GetStride(1000);
// }
//
// void Frobber::Frob(int arg) {
// if (--stride == 0) {
// SampleFrob(arg);
// stride_ = exponential_biased_.GetStride(1000);
// }
// ...
// }
//
// The rounding of the return value creates a bias, especially for smaller means
// where the distribution of the fraction is not evenly distributed. We correct
// this bias by tracking the fraction we rounded up or down on each iteration,
// effectively tracking the distance between the cumulative value, and the
// rounded cumulative value. For example, given a mean of 2:
//
// raw = 1.63076, cumulative = 1.63076, rounded = 2, bias = -0.36923
// raw = 0.14624, cumulative = 1.77701, rounded = 2, bias = 0.14624
// raw = 4.93194, cumulative = 6.70895, rounded = 7, bias = -0.06805
// raw = 0.24206, cumulative = 6.95101, rounded = 7, bias = 0.24206
// etc...
//
// Adjusting with rounding bias is relatively trivial:
//
// double value = bias_ + exponential_distribution(mean)();
// double rounded_value = std::rint(value);
// bias_ = value - rounded_value;
// return rounded_value;
//
// This class is thread-compatible.
class ExponentialBiased {
public:
// The number of bits set by NextRandom.
static constexpr int kPrngNumBits = 48;
// `GetSkipCount()` returns the number of events to skip before some chosen
// event happens. For example, randomly tossing a coin, we will on average
// throw heads once before we get tails. We can simulate random coin tosses
// using GetSkipCount() as:
//
// ExponentialBiased eb;
// for (...) {
// int number_of_heads_before_tail = eb.GetSkipCount(1);
// for (int flips = 0; flips < number_of_heads_before_tail; ++flips) {
// printf("head...");
// }
// printf("tail\n");
// }
//
int64_t GetSkipCount(int64_t mean);
// GetStride() returns the number of events required for a specific event to
// happen. See the class comments for a usage example. `GetStride()` is
// equivalent to `GetSkipCount(mean - 1) + 1`. When to use `GetStride()` or
// `GetSkipCount()` depends mostly on what best fits the use case.
int64_t GetStride(int64_t mean);
// Computes a random number in the range [0, 1<<(kPrngNumBits+1) - 1]
//
// This is public to enable testing.
static uint64_t NextRandom(uint64_t rnd);
private:
void Initialize();
uint64_t rng_{0};
double bias_{0};
bool initialized_{false};
};
// Returns the next prng value.
// pRNG is: aX+b mod c with a = 0x5DEECE66D, b = 0xB, c = 1<<48
// This is the lrand64 generator.
inline uint64_t ExponentialBiased::NextRandom(uint64_t rnd) {
const uint64_t prng_mult = uint64_t{0x5DEECE66D};
const uint64_t prng_add = 0xB;
const uint64_t prng_mod_power = 48;
const uint64_t prng_mod_mask =
~((~static_cast<uint64_t>(0)) << prng_mod_power);
return (prng_mult * rnd + prng_add) & prng_mod_mask;
}
} // namespace profiling_internal
ABSL_NAMESPACE_END
} // namespace absl
#endif // ABSL_PROFILING_INTERNAL_EXPONENTIAL_BIASED_H_

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// Copyright 2019 The Abseil Authors.
//
// 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
//
// https://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 "absl/profiling/internal/exponential_biased.h"
#include <stddef.h>
#include <cmath>
#include <cstdint>
#include <vector>
#include "gmock/gmock.h"
#include "gtest/gtest.h"
#include "absl/strings/str_cat.h"
using ::testing::Ge;
namespace absl {
ABSL_NAMESPACE_BEGIN
namespace profiling_internal {
namespace {
MATCHER_P2(IsBetween, a, b,
absl::StrCat(std::string(negation ? "isn't" : "is"), " between ", a,
" and ", b)) {
return a <= arg && arg <= b;
}
// Tests of the quality of the random numbers generated
// This uses the Anderson Darling test for uniformity.
// See "Evaluating the Anderson-Darling Distribution" by Marsaglia
// for details.
// Short cut version of ADinf(z), z>0 (from Marsaglia)
// This returns the p-value for Anderson Darling statistic in
// the limit as n-> infinity. For finite n, apply the error fix below.
