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+// Copyright 2017 Google Inc. All Rights Reserved.
+//
+// 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_RANDOM_INTERNAL_NANOBENCHMARK_H_
+#define ABSL_RANDOM_INTERNAL_NANOBENCHMARK_H_
+
+// Benchmarks functions of a single integer argument with realistic branch
+// prediction hit rates. Uses a robust estimator to summarize the measurements.
+// The precision is about 0.2%.
+//
+// Examples: see nanobenchmark_test.cc.
+//
+// Background: Microbenchmarks such as http://github.com/google/benchmark
+// can measure elapsed times on the order of a microsecond. Shorter functions
+// are typically measured by repeating them thousands of times and dividing
+// the total elapsed time by this count. Unfortunately, repetition (especially
+// with the same input parameter!) influences the runtime. In time-critical
+// code, it is reasonable to expect warm instruction/data caches and TLBs,
+// but a perfect record of which branches will be taken is unrealistic.
+// Unless the application also repeatedly invokes the measured function with
+// the same parameter, the benchmark is measuring something very different -
+// a best-case result, almost as if the parameter were made a compile-time
+// constant. This may lead to erroneous conclusions about branch-heavy
+// algorithms outperforming branch-free alternatives.
+//
+// Our approach differs in three ways. Adding fences to the timer functions
+// reduces variability due to instruction reordering, improving the timer
+// resolution to about 40 CPU cycles. However, shorter functions must still
+// be invoked repeatedly. For more realistic branch prediction performance,
+// we vary the input parameter according to a user-specified distribution.
+// Thus, instead of VaryInputs(Measure(Repeat(func))), we change the
+// loop nesting to Measure(Repeat(VaryInputs(func))). We also estimate the
+// central tendency of the measurement samples with the "half sample mode",
+// which is more robust to outliers and skewed data than the mean or median.
+
+// NOTE: for compatibility with multiple translation units compiled with
+// distinct flags, avoid #including headers that define functions.
+
+#include <stddef.h>
+#include <stdint.h>
+
+namespace absl {
+inline namespace lts_2019_08_08 {
+namespace random_internal_nanobenchmark {
+
+// Input influencing the function being measured (e.g. number of bytes to copy).
+using FuncInput = size_t;
+
+// "Proof of work" returned by Func to ensure the compiler does not elide it.
+using FuncOutput = uint64_t;
+
+// Function to measure: either 1) a captureless lambda or function with two
+// arguments or 2) a lambda with capture, in which case the first argument
+// is reserved for use by MeasureClosure.
+using Func = FuncOutput (*)(const void*, FuncInput);
+
+// Internal parameters that determine precision/resolution/measuring time.
+struct Params {
+  // For measuring timer overhead/resolution. Used in a nested loop =>
+  // quadratic time, acceptable because we know timer overhead is "low".
+  // constexpr because this is used to define array bounds.
+  static constexpr size_t kTimerSamples = 256;
+
+  // Best-case precision, expressed as a divisor of the timer resolution.
+  // Larger => more calls to Func and higher precision.
+  size_t precision_divisor = 1024;
+
+  // Ratio between full and subset input distribution sizes. Cannot be less
+  // than 2; larger values increase measurement time but more faithfully
+  // model the given input distribution.
+  size_t subset_ratio = 2;
+
+  // Together with the estimated Func duration, determines how many times to
+  // call Func before checking the sample variability. Larger values increase
+  // measurement time, memory/cache use and precision.
+  double seconds_per_eval = 4E-3;
+
+  // The minimum number of samples before estimating the central tendency.
+  size_t min_samples_per_eval = 7;
+
+  // The mode is better than median for estimating the central tendency of
+  // skewed/fat-tailed distributions, but it requires sufficient samples
+  // relative to the width of half-ranges.
+  size_t min_mode_samples = 64;
+
+  // Maximum permissible variability (= median absolute deviation / center).
+  double target_rel_mad = 0.002;
+
+  // Abort after this many evals without reaching target_rel_mad. This
+  // prevents infinite loops.
+  size_t max_evals = 9;
+
+  // Retry the measure loop up to this many times.
+  size_t max_measure_retries = 2;
+
+  // Whether to print additional statistics to stdout.
+  bool verbose = true;
+};
+
+// Measurement result for each unique input.
+struct Result {
+  FuncInput input;
+
+  // Robust estimate (mode or median) of duration.
+  float ticks;
+
+  // Measure of variability (median absolute deviation relative to "ticks").
+  float variability;
+};
+
+// Ensures the thread is running on the specified cpu, and no others.
+// Reduces noise due to desynchronized socket RDTSC and context switches.
+// If "cpu" is negative, pin to the currently running core.
+void PinThreadToCPU(const int cpu = -1);
+
+// Returns tick rate, useful for converting measurements to seconds. Invariant
+// means the tick counter frequency is independent of CPU throttling or sleep.
+// This call may be expensive, callers should cache the result.
+double InvariantTicksPerSecond();
+
+// Precisely measures the number of ticks elapsed when calling "func" with the
+// given inputs, shuffled to ensure realistic branch prediction hit rates.
+//
+// "func" returns a 'proof of work' to ensure its computations are not elided.
+// "arg" is passed to Func, or reserved for internal use by MeasureClosure.
+// "inputs" is an array of "num_inputs" (not necessarily unique) arguments to
+//   "func". The values should be chosen to maximize coverage of "func". This
+//   represents a distribution, so a value's frequency should reflect its
+//   probability in the real application. Order does not matter; for example, a
+//   uniform distribution over [0, 4) could be represented as {3,0,2,1}.
+// Returns how many Result were written to "results": one per unique input, or
+//   zero if the measurement failed (an error message goes to stderr).
+size_t Measure(const Func func, const void* arg, const FuncInput* inputs,
+               const size_t num_inputs, Result* results,
+               const Params& p = Params());
+
+// Calls operator() of the given closure (lambda function).
+template <class Closure>
+static FuncOutput CallClosure(const void* f, const FuncInput input) {
+  return (*reinterpret_cast<const Closure*>(f))(input);
+}
+
+// Same as Measure, except "closure" is typically a lambda function of
+// FuncInput -> FuncOutput with a capture list.
+template <class Closure>
+static inline size_t MeasureClosure(const Closure& closure,
+                                    const FuncInput* inputs,
+                                    const size_t num_inputs, Result* results,
+                                    const Params& p = Params()) {
+  return Measure(reinterpret_cast<Func>(&CallClosure<Closure>),
+                 reinterpret_cast<const void*>(&closure), inputs, num_inputs,
+                 results, p);
+}
+
+}  // namespace random_internal_nanobenchmark
+}  // inline namespace lts_2019_08_08
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_NANOBENCHMARK_H_