tvl-depot/absl/base/internal/exponential_biased.cc
Abseil Team 1e39f8626a Export of internal Abseil changes
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b842b7fd9b1289be31f0b7ee8e62e48e550747cf by Greg Falcon <gfalcon@google.com>:

Change the Cord str_format formatter to use iteration instead of CordReader.

When Cord is publicly released, CordReader is not going with it.

PiperOrigin-RevId: 284780736

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28e76c08ea7185a7ff9f4e0e02ae565fbbf7980f by Greg Falcon <gfalcon@google.com>:

Implementation detail change.

Introduce ABSL_NAMESPACE_BEGIN and _END annotation macros which indicate the beginning and end of a `namespace absl` scope.  Currently these do nothing, but they will be used to inject an inline namespace for LTS builds (to avoid symbol collisions against other Abseil versions).

These macros should not be used by end users, because end users should never write `namespace absl {` in their own code.

This CL applies these annotations to all code under //absl/base/.  The rest of Abseil will be annotated in this way in follow-up CLs.

PiperOrigin-RevId: 284776410

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e1711dc6d696dcca50d4e7d4b4d8f3076575b7ec by Abseil Team <absl-team@google.com>:

--help changed to report long flags.

PiperOrigin-RevId: 284757720

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78f66a68f428bbbd19d8d60e1125f43ba765fd35 by Tom Manshreck <shreck@google.com>:

Update comment on + or - in SimpleAToi()

PiperOrigin-RevId: 284231843
GitOrigin-RevId: b842b7fd9b1289be31f0b7ee8e62e48e550747cf
Change-Id: I3046b31391bd11c8bc4abab7785a863c377cd757
2019-12-10 15:07:05 -05:00

93 lines
3.4 KiB
C++

// 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/base/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 base_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::round(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 base_internal
ABSL_NAMESPACE_END
} // namespace absl