double AndersonDarlingInf(double z) {
if (z < 2) {
return exp(-1.2337141 / z) / sqrt(z) *
(2.00012 +
(0.247105 -
(0.0649821 - (0.0347962 - (0.011672 - 0.00168691 * z) * z) * z) *
z) *
z);
}
return exp(
-exp(1.0776 -
(2.30695 -
(0.43424 - (0.082433 - (0.008056 - 0.0003146 * z) * z) * z) * z) *
z));
}
// Corrects the approximation error in AndersonDarlingInf for small values of n
// Add this to AndersonDarlingInf to get a better approximation
// (from Marsaglia)
double AndersonDarlingErrFix(int n, double x) {
if (x > 0.8) {
return (-130.2137 +
(745.2337 -
(1705.091 - (1950.646 - (1116.360 - 255.7844 * x) * x) * x) * x) *
x) /
n;
}
double cutoff = 0.01265 + 0.1757 / n;
if (x < cutoff) {
double t = x / cutoff;
t = sqrt(t) * (1 - t) * (49 * t - 102);
return t * (0.0037 / (n * n) + 0.00078 / n + 0.00006) / n;
} else {
double t = (x - cutoff) / (0.8 - cutoff);
t = -0.00022633 +
(6.54034 - (14.6538 - (14.458 - (8.259 - 1.91864 * t) * t) * t) * t) *
t;
return t * (0.04213 + 0.01365 / n) / n;
}
}
// Returns the AndersonDarling p-value given n and the value of the statistic
double AndersonDarlingPValue(int n, double z) {
double ad = AndersonDarlingInf(z);
double errfix = AndersonDarlingErrFix(n, ad);
return ad + errfix;
}
double AndersonDarlingStatistic(const std::vector<double>& random_sample) {
size_t n = random_sample.size();
double ad_sum = 0;
for (size_t i = 0; i < n; i++) {
ad_sum += (2 * i + 1) *
std::log(random_sample[i] * (1 - random_sample[n - 1 - i]));
}
const auto n_as_double = static_cast<double>(n);
double ad_statistic = -n_as_double - 1 / n_as_double * ad_sum;
return ad_statistic;
}
// Tests if the array of doubles is uniformly distributed.
// Returns the p-value of the Anderson Darling Statistic
// for the given set of sorted random doubles
// See "Evaluating the Anderson-Darling Distribution" by
// Marsaglia and Marsaglia for details.
double AndersonDarlingTest(const std::vector<double>& random_sample) {
double ad_statistic = AndersonDarlingStatistic(random_sample);
double p = AndersonDarlingPValue(static_cast<int>(random_sample.size()),
ad_statistic);
return p;
}
TEST(ExponentialBiasedTest, CoinTossDemoWithGetSkipCount) {
ExponentialBiased eb;
for (int runs = 0; runs < 10; ++runs) {
for (int64_t flips = eb.GetSkipCount(1); flips > 0; --flips) {
printf("head...");
}
printf("tail\n");
}
int heads = 0;
for (int i = 0; i < 10000000; i += 1 + eb.GetSkipCount(1)) {
++heads;
}
printf("Heads = %d (%f%%)\n", heads, 100.0 * heads / 10000000);
}
TEST(ExponentialBiasedTest, SampleDemoWithStride) {
ExponentialBiased eb;
int64_t stride = eb.GetStride(10);
int samples = 0;
for (int i = 0; i < 10000000; ++i) {
if (--stride == 0) {
++samples;
stride = eb.GetStride(10);
}
}
printf("Samples = %d (%f%%)\n", samples, 100.0 * samples / 10000000);
}
// Testing that NextRandom generates uniform random numbers. Applies the
// Anderson-Darling test for uniformity
TEST(ExponentialBiasedTest, TestNextRandom) {
for (auto n : std::vector<size_t>({
10, // Check short-range correlation
100, 1000,
10000 // Make sure there's no systemic error
})) {
uint64_t x = 1;
// This assumes that the prng returns 48 bit numbers
uint64_t max_prng_value = static_cast<uint64_t>(1) << 48;
// Initialize.
for (int i = 1; i <= 20; i++) {
x = ExponentialBiased::NextRandom(x);
}
std::vector<uint64_t> int_random_sample(n);
// Collect samples
for (size_t i = 0; i < n; i++) {
int_random_sample[i] = x;
x = ExponentialBiased::NextRandom(x);
}
// First sort them...
std::sort(int_random_sample.begin(), int_random_sample.end());
std::vector<double> random_sample(n);
// Convert them to uniform randoms (in the range [0,1])
for (size_t i = 0; i < n; i++) {
random_sample[i] =
static_cast<double>(int_random_sample[i]) / max_prng_value;
}
// Now compute the Anderson-Darling statistic
double ad_pvalue = AndersonDarlingTest(random_sample);
EXPECT_GT(std::min(ad_pvalue, 1 - ad_pvalue), 0.0001)
<< "prng is not uniform: n = " << n << " p = " << ad_pvalue;
}
}
// The generator needs to be available as a thread_local and as a static
// variable.
TEST(ExponentialBiasedTest, InitializationModes) {
ABSL_CONST_INIT static ExponentialBiased eb_static;
EXPECT_THAT(eb_static.GetSkipCount(2), Ge(0));
#ifdef ABSL_HAVE_THREAD_LOCAL
thread_local ExponentialBiased eb_thread;
EXPECT_THAT(eb_thread.GetSkipCount(2), Ge(0));
#endif
ExponentialBiased eb_stack;
EXPECT_THAT(eb_stack.GetSkipCount(2), Ge(0));
}
} // namespace
} // namespace profiling_internal
ABSL_NAMESPACE_END
} // namespace absl

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// Copyright 2019 The Abseil Authors.
