90e285efde
Summary: Fixed some spots where converting size_t or uint_fast32_t to uint32_t. Wrapped mt19937 in a new Random32 class to avoid future such traps. NB: I tried using Random32::Uniform (std::uniform_int_distribution) in filter_bench instead of fastrange, but that more than doubled the dry run time! So I added fastrange as Random32::Uniformish. ;) Pull Request resolved: https://github.com/facebook/rocksdb/pull/5894 Test Plan: USE_CLANG=1 build, and manual re-run filter_bench Differential Revision: D17825131 Pulled By: pdillinger fbshipit-source-id: 68feee333b5f8193c084ded760e3d6679b405ecd
154 lines
4.9 KiB
C++
154 lines
4.9 KiB
C++
// Copyright (c) 2011-present, Facebook, Inc. All rights reserved.
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// This source code is licensed under both the GPLv2 (found in the
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// COPYING file in the root directory) and Apache 2.0 License
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// (found in the LICENSE.Apache file in the root directory).
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//
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// Copyright (c) 2011 The LevelDB Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style license that can be
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// found in the LICENSE file. See the AUTHORS file for names of contributors.
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#pragma once
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#include <random>
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#include <stdint.h>
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namespace rocksdb {
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// A very simple random number generator. Not especially good at
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// generating truly random bits, but good enough for our needs in this
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// package.
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class Random {
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private:
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enum : uint32_t {
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M = 2147483647L // 2^31-1
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};
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enum : uint64_t {
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A = 16807 // bits 14, 8, 7, 5, 2, 1, 0
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};
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uint32_t seed_;
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static uint32_t GoodSeed(uint32_t s) { return (s & M) != 0 ? (s & M) : 1; }
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public:
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// This is the largest value that can be returned from Next()
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enum : uint32_t { kMaxNext = M };
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explicit Random(uint32_t s) : seed_(GoodSeed(s)) {}
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void Reset(uint32_t s) { seed_ = GoodSeed(s); }
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uint32_t Next() {
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// We are computing
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// seed_ = (seed_ * A) % M, where M = 2^31-1
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//
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// seed_ must not be zero or M, or else all subsequent computed values
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// will be zero or M respectively. For all other values, seed_ will end
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// up cycling through every number in [1,M-1]
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uint64_t product = seed_ * A;
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// Compute (product % M) using the fact that ((x << 31) % M) == x.
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seed_ = static_cast<uint32_t>((product >> 31) + (product & M));
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// The first reduction may overflow by 1 bit, so we may need to
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// repeat. mod == M is not possible; using > allows the faster
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// sign-bit-based test.
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if (seed_ > M) {
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seed_ -= M;
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}
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return seed_;
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}
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// Returns a uniformly distributed value in the range [0..n-1]
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// REQUIRES: n > 0
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uint32_t Uniform(int n) { return Next() % n; }
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// Randomly returns true ~"1/n" of the time, and false otherwise.
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// REQUIRES: n > 0
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bool OneIn(int n) { return (Next() % n) == 0; }
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// Skewed: pick "base" uniformly from range [0,max_log] and then
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// return "base" random bits. The effect is to pick a number in the
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// range [0,2^max_log-1] with exponential bias towards smaller numbers.
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uint32_t Skewed(int max_log) {
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return Uniform(1 << Uniform(max_log + 1));
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}
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// Returns a Random instance for use by the current thread without
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// additional locking
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static Random* GetTLSInstance();
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};
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// A good 32-bit random number generator based on std::mt19937.
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// This exists in part to avoid compiler variance in warning about coercing
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// uint_fast32_t from mt19937 to uint32_t.
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class Random32 {
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private:
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std::mt19937 generator_;
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public:
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explicit Random32(uint32_t s) : generator_(s) {}
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// Generates the next random number
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uint32_t Next() { return static_cast<uint32_t>(generator_()); }
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// Returns a uniformly distributed value in the range [0..n-1]
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// REQUIRES: n > 0
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uint32_t Uniform(uint32_t n) {
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return static_cast<uint32_t>(
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std::uniform_int_distribution<std::mt19937::result_type>(
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0, n - 1)(generator_));
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}
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// Returns an *almost* uniformly distributed value in the range [0..n-1].
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// Much faster than Uniform().
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// REQUIRES: n > 0
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uint32_t Uniformish(uint32_t n) {
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// fastrange (without the header)
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return static_cast<uint32_t>((uint64_t(generator_()) * uint64_t(n)) >> 32);
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}
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// Randomly returns true ~"1/n" of the time, and false otherwise.
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// REQUIRES: n > 0
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bool OneIn(uint32_t n) { return Uniform(n) == 0; }
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// Skewed: pick "base" uniformly from range [0,max_log] and then
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// return "base" random bits. The effect is to pick a number in the
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// range [0,2^max_log-1] with exponential bias towards smaller numbers.
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uint32_t Skewed(int max_log) {
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return Uniform(uint32_t{1} << Uniform(max_log + 1));
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}
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// Reset the seed of the generator to the given value
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void Seed(uint32_t new_seed) { generator_.seed(new_seed); }
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};
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// A good 64-bit random number generator based on std::mt19937_64
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class Random64 {
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private:
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std::mt19937_64 generator_;
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public:
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explicit Random64(uint64_t s) : generator_(s) { }
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// Generates the next random number
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uint64_t Next() { return generator_(); }
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// Returns a uniformly distributed value in the range [0..n-1]
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// REQUIRES: n > 0
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uint64_t Uniform(uint64_t n) {
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return std::uniform_int_distribution<uint64_t>(0, n - 1)(generator_);
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}
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// Randomly returns true ~"1/n" of the time, and false otherwise.
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// REQUIRES: n > 0
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bool OneIn(uint64_t n) { return Uniform(n) == 0; }
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// Skewed: pick "base" uniformly from range [0,max_log] and then
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// return "base" random bits. The effect is to pick a number in the
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// range [0,2^max_log-1] with exponential bias towards smaller numbers.
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uint64_t Skewed(int max_log) {
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return Uniform(uint64_t(1) << Uniform(max_log + 1));
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}
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};
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} // namespace rocksdb
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