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benchmarks.cc
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benchmarks.cc
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// Copyright 2017 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.
// Benchmarks for absl random distributions as well as a selection of the
// C++ standard library random distributions.
#include <algorithm>
#include <cstddef>
#include <cstdint>
#include <initializer_list>
#include <iterator>
#include <limits>
#include <random>
#include <type_traits>
#include <vector>
#include "absl/base/macros.h"
#include "absl/meta/type_traits.h"
#include "absl/random/bernoulli_distribution.h"
#include "absl/random/beta_distribution.h"
#include "absl/random/exponential_distribution.h"
#include "absl/random/gaussian_distribution.h"
#include "absl/random/internal/fast_uniform_bits.h"
#include "absl/random/internal/randen_engine.h"
#include "absl/random/log_uniform_int_distribution.h"
#include "absl/random/poisson_distribution.h"
#include "absl/random/random.h"
#include "absl/random/uniform_int_distribution.h"
#include "absl/random/uniform_real_distribution.h"
#include "absl/random/zipf_distribution.h"
#include "benchmark/benchmark.h"
namespace {
// Seed data to avoid reading random_device() for benchmarks.
uint32_t kSeedData[] = {
0x1B510052, 0x9A532915, 0xD60F573F, 0xBC9BC6E4, 0x2B60A476, 0x81E67400,
0x08BA6FB5, 0x571BE91F, 0xF296EC6B, 0x2A0DD915, 0xB6636521, 0xE7B9F9B6,
0xFF34052E, 0xC5855664, 0x53B02D5D, 0xA99F8FA1, 0x08BA4799, 0x6E85076A,
0x4B7A70E9, 0xB5B32944, 0xDB75092E, 0xC4192623, 0xAD6EA6B0, 0x49A7DF7D,
0x9CEE60B8, 0x8FEDB266, 0xECAA8C71, 0x699A18FF, 0x5664526C, 0xC2B19EE1,
0x193602A5, 0x75094C29, 0xA0591340, 0xE4183A3E, 0x3F54989A, 0x5B429D65,
0x6B8FE4D6, 0x99F73FD6, 0xA1D29C07, 0xEFE830F5, 0x4D2D38E6, 0xF0255DC1,
0x4CDD2086, 0x8470EB26, 0x6382E9C6, 0x021ECC5E, 0x09686B3F, 0x3EBAEFC9,
0x3C971814, 0x6B6A70A1, 0x687F3584, 0x52A0E286, 0x13198A2E, 0x03707344,
};
// PrecompiledSeedSeq provides kSeedData to a conforming
// random engine to speed initialization in the benchmarks.
class PrecompiledSeedSeq {
public:
using result_type = uint32_t;
PrecompiledSeedSeq() = default;
template <typename Iterator>
PrecompiledSeedSeq(Iterator begin, Iterator end) {}
template <typename T>
PrecompiledSeedSeq(std::initializer_list<T> il) {}
template <typename OutIterator>
void generate(OutIterator begin, OutIterator end) {
static size_t idx = 0;
for (; begin != end; begin++) {
*begin = kSeedData[idx++];
if (idx >= ABSL_ARRAYSIZE(kSeedData)) {
idx = 0;
}
}
}
size_t size() const { return ABSL_ARRAYSIZE(kSeedData); }
template <typename OutIterator>
void param(OutIterator out) const {
std::copy(std::begin(kSeedData), std::end(kSeedData), out);
}
};
// use_default_initialization<T> indicates whether the random engine
// T must be default initialized, or whether we may initialize it using
// a seed sequence. This is used because some engines do not accept seed
// sequence-based initialization.
template <typename E>
using use_default_initialization = std::false_type;
// make_engine<T, SSeq> returns a random_engine which is initialized,
// either via the default constructor, when use_default_initialization<T>
// is true, or via the indicated seed sequence, SSeq.
template <typename Engine, typename SSeq = PrecompiledSeedSeq>
typename absl::enable_if_t<!use_default_initialization<Engine>::value, Engine>
make_engine() {
// Initialize the random engine using the seed sequence SSeq, which
// is constructed from the precompiled seed data.