//
// 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
//
// https://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 "absl/profiling/internal/periodic_sampler.h"
#include <atomic>
#include "absl/profiling/internal/exponential_biased.h"
namespace absl {
ABSL_NAMESPACE_BEGIN
namespace profiling_internal {
int64_t PeriodicSamplerBase::GetExponentialBiased(int period) noexcept {
return rng_.GetStride(period);
}
bool PeriodicSamplerBase::SubtleConfirmSample() noexcept {
int current_period = period();
// Deal with period case 0 (always off) and 1 (always on)
if (ABSL_PREDICT_FALSE(current_period < 2)) {
stride_ = 0;
return current_period == 1;
}
// Check if this is the first call to Sample()
if (ABSL_PREDICT_FALSE(stride_ == 1)) {
stride_ = static_cast<uint64_t>(-GetExponentialBiased(current_period));
if (static_cast<int64_t>(stride_) < -1) {
++stride_;
return false;
}
}
stride_ = static_cast<uint64_t>(-GetExponentialBiased(current_period));
return true;
}
} // namespace profiling_internal
ABSL_NAMESPACE_END
} // namespace absl

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// Copyright 2019 The Abseil Authors.
//
// 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
//
// https://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.
#ifndef ABSL_PROFILING_INTERNAL_PERIODIC_SAMPLER_H_
#define ABSL_PROFILING_INTERNAL_PERIODIC_SAMPLER_H_
#include <stdint.h>
#include <atomic>
#include "absl/base/optimization.h"
#include "absl/profiling/internal/exponential_biased.h"
namespace absl {
ABSL_NAMESPACE_BEGIN
namespace profiling_internal {
// PeriodicSamplerBase provides the basic period sampler implementation.
//
// This is the base class for the templated PeriodicSampler class, which holds
// a global std::atomic value identified by a user defined tag, such that
// each specific PeriodSampler implementation holds its own global period.
//
// PeriodicSamplerBase is thread-compatible except where stated otherwise.
class PeriodicSamplerBase {
public:
// PeriodicSamplerBase is trivial / copyable / movable / destructible.
PeriodicSamplerBase() = default;
PeriodicSamplerBase(PeriodicSamplerBase&&) = default;
PeriodicSamplerBase(const PeriodicSamplerBase&) = default;
// Returns true roughly once every `period` calls. This is established by a
// randomly picked `stride` that is counted down on each call to `Sample`.
// This stride is picked such that the probability of `Sample()` returning
// true is 1 in `period`.
inline bool Sample() noexcept;
// The below methods are intended for optimized use cases where the
// size of the inlined fast path code is highly important. Applications
// should use the `Sample()` method unless they have proof that their
// specific use case requires the optimizations offered by these methods.
//
// An example of such a use case is SwissTable sampling. All sampling checks
// are in inlined SwissTable methods, and the number of call sites is huge.
// In this case, the inlined code size added to each translation unit calling
// SwissTable methods is non-trivial.
//
// The `SubtleMaybeSample()` function spuriously returns true even if the
// function should not be sampled, applications MUST match each call to
// 'SubtleMaybeSample()' returning true with a `SubtleConfirmSample()` call,
// and use the result of the latter as the sampling decision.
// In other words: the code should logically be equivalent to:
//
// if (SubtleMaybeSample() && SubtleConfirmSample()) {
// // Sample this call
// }
//
// In the 'inline-size' optimized case, the `SubtleConfirmSample()` call can
// be placed out of line, for example, the typical use case looks as follows:
//
// // --- frobber.h -----------
// void FrobberSampled();
//
// inline void FrobberImpl() {
// // ...
// }
//
// inline void Frobber() {
// if (ABSL_PREDICT_FALSE(sampler.SubtleMaybeSample())) {
// FrobberSampled();
// } else {
// FrobberImpl();
// }
// }
//
// // --- frobber.cc -----------
// void FrobberSampled() {
// if (!sampler.SubtleConfirmSample())) {
// // Spurious false positive
// FrobberImpl();
// return;
// }
//
// // Sampled execution
// // ...
// }
inline bool SubtleMaybeSample() noexcept;
bool SubtleConfirmSample() noexcept;
protected:
// We explicitly don't use a virtual destructor as this class is never
// virtually destroyed, and it keeps the class trivial, which avoids TLS
// prologue and epilogue code for our TLS instances.
~PeriodicSamplerBase() = default;
// Returns the next stride for our sampler.
// This function is virtual for testing purposes only.
virtual int64_t GetExponentialBiased(int period) noexcept;
private:
// Returns the current period of this sampler. Thread-safe.
virtual int period() const noexcept = 0;
// Keep and decrement stride_ as an unsigned integer, but compare the value
// to zero casted as a signed int. clang and msvc do not create optimum code
// if we use signed for the combined decrement and sign comparison.
//
// Below 3 alternative options, all compiles generate the best code
// using the unsigned increment <---> signed int comparison option.
