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// Copyright 2019 Google LLC // SPDX-License-Identifier: Apache-2.0 // // 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 // // http://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 HIGHWAY_HWY_NANOBENCHMARK_H_ #define HIGHWAY_HWY_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. #include #include #include "hwy/highway_export.h" #include "hwy/timer.h" // IWYU pragma: export namespace hwy { // Returns 1, but without the compiler knowing what the value is. This prevents // optimizing out code. HWY_DLLEXPORT int Unpredictable1(); // 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 { // 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; // 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; }; // 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). HWY_DLLEXPORT size_t Measure(Func func, const uint8_t* arg, const FuncInput* inputs, size_t num_inputs, Result* results, const Params& p = Params()); // Calls operator() of the given closure (lambda function). template static FuncOutput CallClosure(const Closure* f, const FuncInput input) { return (*f)(input); } // Same as Measure, except "closure" is typically a lambda function of // FuncInput -> FuncOutput with a capture list. template 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(&CallClosure), reinterpret_cast(&closure), inputs, num_inputs, results, p); } } // namespace hwy #endif // HIGHWAY_HWY_NANOBENCHMARK_H_