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dawn-cmake/third_party/abseil-cpp/absl/hash/hash_benchmark.cc

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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 <string>
#include <type_traits>
#include <typeindex>
#include <utility>
#include <vector>
#include "absl/base/attributes.h"
#include "absl/container/flat_hash_set.h"
#include "absl/hash/hash.h"
#include "absl/random/random.h"
#include "absl/strings/cord.h"
#include "absl/strings/cord_test_helpers.h"
#include "absl/strings/string_view.h"
#include "benchmark/benchmark.h"
namespace {
using absl::Hash;
template <template <typename> class H, typename T>
void RunBenchmark(benchmark::State& state, T value) {
H<T> h;
for (auto _ : state) {
benchmark::DoNotOptimize(value);
benchmark::DoNotOptimize(h(value));
}
}
} // namespace
template <typename T>
using AbslHash = absl::Hash<T>;
class TypeErasedInterface {
public:
virtual ~TypeErasedInterface() = default;
template <typename H>
friend H AbslHashValue(H state, const TypeErasedInterface& wrapper) {
state = H::combine(std::move(state), std::type_index(typeid(wrapper)));
wrapper.HashValue(absl::HashState::Create(&state));
return state;
}
private:
virtual void HashValue(absl::HashState state) const = 0;
};
template <typename T>
struct TypeErasedAbslHash {
class Wrapper : public TypeErasedInterface {
public:
explicit Wrapper(const T& value) : value_(value) {}
private:
void HashValue(absl::HashState state) const override {
absl::HashState::combine(std::move(state), value_);
}
const T& value_;
};
size_t operator()(const T& value) {
return absl::Hash<Wrapper>{}(Wrapper(value));
}
};
template <typename FuncType>
inline FuncType* ODRUseFunction(FuncType* ptr) {
volatile FuncType* dummy = ptr;
return dummy;
}
absl::Cord FlatCord(size_t size) {
absl::Cord result(std::string(size, 'a'));
result.Flatten();
return result;
}
absl::Cord FragmentedCord(size_t size) {
const size_t orig_size = size;
std::vector<std::string> chunks;
size_t chunk_size = std::max<size_t>(1, size / 10);
while (size > chunk_size) {
chunks.push_back(std::string(chunk_size, 'a'));
size -= chunk_size;
}
if (size > 0) {
chunks.push_back(std::string(size, 'a'));
}
absl::Cord result = absl::MakeFragmentedCord(chunks);
(void) orig_size;
assert(result.size() == orig_size);
return result;
}
template <typename T>
std::vector<T> Vector(size_t count) {
std::vector<T> result;
for (size_t v = 0; v < count; ++v) {
result.push_back(v);
}
return result;
}
// Bogus type that replicates an unorderd_set's bit mixing, but with
// vector-speed iteration. This is intended to measure the overhead of unordered
// hashing without counting the speed of unordered_set iteration.
template <typename T>
struct FastUnorderedSet {
explicit FastUnorderedSet(size_t count) {
for (size_t v = 0; v < count; ++v) {
values.push_back(v);
}
}
std::vector<T> values;
template <typename H>
friend H AbslHashValue(H h, const FastUnorderedSet& fus) {
return H::combine(H::combine_unordered(std::move(h), fus.values.begin(),
fus.values.end()),
fus.values.size());
}
};
template <typename T>
absl::flat_hash_set<T> FlatHashSet(size_t count) {
absl::flat_hash_set<T> result;
for (size_t v = 0; v < count; ++v) {
result.insert(v);
}
return result;
}
// Generates a benchmark and a codegen method for the provided types. The
// codegen method provides a well known entrypoint for dumping assembly.