//
// Option 1:
// int64_t stride_;
// if (ABSL_PREDICT_TRUE(++stride_ < 0)) { ... }
//
// GCC x64 (OK) : https://gcc.godbolt.org/z/R5MzzA
// GCC ppc (OK) : https://gcc.godbolt.org/z/z7NZAt
// Clang x64 (BAD): https://gcc.godbolt.org/z/t4gPsd
// ICC x64 (OK) : https://gcc.godbolt.org/z/rE6s8W
// MSVC x64 (OK) : https://gcc.godbolt.org/z/ARMXqS
//
// Option 2:
// int64_t stride_ = 0;
// if (ABSL_PREDICT_TRUE(--stride_ >= 0)) { ... }
//
// GCC x64 (OK) : https://gcc.godbolt.org/z/jSQxYK
// GCC ppc (OK) : https://gcc.godbolt.org/z/VJdYaA
// Clang x64 (BAD): https://gcc.godbolt.org/z/Xm4NjX
// ICC x64 (OK) : https://gcc.godbolt.org/z/4snaFd
// MSVC x64 (BAD): https://gcc.godbolt.org/z/BgnEKE
//
// Option 3:
// uint64_t stride_;
// if (ABSL_PREDICT_TRUE(static_cast<int64_t>(++stride_) < 0)) { ... }
//
// GCC x64 (OK) : https://gcc.godbolt.org/z/bFbfPy
// GCC ppc (OK) : https://gcc.godbolt.org/z/S9KkUE
// Clang x64 (OK) : https://gcc.godbolt.org/z/UYzRb4
// ICC x64 (OK) : https://gcc.godbolt.org/z/ptTNfD
// MSVC x64 (OK) : https://gcc.godbolt.org/z/76j4-5
uint64_t stride_ = 0;
absl::profiling_internal::ExponentialBiased rng_;
};
inline bool PeriodicSamplerBase::SubtleMaybeSample() noexcept {
// See comments on `stride_` for the unsigned increment / signed compare.
if (ABSL_PREDICT_TRUE(static_cast<int64_t>(++stride_) < 0)) {
return false;
}
return true;
}
inline bool PeriodicSamplerBase::Sample() noexcept {
return ABSL_PREDICT_FALSE(SubtleMaybeSample()) ? SubtleConfirmSample()
: false;
}
// PeriodicSampler is a concreted periodic sampler implementation.
// The user provided Tag identifies the implementation, and is required to
// isolate the global state of this instance from other instances.
//
// Typical use case:
//
// struct HashTablezTag {};
// thread_local PeriodicSampler<HashTablezTag, 100> sampler;
//
// void HashTableSamplingLogic(...) {
// if (sampler.Sample()) {
// HashTableSlowSamplePath(...);
// }
// }
//
template <typename Tag, int default_period = 0>
class PeriodicSampler final : public PeriodicSamplerBase {
public:
~PeriodicSampler() = default;
int period() const noexcept final {
return period_.load(std::memory_order_relaxed);
}
// Sets the global period for this sampler. Thread-safe.
// Setting a period of 0 disables the sampler, i.e., every call to Sample()
// will return false. Setting a period of 1 puts the sampler in 'always on'
// mode, i.e., every call to Sample() returns true.
static void SetGlobalPeriod(int period) {
period_.store(period, std::memory_order_relaxed);
}
private:
static std::atomic<int> period_;
};
template <typename Tag, int default_period>
std::atomic<int> PeriodicSampler<Tag, default_period>::period_(default_period);
} // namespace profiling_internal
ABSL_NAMESPACE_END
} // namespace absl
#endif // ABSL_PROFILING_INTERNAL_PERIODIC_SAMPLER_H_

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// Copyright 2019 The Abseil Authors.
//
// 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
//
// https://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 "absl/profiling/internal/periodic_sampler.h"
#include "benchmark/benchmark.h"
namespace absl {
ABSL_NAMESPACE_BEGIN
namespace profiling_internal {
namespace {
template <typename Sampler>
void BM_Sample(Sampler* sampler, benchmark::State& state) {
for (auto _ : state) {
benchmark::DoNotOptimize(sampler);
benchmark::DoNotOptimize(sampler->Sample());
}
}
template <typename Sampler>
void BM_SampleMinunumInlined(Sampler* sampler, benchmark::State& state) {
for (auto _ : state) {
benchmark::DoNotOptimize(sampler);
if (ABSL_PREDICT_FALSE(sampler->SubtleMaybeSample())) {
benchmark::DoNotOptimize(sampler->SubtleConfirmSample());
}
}
}
void BM_PeriodicSampler_TinySample(benchmark::State& state) {
struct Tag {};
PeriodicSampler<Tag, 10> sampler;
BM_Sample(&sampler, state);
}
BENCHMARK(BM_PeriodicSampler_TinySample);
void BM_PeriodicSampler_ShortSample(benchmark::State& state) {
struct Tag {};
PeriodicSampler<Tag, 1024> sampler;
BM_Sample(&sampler, state);
}
BENCHMARK(BM_PeriodicSampler_ShortSample);
void BM_PeriodicSampler_LongSample(benchmark::State& state) {
struct Tag {};
PeriodicSampler<Tag, 1024 * 1024> sampler;
BM_Sample(&sampler, state);
}
BENCHMARK(BM_PeriodicSampler_LongSample);
void BM_PeriodicSampler_LongSampleMinunumInlined(benchmark::State& state) {
struct Tag {};
PeriodicSampler<Tag, 1024 * 1024> sampler;
BM_SampleMinunumInlined(&sampler, state);
}
BENCHMARK(BM_PeriodicSampler_LongSampleMinunumInlined);
void BM_PeriodicSampler_Disabled(benchmark::State& state) {
struct Tag {};
PeriodicSampler<Tag, 0> sampler;
BM_Sample(&sampler, state);
}
BENCHMARK(BM_PeriodicSampler_Disabled);
} // namespace
} // namespace profiling_internal
ABSL_NAMESPACE_END
} // namespace absl

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// Copyright 2019 The Abseil Authors.