SSeq seq(std::begin(kSeedData), std::end(kSeedData));
return Engine(seq);
}
template <typename Engine, typename SSeq = PrecompiledSeedSeq>
typename absl::enable_if_t<use_default_initialization<Engine>::value, Engine>
make_engine() {
// Initialize the random engine using the default constructor.
return Engine();
}
template <typename Engine, typename SSeq>
void BM_Construct(benchmark::State& state) {
for (auto _ : state) {
auto rng = make_engine<Engine, SSeq>();
benchmark::DoNotOptimize(rng());
}
}
template <typename Engine>
void BM_Direct(benchmark::State& state) {
using value_type = typename Engine::result_type;
// Direct use of the URBG.
auto rng = make_engine<Engine>();
for (auto _ : state) {
benchmark::DoNotOptimize(rng());
}
state.SetBytesProcessed(sizeof(value_type) * state.iterations());
}
template <typename Engine>
void BM_Generate(benchmark::State& state) {
// std::generate makes a copy of the RNG; thus this tests the
// copy-constructor efficiency.
using value_type = typename Engine::result_type;
std::vector<value_type> v(64);
auto rng = make_engine<Engine>();
while (state.KeepRunningBatch(64)) {
std::generate(std::begin(v), std::end(v), rng);
}
}
template <typename Engine, size_t elems>
void BM_Shuffle(benchmark::State& state) {
// Direct use of the Engine.
std::vector<uint32_t> v(elems);
while (state.KeepRunningBatch(elems)) {
auto rng = make_engine<Engine>();
std::shuffle(std::begin(v), std::end(v), rng);
}
}
template <typename Engine, size_t elems>
void BM_ShuffleReuse(benchmark::State& state) {
// Direct use of the Engine.
std::vector<uint32_t> v(elems);
auto rng = make_engine<Engine>();
while (state.KeepRunningBatch(elems)) {
std::shuffle(std::begin(v), std::end(v), rng);
}
}
template <typename Engine, typename Dist, typename... Args>
void BM_Dist(benchmark::State& state, Args&&... args) {
using value_type = typename Dist::result_type;
auto rng = make_engine<Engine>();
Dist dis{std::forward<Args>(args)...};
// Compare the following loop performance:
for (auto _ : state) {
benchmark::DoNotOptimize(dis(rng));
}
state.SetBytesProcessed(sizeof(value_type) * state.iterations());
}
template <typename Engine, typename Dist>
void BM_Large(benchmark::State& state) {
using value_type = typename Dist::result_type;
volatile value_type kMin = 0;
volatile value_type kMax = std::numeric_limits<value_type>::max() / 2 + 1;
BM_Dist<Engine, Dist>(state, kMin, kMax);
}
template <typename Engine, typename Dist>
void BM_Small(benchmark::State& state) {
using value_type = typename Dist::result_type;
volatile value_type kMin = 0;
volatile value_type kMax = std::numeric_limits<value_type>::max() / 64 + 1;
BM_Dist<Engine, Dist>(state, kMin, kMax);
}
template <typename Engine, typename Dist, int A>
void BM_Bernoulli(benchmark::State& state) {
volatile double a = static_cast<double>(A) / 1000000;
BM_Dist<Engine, Dist>(state, a);
}
template <typename Engine, typename Dist, int A, int B>
void BM_Beta(benchmark::State& state) {
using value_type = typename Dist::result_type;
volatile value_type a = static_cast<value_type>(A) / 100;
volatile value_type b = static_cast<value_type>(B) / 100;
BM_Dist<Engine, Dist>(state, a, b);
}
template <typename Engine, typename Dist, int A>
void BM_Gamma(benchmark::State& state) {
using value_type = typename Dist::result_type;
volatile value_type a = static_cast<value_type>(A) / 100;
BM_Dist<Engine, Dist>(state, a);
}
template <typename Engine, typename Dist, int A = 100>
void BM_Poisson(benchmark::State& state) {
volatile double a = static_cast<double>(A) / 100;
BM_Dist<Engine, Dist>(state, a);
}
template <typename Engine, typename Dist, int Q = 2, int V = 1>
void BM_Zipf(benchmark::State& state) {
using value_type = typename Dist::result_type;
volatile double q = Q;
volatile double v = V;
BM_Dist<Engine, Dist>(state, std::numeric_limits<value_type>::max(), q, v);
}
template <typename Engine, typename Dist>
void BM_Thread(benchmark::State& state) {
using value_type = typename Dist::result_type;
auto rng = make_engine<Engine>();
Dist dis{};
for (auto _ : state) {
benchmark::DoNotOptimize(dis(rng));
}
state.SetBytesProcessed(sizeof(value_type) * state.iterations());
}
// NOTES:
//
// std::geometric_distribution is similar to the zipf distributions.