#define MAKE_BENCHMARK(hash, name, ...) \
namespace { \
void BM_##hash##_##name(benchmark::State& state) { \
RunBenchmark<hash>(state, __VA_ARGS__); \
} \
BENCHMARK(BM_##hash##_##name); \
} \
size_t Codegen##hash##name(const decltype(__VA_ARGS__)& arg); \
size_t Codegen##hash##name(const decltype(__VA_ARGS__)& arg) { \
return hash<decltype(__VA_ARGS__)>{}(arg); \
} \
bool absl_hash_test_odr_use##hash##name = \
ODRUseFunction(&Codegen##hash##name);
MAKE_BENCHMARK(AbslHash, Int32, int32_t{});
MAKE_BENCHMARK(AbslHash, Int64, int64_t{});
MAKE_BENCHMARK(AbslHash, Double, 1.2);
MAKE_BENCHMARK(AbslHash, DoubleZero, 0.0);
MAKE_BENCHMARK(AbslHash, PairInt32Int32, std::pair<int32_t, int32_t>{});
MAKE_BENCHMARK(AbslHash, PairInt64Int64, std::pair<int64_t, int64_t>{});
MAKE_BENCHMARK(AbslHash, TupleInt32BoolInt64,
std::tuple<int32_t, bool, int64_t>{});
MAKE_BENCHMARK(AbslHash, String_0, std::string());
MAKE_BENCHMARK(AbslHash, String_10, std::string(10, 'a'));
MAKE_BENCHMARK(AbslHash, String_30, std::string(30, 'a'));
MAKE_BENCHMARK(AbslHash, String_90, std::string(90, 'a'));
MAKE_BENCHMARK(AbslHash, String_200, std::string(200, 'a'));
MAKE_BENCHMARK(AbslHash, String_5000, std::string(5000, 'a'));
MAKE_BENCHMARK(AbslHash, Cord_Flat_0, absl::Cord());
MAKE_BENCHMARK(AbslHash, Cord_Flat_10, FlatCord(10));
MAKE_BENCHMARK(AbslHash, Cord_Flat_30, FlatCord(30));
MAKE_BENCHMARK(AbslHash, Cord_Flat_90, FlatCord(90));
MAKE_BENCHMARK(AbslHash, Cord_Flat_200, FlatCord(200));
MAKE_BENCHMARK(AbslHash, Cord_Flat_5000, FlatCord(5000));
MAKE_BENCHMARK(AbslHash, Cord_Fragmented_200, FragmentedCord(200));
MAKE_BENCHMARK(AbslHash, Cord_Fragmented_5000, FragmentedCord(5000));
MAKE_BENCHMARK(AbslHash, VectorInt64_10, Vector<int64_t>(10));
MAKE_BENCHMARK(AbslHash, VectorInt64_100, Vector<int64_t>(100));
MAKE_BENCHMARK(AbslHash, VectorInt64_1000, Vector<int64_t>(1000));
MAKE_BENCHMARK(AbslHash, VectorDouble_10, Vector<double>(10));
MAKE_BENCHMARK(AbslHash, VectorDouble_100, Vector<double>(100));
MAKE_BENCHMARK(AbslHash, VectorDouble_1000, Vector<double>(1000));
MAKE_BENCHMARK(AbslHash, FlatHashSetInt64_10, FlatHashSet<int64_t>(10));
MAKE_BENCHMARK(AbslHash, FlatHashSetInt64_100, FlatHashSet<int64_t>(100));
MAKE_BENCHMARK(AbslHash, FlatHashSetInt64_1000, FlatHashSet<int64_t>(1000));
MAKE_BENCHMARK(AbslHash, FlatHashSetDouble_10, FlatHashSet<double>(10));
MAKE_BENCHMARK(AbslHash, FlatHashSetDouble_100, FlatHashSet<double>(100));
MAKE_BENCHMARK(AbslHash, FlatHashSetDouble_1000, FlatHashSet<double>(1000));
MAKE_BENCHMARK(AbslHash, FastUnorderedSetInt64_1000,
FastUnorderedSet<int64_t>(1000));
MAKE_BENCHMARK(AbslHash, FastUnorderedSetDouble_1000,
FastUnorderedSet<double>(1000));
MAKE_BENCHMARK(AbslHash, PairStringString_0,
std::make_pair(std::string(), std::string()));
MAKE_BENCHMARK(AbslHash, PairStringString_10,
std::make_pair(std::string(10, 'a'), std::string(10, 'b')));
MAKE_BENCHMARK(AbslHash, PairStringString_30,
std::make_pair(std::string(30, 'a'), std::string(30, 'b')));
MAKE_BENCHMARK(AbslHash, PairStringString_90,
std::make_pair(std::string(90, 'a'), std::string(90, 'b')));
MAKE_BENCHMARK(AbslHash, PairStringString_200,
std::make_pair(std::string(200, 'a'), std::string(200, 'b')));
MAKE_BENCHMARK(AbslHash, PairStringString_5000,
std::make_pair(std::string(5000, 'a'), std::string(5000, 'b')));
MAKE_BENCHMARK(TypeErasedAbslHash, Int32, int32_t{});
MAKE_BENCHMARK(TypeErasedAbslHash, Int64, int64_t{});
MAKE_BENCHMARK(TypeErasedAbslHash, PairInt32Int32,
std::pair<int32_t, int32_t>{});
MAKE_BENCHMARK(TypeErasedAbslHash, PairInt64Int64,
std::pair<int64_t, int64_t>{});
MAKE_BENCHMARK(TypeErasedAbslHash, TupleInt32BoolInt64,
std::tuple<int32_t, bool, int64_t>{});
MAKE_BENCHMARK(TypeErasedAbslHash, String_0, std::string());
MAKE_BENCHMARK(TypeErasedAbslHash, String_10, std::string(10, 'a'));
MAKE_BENCHMARK(TypeErasedAbslHash, String_30, std::string(30, 'a'));