//
// 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
//
// https://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 "absl/profiling/internal/periodic_sampler.h"
#include <thread> // NOLINT(build/c++11)
#include "gmock/gmock.h"
#include "gtest/gtest.h"
#include "absl/base/attributes.h"
#include "absl/base/macros.h"
namespace absl {
ABSL_NAMESPACE_BEGIN
namespace profiling_internal {
namespace {
using testing::Eq;
using testing::Return;
using testing::StrictMock;
class MockPeriodicSampler : public PeriodicSamplerBase {
public:
virtual ~MockPeriodicSampler() = default;
MOCK_METHOD(int, period, (), (const, noexcept));
MOCK_METHOD(int64_t, GetExponentialBiased, (int), (noexcept));
};
TEST(PeriodicSamplerBaseTest, Sample) {
StrictMock<MockPeriodicSampler> sampler;
EXPECT_CALL(sampler, period()).Times(3).WillRepeatedly(Return(16));
EXPECT_CALL(sampler, GetExponentialBiased(16))
.WillOnce(Return(2))
.WillOnce(Return(3))
.WillOnce(Return(4));
EXPECT_FALSE(sampler.Sample());
EXPECT_TRUE(sampler.Sample());
EXPECT_FALSE(sampler.Sample());
EXPECT_FALSE(sampler.Sample());
EXPECT_TRUE(sampler.Sample());
EXPECT_FALSE(sampler.Sample());
EXPECT_FALSE(sampler.Sample());
EXPECT_FALSE(sampler.Sample());
}
TEST(PeriodicSamplerBaseTest, ImmediatelySample) {
StrictMock<MockPeriodicSampler> sampler;
EXPECT_CALL(sampler, period()).Times(2).WillRepeatedly(Return(16));
EXPECT_CALL(sampler, GetExponentialBiased(16))
.WillOnce(Return(1))
.WillOnce(Return(2))
.WillOnce(Return(3));
EXPECT_TRUE(sampler.Sample());
EXPECT_FALSE(sampler.Sample());
EXPECT_TRUE(sampler.Sample());
EXPECT_FALSE(sampler.Sample());
EXPECT_FALSE(sampler.Sample());
}
TEST(PeriodicSamplerBaseTest, Disabled) {
StrictMock<MockPeriodicSampler> sampler;
EXPECT_CALL(sampler, period()).Times(3).WillRepeatedly(Return(0));
EXPECT_FALSE(sampler.Sample());
EXPECT_FALSE(sampler.Sample());
EXPECT_FALSE(sampler.Sample());
}
TEST(PeriodicSamplerBaseTest, AlwaysOn) {
StrictMock<MockPeriodicSampler> sampler;
EXPECT_CALL(sampler, period()).Times(3).WillRepeatedly(Return(1));
EXPECT_TRUE(sampler.Sample());
EXPECT_TRUE(sampler.Sample());
EXPECT_TRUE(sampler.Sample());
}
TEST(PeriodicSamplerBaseTest, Disable) {
StrictMock<MockPeriodicSampler> sampler;
EXPECT_CALL(sampler, period()).WillOnce(Return(16));
EXPECT_CALL(sampler, GetExponentialBiased(16)).WillOnce(Return(3));
EXPECT_FALSE(sampler.Sample());
EXPECT_FALSE(sampler.Sample());
EXPECT_CALL(sampler, period()).Times(2).WillRepeatedly(Return(0));
EXPECT_FALSE(sampler.Sample());
EXPECT_FALSE(sampler.Sample());
}
TEST(PeriodicSamplerBaseTest, Enable) {
StrictMock<MockPeriodicSampler> sampler;
EXPECT_CALL(sampler, period()).WillOnce(Return(0));
EXPECT_FALSE(sampler.Sample());
EXPECT_CALL(sampler, period()).Times(2).WillRepeatedly(Return(16));
EXPECT_CALL(sampler, GetExponentialBiased(16))
.Times(2)
.WillRepeatedly(Return(3));
EXPECT_FALSE(sampler.Sample());
EXPECT_FALSE(sampler.Sample());
EXPECT_TRUE(sampler.Sample());
EXPECT_FALSE(sampler.Sample());
EXPECT_FALSE(sampler.Sample());
}
TEST(PeriodicSamplerTest, ConstructConstInit) {
struct Tag {};
ABSL_CONST_INIT static PeriodicSampler<Tag> sampler;
(void)sampler;
}
TEST(PeriodicSamplerTest, DefaultPeriod0) {
struct Tag {};
PeriodicSampler<Tag> sampler;
EXPECT_THAT(sampler.period(), Eq(0));
}
TEST(PeriodicSamplerTest, DefaultPeriod) {
struct Tag {};
PeriodicSampler<Tag, 100> sampler;
EXPECT_THAT(sampler.period(), Eq(100));
}
TEST(PeriodicSamplerTest, SetGlobalPeriod) {
struct Tag1 {};
struct Tag2 {};
PeriodicSampler<Tag1, 25> sampler1;
PeriodicSampler<Tag2, 50> sampler2;
EXPECT_THAT(sampler1.period(), Eq(25));
