173 lines
7.5 KiB
C
173 lines
7.5 KiB
C
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// Copyright 2017 Google Inc. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// https://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#ifndef ABSL_RANDOM_INTERNAL_NANOBENCHMARK_H_
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#define ABSL_RANDOM_INTERNAL_NANOBENCHMARK_H_
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// Benchmarks functions of a single integer argument with realistic branch
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// prediction hit rates. Uses a robust estimator to summarize the measurements.
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// The precision is about 0.2%.
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//
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// Examples: see nanobenchmark_test.cc.
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//
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// Background: Microbenchmarks such as http://github.com/google/benchmark
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// can measure elapsed times on the order of a microsecond. Shorter functions
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// are typically measured by repeating them thousands of times and dividing
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// the total elapsed time by this count. Unfortunately, repetition (especially
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// with the same input parameter!) influences the runtime. In time-critical
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// code, it is reasonable to expect warm instruction/data caches and TLBs,
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// but a perfect record of which branches will be taken is unrealistic.
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// Unless the application also repeatedly invokes the measured function with
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// the same parameter, the benchmark is measuring something very different -
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// a best-case result, almost as if the parameter were made a compile-time
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// constant. This may lead to erroneous conclusions about branch-heavy
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// algorithms outperforming branch-free alternatives.
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//
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// Our approach differs in three ways. Adding fences to the timer functions
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// reduces variability due to instruction reordering, improving the timer
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// resolution to about 40 CPU cycles. However, shorter functions must still
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// be invoked repeatedly. For more realistic branch prediction performance,
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// we vary the input parameter according to a user-specified distribution.
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// Thus, instead of VaryInputs(Measure(Repeat(func))), we change the
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// loop nesting to Measure(Repeat(VaryInputs(func))). We also estimate the
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// central tendency of the measurement samples with the "half sample mode",
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// which is more robust to outliers and skewed data than the mean or median.
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// NOTE: for compatibility with multiple translation units compiled with
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// distinct flags, avoid #including headers that define functions.
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#include <stddef.h>
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#include <stdint.h>
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#include "absl/base/config.h"
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namespace absl {
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ABSL_NAMESPACE_BEGIN
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namespace random_internal_nanobenchmark {
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// Input influencing the function being measured (e.g. number of bytes to copy).
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using FuncInput = size_t;
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// "Proof of work" returned by Func to ensure the compiler does not elide it.
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using FuncOutput = uint64_t;
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// Function to measure: either 1) a captureless lambda or function with two
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// arguments or 2) a lambda with capture, in which case the first argument
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// is reserved for use by MeasureClosure.
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using Func = FuncOutput (*)(const void*, FuncInput);
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// Internal parameters that determine precision/resolution/measuring time.
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struct Params {
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// For measuring timer overhead/resolution. Used in a nested loop =>
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// quadratic time, acceptable because we know timer overhead is "low".
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// constexpr because this is used to define array bounds.
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static constexpr size_t kTimerSamples = 256;
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// Best-case precision, expressed as a divisor of the timer resolution.
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// Larger => more calls to Func and higher precision.
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size_t precision_divisor = 1024;
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// Ratio between full and subset input distribution sizes. Cannot be less
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// than 2; larger values increase measurement time but more faithfully
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// model the given input distribution.
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size_t subset_ratio = 2;
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// Together with the estimated Func duration, determines how many times to
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// call Func before checking the sample variability. Larger values increase
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// measurement time, memory/cache use and precision.
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double seconds_per_eval = 4E-3;
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// The minimum number of samples before estimating the central tendency.
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size_t min_samples_per_eval = 7;
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// The mode is better than median for estimating the central tendency of
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// skewed/fat-tailed distributions, but it requires sufficient samples
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// relative to the width of half-ranges.
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size_t min_mode_samples = 64;
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// Maximum permissible variability (= median absolute deviation / center).
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double target_rel_mad = 0.002;
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// Abort after this many evals without reaching target_rel_mad. This
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// prevents infinite loops.
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size_t max_evals = 9;
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// Retry the measure loop up to this many times.
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size_t max_measure_retries = 2;
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// Whether to print additional statistics to stdout.
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bool verbose = true;
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};
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// Measurement result for each unique input.
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struct Result {
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FuncInput input;
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// Robust estimate (mode or median) of duration.
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float ticks;
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// Measure of variability (median absolute deviation relative to "ticks").
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float variability;
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};
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// Ensures the thread is running on the specified cpu, and no others.
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// Reduces noise due to desynchronized socket RDTSC and context switches.
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// If "cpu" is negative, pin to the currently running core.
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void PinThreadToCPU(const int cpu = -1);
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// Returns tick rate, useful for converting measurements to seconds. Invariant
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// means the tick counter frequency is independent of CPU throttling or sleep.
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// This call may be expensive, callers should cache the result.
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double InvariantTicksPerSecond();
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// Precisely measures the number of ticks elapsed when calling "func" with the
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// given inputs, shuffled to ensure realistic branch prediction hit rates.
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//
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// "func" returns a 'proof of work' to ensure its computations are not elided.
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// "arg" is passed to Func, or reserved for internal use by MeasureClosure.
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// "inputs" is an array of "num_inputs" (not necessarily unique) arguments to
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// "func". The values should be chosen to maximize coverage of "func". This
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// represents a distribution, so a value's frequency should reflect its
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// probability in the real application. Order does not matter; for example, a
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// uniform distribution over [0, 4) could be represented as {3,0,2,1}.
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// Returns how many Result were written to "results": one per unique input, or
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// zero if the measurement failed (an error message goes to stderr).
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size_t Measure(const Func func, const void* arg, const FuncInput* inputs,
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const size_t num_inputs, Result* results,
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const Params& p = Params());
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// Calls operator() of the given closure (lambda function).
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template <class Closure>
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static FuncOutput CallClosure(const void* f, const FuncInput input) {
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return (*reinterpret_cast<const Closure*>(f))(input);
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}
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// Same as Measure, except "closure" is typically a lambda function of
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// FuncInput -> FuncOutput with a capture list.
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template <class Closure>
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static inline size_t MeasureClosure(const Closure& closure,
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const FuncInput* inputs,
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const size_t num_inputs, Result* results,
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const Params& p = Params()) {
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return Measure(reinterpret_cast<Func>(&CallClosure<Closure>),
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reinterpret_cast<const void*>(&closure), inputs, num_inputs,
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results, p);
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}
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} // namespace random_internal_nanobenchmark
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ABSL_NAMESPACE_END
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} // namespace absl
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#endif // ABSL_RANDOM_INTERNAL_NANOBENCHMARK_H_
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