// The algorithm for the geometric_distribution is, basically,
// floor(log(1-X) / log(1-p))
// Normal benchmark suite
#define BM_BASIC(Engine) \
BENCHMARK_TEMPLATE(BM_Construct, Engine, PrecompiledSeedSeq); \
BENCHMARK_TEMPLATE(BM_Construct, Engine, std::seed_seq); \
BENCHMARK_TEMPLATE(BM_Direct, Engine); \
BENCHMARK_TEMPLATE(BM_Shuffle, Engine, 10); \
BENCHMARK_TEMPLATE(BM_Shuffle, Engine, 100); \
BENCHMARK_TEMPLATE(BM_Shuffle, Engine, 1000); \
BENCHMARK_TEMPLATE(BM_ShuffleReuse, Engine, 100); \
BENCHMARK_TEMPLATE(BM_ShuffleReuse, Engine, 1000); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, \
absl::random_internal::FastUniformBits<uint32_t>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, \
absl::random_internal::FastUniformBits<uint64_t>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, std::uniform_int_distribution<int32_t>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, std::uniform_int_distribution<int64_t>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, \
absl::uniform_int_distribution<int32_t>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, \
absl::uniform_int_distribution<int64_t>); \
BENCHMARK_TEMPLATE(BM_Large, Engine, \
std::uniform_int_distribution<int32_t>); \
BENCHMARK_TEMPLATE(BM_Large, Engine, \
std::uniform_int_distribution<int64_t>); \
BENCHMARK_TEMPLATE(BM_Large, Engine, \
absl::uniform_int_distribution<int32_t>); \
BENCHMARK_TEMPLATE(BM_Large, Engine, \
absl::uniform_int_distribution<int64_t>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, std::uniform_real_distribution<float>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, std::uniform_real_distribution<double>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, absl::uniform_real_distribution<float>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, absl::uniform_real_distribution<double>)
#define BM_COPY(Engine) BENCHMARK_TEMPLATE(BM_Generate, Engine)
#define BM_THREAD(Engine) \
BENCHMARK_TEMPLATE(BM_Thread, Engine, \
absl::uniform_int_distribution<int64_t>) \
->ThreadPerCpu(); \
BENCHMARK_TEMPLATE(BM_Thread, Engine, \
absl::uniform_real_distribution<double>) \
->ThreadPerCpu(); \
BENCHMARK_TEMPLATE(BM_Shuffle, Engine, 100)->ThreadPerCpu(); \
BENCHMARK_TEMPLATE(BM_Shuffle, Engine, 1000)->ThreadPerCpu(); \
BENCHMARK_TEMPLATE(BM_ShuffleReuse, Engine, 100)->ThreadPerCpu(); \
BENCHMARK_TEMPLATE(BM_ShuffleReuse, Engine, 1000)->ThreadPerCpu()
#define BM_EXTENDED(Engine) \
/* -------------- Extended Uniform -----------------------*/ \
BENCHMARK_TEMPLATE(BM_Small, Engine, \
std::uniform_int_distribution<int32_t>); \
BENCHMARK_TEMPLATE(BM_Small, Engine, \
std::uniform_int_distribution<int64_t>); \
BENCHMARK_TEMPLATE(BM_Small, Engine, \
absl::uniform_int_distribution<int32_t>); \
BENCHMARK_TEMPLATE(BM_Small, Engine, \
absl::uniform_int_distribution<int64_t>); \
BENCHMARK_TEMPLATE(BM_Small, Engine, std::uniform_real_distribution<float>); \
BENCHMARK_TEMPLATE(BM_Small, Engine, \
std::uniform_real_distribution<double>); \
BENCHMARK_TEMPLATE(BM_Small, Engine, \
absl::uniform_real_distribution<float>); \