EXPECT_THAT(sampler2.period(), Eq(50));
std::thread thread([] {
PeriodicSampler<Tag1, 25> sampler1;
PeriodicSampler<Tag2, 50> sampler2;
EXPECT_THAT(sampler1.period(), Eq(25));
EXPECT_THAT(sampler2.period(), Eq(50));
sampler1.SetGlobalPeriod(10);
sampler2.SetGlobalPeriod(20);
});
thread.join();
EXPECT_THAT(sampler1.period(), Eq(10));
EXPECT_THAT(sampler2.period(), Eq(20));
}
} // namespace
} // namespace profiling_internal
ABSL_NAMESPACE_END
} // namespace absl

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// Copyright 2018 The Abseil Authors.
//
// 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
//
// https://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.
//
// -----------------------------------------------------------------------------
// File: sample_recorder.h
// -----------------------------------------------------------------------------
//
// This header file defines a lock-free linked list for recording samples
// collected from a random/stochastic process.
//
// This utility is internal-only. Use at your own risk.
#ifndef ABSL_PROFILING_INTERNAL_SAMPLE_RECORDER_H_
#define ABSL_PROFILING_INTERNAL_SAMPLE_RECORDER_H_
#include <atomic>
#include <cstddef>
#include <functional>
#include "absl/base/config.h"
#include "absl/base/thread_annotations.h"
#include "absl/synchronization/mutex.h"
#include "absl/time/time.h"
namespace absl {
ABSL_NAMESPACE_BEGIN
namespace profiling_internal {
// Sample<T> that has members required for linking samples in the linked list of
// samples maintained by the SampleRecorder. Type T defines the sampled data.
template <typename T>
struct Sample {
// Guards the ability to restore the sample to a pristine state. This
// prevents races with sampling and resurrecting an object.
absl::Mutex init_mu;
T* next = nullptr;
T* dead ABSL_GUARDED_BY(init_mu) = nullptr;
int64_t weight; // How many sampling events were required to sample this one.
};
// Holds samples and their associated stack traces with a soft limit of
// `SetHashtablezMaxSamples()`.
//
// Thread safe.
template <typename T>
class SampleRecorder {
public:
SampleRecorder();
~SampleRecorder();
// Registers for sampling. Returns an opaque registration info.
template <typename... Targs>
T* Register(Targs&&... args);
// Unregisters the sample.
void Unregister(T* sample);
// The dispose callback will be called on all samples the moment they are
// being unregistered. Only affects samples that are unregistered after the
// callback has been set.
// Returns the previous callback.
using DisposeCallback = void (*)(const T&);
DisposeCallback SetDisposeCallback(DisposeCallback f);
// Iterates over all the registered `StackInfo`s. Returning the number of
// samples that have been dropped.
int64_t Iterate(const std::function<void(const T& stack)>& f);
size_t GetMaxSamples() const;
void SetMaxSamples(size_t max);
private:
void PushNew(T* sample);
void PushDead(T* sample);
template <typename... Targs>
T* PopDead(Targs... args);
std::atomic<size_t> dropped_samples_;
std::atomic<size_t> size_estimate_;
std::atomic<size_t> max_samples_{1 << 20};
// Intrusive lock free linked lists for tracking samples.
//
// `all_` records all samples (they are never removed from this list) and is
// terminated with a `nullptr`.
//
// `graveyard_.dead` is a circular linked list. When it is empty,
// `graveyard_.dead == &graveyard`. The list is circular so that
// every item on it (even the last) has a non-null dead pointer. This allows
// `Iterate` to determine if a given sample is live or dead using only
// information on the sample itself.