MAKE_BENCHMARK(TypeErasedAbslHash, String_90, std::string(90, 'a'));
MAKE_BENCHMARK(TypeErasedAbslHash, String_200, std::string(200, 'a'));
MAKE_BENCHMARK(TypeErasedAbslHash, String_5000, std::string(5000, 'a'));
MAKE_BENCHMARK(TypeErasedAbslHash, VectorDouble_10,
std::vector<double>(10, 1.1));
MAKE_BENCHMARK(TypeErasedAbslHash, VectorDouble_100,
std::vector<double>(100, 1.1));
MAKE_BENCHMARK(TypeErasedAbslHash, VectorDouble_1000,
std::vector<double>(1000, 1.1));
MAKE_BENCHMARK(TypeErasedAbslHash, FlatHashSetInt64_10,
FlatHashSet<int64_t>(10));
MAKE_BENCHMARK(TypeErasedAbslHash, FlatHashSetInt64_100,
FlatHashSet<int64_t>(100));
MAKE_BENCHMARK(TypeErasedAbslHash, FlatHashSetInt64_1000,
FlatHashSet<int64_t>(1000));
MAKE_BENCHMARK(TypeErasedAbslHash, FlatHashSetDouble_10,
FlatHashSet<double>(10));
MAKE_BENCHMARK(TypeErasedAbslHash, FlatHashSetDouble_100,
FlatHashSet<double>(100));
MAKE_BENCHMARK(TypeErasedAbslHash, FlatHashSetDouble_1000,
FlatHashSet<double>(1000));
MAKE_BENCHMARK(TypeErasedAbslHash, FastUnorderedSetInt64_1000,
FastUnorderedSet<int64_t>(1000));
MAKE_BENCHMARK(TypeErasedAbslHash, FastUnorderedSetDouble_1000,
FastUnorderedSet<double>(1000));
// The latency benchmark attempts to model the speed of the hash function in
// production. When a hash function is used for hashtable lookups it is rarely
// used to hash N items in a tight loop nor on constant sized strings. Instead,
// after hashing there is a potential equality test plus a (usually) large
// amount of user code. To simulate this effectively we introduce a data
// dependency between elements we hash by using the hash of the Nth element as
// the selector of the N+1th element to hash. This isolates the hash function
// code much like in production. As a bonus we use the hash to generate strings
// of size [1,N] (instead of fixed N) to disable perfect branch predictions in
// hash function implementations.
namespace {
// 16kb fits in L1 cache of most CPUs we care about. Keeping memory latency low
// will allow us to attribute most time to CPU which means more accurate
// measurements.
static constexpr size_t kEntropySize = 16 << 10;
static char entropy[kEntropySize + 1024];
ABSL_ATTRIBUTE_UNUSED static const bool kInitialized = [] {
absl::BitGen gen;
static_assert(sizeof(entropy) % sizeof(uint64_t) == 0, "");
for (int i = 0; i != sizeof(entropy); i += sizeof(uint64_t)) {
auto rand = absl::Uniform<uint64_t>(gen);
memcpy(&entropy[i], &rand, sizeof(uint64_t));
}
return true;
}();
} // namespace
template <class T>
struct PodRand {
static_assert(std::is_pod<T>::value, "");
static_assert(kEntropySize + sizeof(T) < sizeof(entropy), "");
T Get(size_t i) const {
T v;
memcpy(&v, &entropy[i % kEntropySize], sizeof(T));
return v;
}
};
template <size_t N>
struct StringRand {
static_assert(kEntropySize + N < sizeof(entropy), "");
absl::string_view Get(size_t i) const {
// This has a small bias towards small numbers. Because max N is ~200 this
// is very small and prefer to be very fast instead of absolutely accurate.
// Also we pass N = 2^K+1 so that mod reduces to a bitand.
size_t s = (i % (N - 1)) + 1;
return {&entropy[i % kEntropySize], s};
}
};
#define MAKE_LATENCY_BENCHMARK(hash, name, ...) \
namespace { \
void BM_latency_##hash##_##name(benchmark::State& state) { \
__VA_ARGS__ r; \
hash<decltype(r.Get(0))> h; \
size_t i = 871401241; \
for (auto _ : state) { \
benchmark::DoNotOptimize(i = h(r.Get(i))); \
} \
} \
BENCHMARK(BM_latency_##hash##_##name); \
} // namespace
MAKE_LATENCY_BENCHMARK(AbslHash, Int32, PodRand<int32_t>);
MAKE_LATENCY_BENCHMARK(AbslHash, Int64, PodRand<int64_t>);
MAKE_LATENCY_BENCHMARK(AbslHash, String9, StringRand<9>);
MAKE_LATENCY_BENCHMARK(AbslHash, String33, StringRand<33>);
MAKE_LATENCY_BENCHMARK(AbslHash, String65, StringRand<65>);
MAKE_LATENCY_BENCHMARK(AbslHash, String257, StringRand<257>);
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