BENCHMARK_TEMPLATE(BM_Small, Engine, \
absl::uniform_real_distribution<double>); \
/* -------------- Other -----------------------*/ \
BENCHMARK_TEMPLATE(BM_Dist, Engine, std::normal_distribution<double>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, absl::gaussian_distribution<double>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, std::exponential_distribution<double>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, absl::exponential_distribution<double>); \
BENCHMARK_TEMPLATE(BM_Poisson, Engine, std::poisson_distribution<int64_t>, \
100); \
BENCHMARK_TEMPLATE(BM_Poisson, Engine, absl::poisson_distribution<int64_t>, \
100); \
BENCHMARK_TEMPLATE(BM_Poisson, Engine, std::poisson_distribution<int64_t>, \
10 * 100); \
BENCHMARK_TEMPLATE(BM_Poisson, Engine, absl::poisson_distribution<int64_t>, \
10 * 100); \
BENCHMARK_TEMPLATE(BM_Poisson, Engine, std::poisson_distribution<int64_t>, \
13 * 100); \
BENCHMARK_TEMPLATE(BM_Poisson, Engine, absl::poisson_distribution<int64_t>, \
13 * 100); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, \
absl::log_uniform_int_distribution<int32_t>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, \
absl::log_uniform_int_distribution<int64_t>); \
BENCHMARK_TEMPLATE(BM_Dist, Engine, std::geometric_distribution<int64_t>); \
BENCHMARK_TEMPLATE(BM_Zipf, Engine, absl::zipf_distribution<uint64_t>); \
BENCHMARK_TEMPLATE(BM_Zipf, Engine, absl::zipf_distribution<uint64_t>, 2, \
3); \
BENCHMARK_TEMPLATE(BM_Bernoulli, Engine, std::bernoulli_distribution, \
257305); \
BENCHMARK_TEMPLATE(BM_Bernoulli, Engine, absl::bernoulli_distribution, \
257305); \
BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<double>, 65, \
41); \
BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<double>, 99, \
330); \
BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<double>, 150, \
150); \
BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<double>, 410, \
580); \
BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<float>, 65, 41); \
BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<float>, 99, \
330); \
BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<float>, 150, \
150); \
BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<float>, 410, \
580); \
BENCHMARK_TEMPLATE(BM_Gamma, Engine, std::gamma_distribution<float>, 199); \
BENCHMARK_TEMPLATE(BM_Gamma, Engine, std::gamma_distribution<double>, 199)
// ABSL Recommended interfaces.
BM_BASIC(absl::InsecureBitGen); // === pcg64_2018_engine
BM_BASIC(absl::BitGen); // === randen_engine<uint64_t>.
BM_THREAD(absl::BitGen);
BM_EXTENDED(absl::BitGen);
// Instantiate benchmarks for multiple engines.
using randen_engine_64 = absl::random_internal::randen_engine<uint64_t>;
using randen_engine_32 = absl::random_internal::randen_engine<uint32_t>;
// Comparison interfaces.
BM_BASIC(std::mt19937_64);
BM_COPY(std::mt19937_64);
BM_EXTENDED(std::mt19937_64);
BM_BASIC(randen_engine_64);
BM_COPY(randen_engine_64);
BM_EXTENDED(randen_engine_64);
BM_BASIC(std::mt19937);
BM_COPY(std::mt19937);
BM_BASIC(randen_engine_32);
BM_COPY(randen_engine_32);
} // namespace