THUAI6/CAPI/cpp/grpc/include/absl/random/internal/nanobenchmark.h

// 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>

#include "absl/base/config.h"

namespace absl
{
    ABSL_NAMESPACE_BEGIN
    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
    ABSL_NAMESPACE_END
}  // namespace absl

#endif  // ABSL_RANDOM_INTERNAL_NANOBENCHMARK_H_