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nanobenchmark.h
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1 // Copyright 2019 Google LLC
2 // SPDX-License-Identifier: Apache-2.0
3 //
4 // Licensed under the Apache License, Version 2.0 (the "License");
5 // you may not use this file except in compliance with the License.
6 // You may obtain a copy of the License at
7 //
8 // http://www.apache.org/licenses/LICENSE-2.0
9 //
10 // Unless required by applicable law or agreed to in writing, software
11 // distributed under the License is distributed on an "AS IS" BASIS,
12 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 // See the License for the specific language governing permissions and
14 // limitations under the License.
15 
16 #ifndef HIGHWAY_HWY_NANOBENCHMARK_H_
17 #define HIGHWAY_HWY_NANOBENCHMARK_H_
18 
19 // Benchmarks functions of a single integer argument with realistic branch
20 // prediction hit rates. Uses a robust estimator to summarize the measurements.
21 // The precision is about 0.2%.
22 //
23 // Examples: see nanobenchmark_test.cc.
24 //
25 // Background: Microbenchmarks such as http://github.com/google/benchmark
26 // can measure elapsed times on the order of a microsecond. Shorter functions
27 // are typically measured by repeating them thousands of times and dividing
28 // the total elapsed time by this count. Unfortunately, repetition (especially
29 // with the same input parameter!) influences the runtime. In time-critical
30 // code, it is reasonable to expect warm instruction/data caches and TLBs,
31 // but a perfect record of which branches will be taken is unrealistic.
32 // Unless the application also repeatedly invokes the measured function with
33 // the same parameter, the benchmark is measuring something very different -
34 // a best-case result, almost as if the parameter were made a compile-time
35 // constant. This may lead to erroneous conclusions about branch-heavy
36 // algorithms outperforming branch-free alternatives.
37 //
38 // Our approach differs in three ways. Adding fences to the timer functions
39 // reduces variability due to instruction reordering, improving the timer
40 // resolution to about 40 CPU cycles. However, shorter functions must still
41 // be invoked repeatedly. For more realistic branch prediction performance,
42 // we vary the input parameter according to a user-specified distribution.
43 // Thus, instead of VaryInputs(Measure(Repeat(func))), we change the
44 // loop nesting to Measure(Repeat(VaryInputs(func))). We also estimate the
45 // central tendency of the measurement samples with the "half sample mode",
46 // which is more robust to outliers and skewed data than the mean or median.
47 
48 #include <stddef.h>
49 #include <stdint.h>
50 
51 #include "hwy/highway_export.h"
52 
53 // Enables sanity checks that verify correct operation at the cost of
54 // longer benchmark runs.
55 #ifndef NANOBENCHMARK_ENABLE_CHECKS
56 #define NANOBENCHMARK_ENABLE_CHECKS 0
57 #endif
58 
59 #define NANOBENCHMARK_CHECK_ALWAYS(condition) \
60  while (!(condition)) { \
61  fprintf(stderr, "Nanobenchmark check failed at line %d\n", __LINE__); \
62  abort(); \
63  }
64 
65 #if NANOBENCHMARK_ENABLE_CHECKS
66 #define NANOBENCHMARK_CHECK(condition) NANOBENCHMARK_CHECK_ALWAYS(condition)
67 #else
68 #define NANOBENCHMARK_CHECK(condition)
69 #endif
70 
71 namespace hwy {
72 
73 namespace platform {
74 
75 // Returns tick rate, useful for converting measurements to seconds. Invariant
76 // means the tick counter frequency is independent of CPU throttling or sleep.
77 // This call may be expensive, callers should cache the result.
79 
80 // Returns current timestamp [in seconds] relative to an unspecified origin.
81 // Features: monotonic (no negative elapsed time), steady (unaffected by system
82 // time changes), high-resolution (on the order of microseconds).
83 HWY_DLLEXPORT double Now();
84 
85 // Returns ticks elapsed in back to back timer calls, i.e. a function of the
86 // timer resolution (minimum measurable difference) and overhead.
87 // This call is expensive, callers should cache the result.
89 
90 } // namespace platform
91 
92 // Returns 1, but without the compiler knowing what the value is. This prevents
93 // optimizing out code.
95 
96 // Input influencing the function being measured (e.g. number of bytes to copy).
97 using FuncInput = size_t;
98 
99 // "Proof of work" returned by Func to ensure the compiler does not elide it.
100 using FuncOutput = uint64_t;
101 
102 // Function to measure: either 1) a captureless lambda or function with two
103 // arguments or 2) a lambda with capture, in which case the first argument
104 // is reserved for use by MeasureClosure.
105 using Func = FuncOutput (*)(const void*, FuncInput);
106 
107 // Internal parameters that determine precision/resolution/measuring time.
108 struct Params {
109  // For measuring timer overhead/resolution. Used in a nested loop =>
110  // quadratic time, acceptable because we know timer overhead is "low".
111  // constexpr because this is used to define array bounds.
112  static constexpr size_t kTimerSamples = 256;
113 
114  // Best-case precision, expressed as a divisor of the timer resolution.
