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// Copyright 2019 Google LLC
//
// 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.
// WARNING if included from multiple translation units compiled with distinct
// flags: this header requires textual inclusion and a predefined NB_NAMESPACE
// macro that is unique to the current compile flags. We must also avoid
// standard library headers such as vector and functional that define functions.
#include <stddef.h>
#include <stdint.h>
// Enables sanity checks that verify correct operation at the cost of
// longer benchmark runs.
#ifndef NANOBENCHMARK_ENABLE_CHECKS
#define NANOBENCHMARK_ENABLE_CHECKS 0
#endif
#define NANOBENCHMARK_CHECK_ALWAYS(condition) \
while (!(condition)) { \
fprintf(stderr, "Nanobenchmark check failed at line %d\n", __LINE__); \
abort(); \
}
#if NANOBENCHMARK_ENABLE_CHECKS
#define NANOBENCHMARK_CHECK(condition) NANOBENCHMARK_CHECK_ALWAYS(condition)
#else
#define NANOBENCHMARK_CHECK(condition)
#endif
namespace hwy {
namespace platform {
// 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();
} // namespace platform
// 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;
// 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).
size_t Measure(const Func func, const uint8_t* 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 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 <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 uint8_t*>(&closure), inputs, num_inputs,
results, p);
}
} // namespace hwy
#endif // HIGHWAY_HWY_NANOBENCHMARK_H_