//
// For example, nodes [A, B, C, D, E] with [A, C, E] alive and [B, D] dead
// looks like this (G is the Graveyard):
//
// +---+ +---+ +---+ +---+ +---+
// all -->| A |--->| B |--->| C |--->| D |--->| E |
// | | | | | | | | | |
// +---+ | | +->| |-+ | | +->| |-+ | |
// | G | +---+ | +---+ | +---+ | +---+ | +---+
// | | | | | |
// | | --------+ +--------+ |
// +---+ |
// ^ |
// +--------------------------------------+
//
std::atomic<T*> all_;
T graveyard_;
std::atomic<DisposeCallback> dispose_;
};
template <typename T>
typename SampleRecorder<T>::DisposeCallback
SampleRecorder<T>::SetDisposeCallback(DisposeCallback f) {
return dispose_.exchange(f, std::memory_order_relaxed);
}
template <typename T>
SampleRecorder<T>::SampleRecorder()
: dropped_samples_(0), size_estimate_(0), all_(nullptr), dispose_(nullptr) {
absl::MutexLock l(&graveyard_.init_mu);
graveyard_.dead = &graveyard_;
}
template <typename T>
SampleRecorder<T>::~SampleRecorder() {
T* s = all_.load(std::memory_order_acquire);
while (s != nullptr) {
T* next = s->next;
delete s;
s = next;
}
}
template <typename T>
void SampleRecorder<T>::PushNew(T* sample) {
sample->next = all_.load(std::memory_order_relaxed);
while (!all_.compare_exchange_weak(sample->next, sample,
std::memory_order_release,
std::memory_order_relaxed)) {
}
}
template <typename T>
void SampleRecorder<T>::PushDead(T* sample) {
if (auto* dispose = dispose_.load(std::memory_order_relaxed)) {
dispose(*sample);
}
absl::MutexLock graveyard_lock(&graveyard_.init_mu);
absl::MutexLock sample_lock(&sample->init_mu);
sample->dead = graveyard_.dead;
graveyard_.dead = sample;
}
template <typename T>
template <typename... Targs>
T* SampleRecorder<T>::PopDead(Targs... args) {
absl::MutexLock graveyard_lock(&graveyard_.init_mu);
// The list is circular, so eventually it collapses down to
// graveyard_.dead == &graveyard_
// when it is empty.
T* sample = graveyard_.dead;
if (sample == &graveyard_) return nullptr;
absl::MutexLock sample_lock(&sample->init_mu);
graveyard_.dead = sample->dead;
sample->dead = nullptr;
sample->PrepareForSampling(std::forward<Targs>(args)...);
return sample;
}
template <typename T>
template <typename... Targs>
T* SampleRecorder<T>::Register(Targs&&... args) {
size_t size = size_estimate_.fetch_add(1, std::memory_order_relaxed);
if (size > max_samples_.load(std::memory_order_relaxed)) {
size_estimate_.fetch_sub(1, std::memory_order_relaxed);
dropped_samples_.fetch_add(1, std::memory_order_relaxed);
return nullptr;
}
T* sample = PopDead(args...);
if (sample == nullptr) {
// Resurrection failed. Hire a new warlock.
sample = new T();
{
absl::MutexLock sample_lock(&sample->init_mu);
// If flag initialization happens to occur (perhaps in another thread)
// while in this block, it will lock `graveyard_` which is usually always
// locked before any sample. This will appear as a lock inversion.
// However, this code is run exactly once per sample, and this sample
// cannot be accessed until after it is returned from this method. This
// means that this lock state can never be recreated, so we can safely
// inform the deadlock detector to ignore it.
sample->init_mu.ForgetDeadlockInfo();
sample->PrepareForSampling(std::forward<Targs>(args)...);
}
PushNew(sample);
}
return sample;
}
template <typename T>
void SampleRecorder<T>::Unregister(T* sample) {
PushDead(sample);
size_estimate_.fetch_sub(1, std::memory_order_relaxed);
}
template <typename T>
int64_t SampleRecorder<T>::Iterate(
const std::function<void(const T& stack)>& f) {
T* s = all_.load(std::memory_order_acquire);
while (s != nullptr) {
absl::MutexLock l(&s->init_mu);
if (s->dead == nullptr) {
f(*s);
}
s = s->next;
}
return dropped_samples_.load(std::memory_order_relaxed);
}
template <typename T>
void SampleRecorder<T>::SetMaxSamples(size_t max) {
max_samples_.store(max, std::memory_order_release);
}
template <typename T>
size_t SampleRecorder<T>::GetMaxSamples() const {
return max_samples_.load(std::memory_order_acquire);
}
} // namespace profiling_internal
ABSL_NAMESPACE_END
} // namespace absl
#endif // ABSL_PROFILING_INTERNAL_SAMPLE_RECORDER_H_

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// Copyright 2018 The Abseil Authors.
//
// 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
//
// https://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 "absl/profiling/internal/sample_recorder.h"
#include <atomic>
#include <random>
#include <vector>
#include "gmock/gmock.h"
#include "absl/base/thread_annotations.h"
#include "absl/synchronization/internal/thread_pool.h"
#include "absl/synchronization/mutex.h"
#include "absl/synchronization/notification.h"
#include "absl/time/time.h"
namespace absl {
ABSL_NAMESPACE_BEGIN
namespace profiling_internal {
namespace {
using ::absl::synchronization_internal::ThreadPool;
using ::testing::IsEmpty;
using ::testing::UnorderedElementsAre;
struct Info : public Sample<Info> {
public:
void PrepareForSampling(int64_t w) { weight = w; }
std::atomic<size_t> size;
absl::Time create_time;
};
std::vector<size_t> GetSizes(SampleRecorder<Info>* s) {
std::vector<size_t> res;
s->Iterate([&](const Info& info) {
res.push_back(info.size.load(std::memory_order_acquire));
});
return res;
}
std::vector<int64_t> GetWeights(SampleRecorder<Info>* s) {
std::vector<int64_t> res;
s->Iterate([&](const Info& info) { res.push_back(info.weight); });
return res;
}
Info* Register(SampleRecorder<Info>* s, int64_t weight, size_t size) {
auto* info = s->Register(weight);
assert(info != nullptr);
info->size.store(size);
return info;
}
TEST(SampleRecorderTest, Registration) {
SampleRecorder<Info> sampler;
auto* info1 = Register(&sampler, 31, 1);
EXPECT_THAT(GetSizes(&sampler), UnorderedElementsAre(1));
EXPECT_THAT(GetWeights(&sampler), UnorderedElementsAre(31));
auto* info2 = Register(&sampler, 32, 2);
EXPECT_THAT(GetSizes(&sampler), UnorderedElementsAre(1, 2));
info1->size.store(3);
EXPECT_THAT(GetSizes(&sampler), UnorderedElementsAre(3, 2));
EXPECT_THAT(GetWeights(&sampler), UnorderedElementsAre(31, 32));
sampler.Unregister(info1);
sampler.Unregister(info2);
}
TEST(SampleRecorderTest, Unregistration) {
SampleRecorder<Info> sampler;
std::vector<Info*> infos;
for (size_t i = 0; i < 3; ++i) {
infos.push_back(Register(&sampler, 33 + i, i));
}
EXPECT_THAT(GetSizes(&sampler), UnorderedElementsAre(0, 1, 2));
EXPECT_THAT(GetWeights(&sampler), UnorderedElementsAre(33, 34, 35));
sampler.Unregister(infos[1]);
EXPECT_THAT(GetSizes(&sampler), UnorderedElementsAre(0, 2));
EXPECT_THAT(GetWeights(&sampler), UnorderedElementsAre(33, 35));
infos.push_back(Register(&sampler, 36, 3));
infos.push_back(Register(&sampler, 37, 4));
EXPECT_THAT(GetSizes(&sampler), UnorderedElementsAre(0, 2, 3, 4));
EXPECT_THAT(GetWeights(&sampler), UnorderedElementsAre(33, 35, 36, 37));
sampler.Unregister(infos[3]);
EXPECT_THAT(GetSizes(&sampler), UnorderedElementsAre(0, 2, 4));
EXPECT_THAT(GetWeights(&sampler), UnorderedElementsAre(33, 35, 37));
sampler.Unregister(infos[0]);
sampler.Unregister(infos[2]);
sampler.Unregister(infos[4]);
EXPECT_THAT(GetSizes(&sampler), IsEmpty());
}
TEST(SampleRecorderTest, MultiThreaded) {
SampleRecorder<Info> sampler;
Notification stop;
ThreadPool pool(10);
for (int i = 0; i < 10; ++i) {
pool.Schedule([&sampler, &stop, i]() {
std::random_device rd;
std::mt19937 gen(rd());
std::vector<Info*> infoz;
while (!stop.HasBeenNotified()) {
if (infoz.empty()) {
infoz.push_back(sampler.Register(i));
}
switch (std::uniform_int_distribution<>(0, 2)(gen)) {
case 0: {
infoz.push_back(sampler.Register(i));
break;
}
case 1: {
size_t p =
std::uniform_int_distribution<>(0, infoz.size() - 1)(gen);
Info* info = infoz[p];
infoz[p] = infoz.back();
infoz.pop_back();
EXPECT_EQ(info->weight, i);
sampler.Unregister(info);
break;
}
case 2: {
absl::Duration oldest = absl::ZeroDuration();
sampler.Iterate([&](const Info& info) {
oldest = std::max(oldest, absl::Now() - info.create_time);
});
ASSERT_GE(oldest, absl::ZeroDuration());
break;
}
}
}
});
}
// The threads will hammer away. Give it a little bit of time for tsan to
// spot errors.
absl::SleepFor(absl::Seconds(3));
stop.Notify();
}
TEST(SampleRecorderTest, Callback) {
SampleRecorder<Info> sampler;
auto* info1 = Register(&sampler, 39, 1);
auto* info2 = Register(&sampler, 40, 2);
static const Info* expected;
auto callback = [](const Info& info) {
// We can't use `info` outside of this callback because the object will be
// disposed as soon as we return from here.
EXPECT_EQ(&info, expected);
};
// Set the callback.
EXPECT_EQ(sampler.SetDisposeCallback(callback), nullptr);
expected = info1;
sampler.Unregister(info1);
// Unset the callback.
EXPECT_EQ(callback, sampler.SetDisposeCallback(nullptr));
expected = nullptr; // no more calls.
sampler.Unregister(info2);
}
} // namespace
} // namespace profiling_internal
ABSL_NAMESPACE_END
} // namespace absl