115  // Larger => more calls to Func and higher precision.
116  size_t precision_divisor = 1024;
117 
118  // Ratio between full and subset input distribution sizes. Cannot be less
119  // than 2; larger values increase measurement time but more faithfully
120  // model the given input distribution.
121  size_t subset_ratio = 2;
122 
123  // Together with the estimated Func duration, determines how many times to
124  // call Func before checking the sample variability. Larger values increase
125  // measurement time, memory/cache use and precision.
126  double seconds_per_eval = 4E-3;
127 
128  // The minimum number of samples before estimating the central tendency.
130 
131  // The mode is better than median for estimating the central tendency of
132  // skewed/fat-tailed distributions, but it requires sufficient samples
133  // relative to the width of half-ranges.
134  size_t min_mode_samples = 64;
135 
136  // Maximum permissible variability (= median absolute deviation / center).
137  double target_rel_mad = 0.002;
138 
139  // Abort after this many evals without reaching target_rel_mad. This
140  // prevents infinite loops.
141  size_t max_evals = 9;
142 
143  // Whether to print additional statistics to stdout.
144  bool verbose = true;
145 };
146 
147 // Measurement result for each unique input.
148 struct Result {
150 
151  // Robust estimate (mode or median) of duration.
152  float ticks;
153 
154  // Measure of variability (median absolute deviation relative to "ticks").
155  float variability;
156 };
157 
158 // Precisely measures the number of ticks elapsed when calling "func" with the
159 // given inputs, shuffled to ensure realistic branch prediction hit rates.
160 //
161 // "func" returns a 'proof of work' to ensure its computations are not elided.
162 // "arg" is passed to Func, or reserved for internal use by MeasureClosure.
163 // "inputs" is an array of "num_inputs" (not necessarily unique) arguments to
164 // "func". The values should be chosen to maximize coverage of "func". This
165 // represents a distribution, so a value's frequency should reflect its
166 // probability in the real application. Order does not matter; for example, a
167 // uniform distribution over [0, 4) could be represented as {3,0,2,1}.
168 // Returns how many Result were written to "results": one per unique input, or
169 // zero if the measurement failed (an error message goes to stderr).
170 HWY_DLLEXPORT size_t Measure(const Func func, const uint8_t* arg,
171  const FuncInput* inputs, const size_t num_inputs,
172  Result* results, const Params& p = Params());
173 
174 // Calls operator() of the given closure (lambda function).
175 template <class Closure>
176 static FuncOutput CallClosure(const Closure* f, const FuncInput input) {
177  return (*f)(input);
178 }
179 
180 // Same as Measure, except "closure" is typically a lambda function of
181 // FuncInput -> FuncOutput with a capture list.
182 template <class Closure>
183 static inline size_t MeasureClosure(const Closure& closure,
184  const FuncInput* inputs,
185  const size_t num_inputs, Result* results,
186  const Params& p = Params()) {
187  return Measure(reinterpret_cast<Func>(&CallClosure<Closure>),
188  reinterpret_cast<const uint8_t*>(&closure), inputs, num_inputs,
189  results, p);
190 }
191 
192 } // namespace hwy
193 
194 #endif // HIGHWAY_HWY_NANOBENCHMARK_H_
#define HWY_DLLEXPORT
Definition: highway_export.h:13
HWY_DLLEXPORT uint64_t TimerResolution()
HWY_DLLEXPORT double Now()
HWY_DLLEXPORT double InvariantTicksPerSecond()
Definition: aligned_allocator.h:27
static FuncOutput CallClosure(const Closure *f, const FuncInput input)
Definition: nanobenchmark.h:176
FuncOutput(*)(const void *, FuncInput) Func
Definition: nanobenchmark.h:105
size_t FuncInput
Definition: nanobenchmark.h:97
uint64_t FuncOutput
Definition: nanobenchmark.h:100
HWY_DLLEXPORT size_t Measure(const Func func, const uint8_t *arg, const FuncInput *inputs, const size_t num_inputs, Result *results, const Params &p=Params())
static size_t MeasureClosure(const Closure &closure, const FuncInput *inputs, const size_t num_inputs, Result *results, const Params &p=Params())
Definition: nanobenchmark.h:183
HWY_DLLEXPORT int Unpredictable1()
Definition: nanobenchmark.h:108
size_t subset_ratio
Definition: nanobenchmark.h:121
size_t precision_divisor
Definition: nanobenchmark.h:116
bool verbose
Definition: nanobenchmark.h:144
size_t min_mode_samples
Definition: nanobenchmark.h:134
static constexpr size_t kTimerSamples
Definition: nanobenchmark.h:112
size_t max_evals
Definition: nanobenchmark.h:141
double target_rel_mad
Definition: nanobenchmark.h:137
size_t min_samples_per_eval
Definition: nanobenchmark.h:129
double seconds_per_eval
Definition: nanobenchmark.h:126
Definition: nanobenchmark.h:148
float ticks
Definition: nanobenchmark.h:152
float variability
Definition: nanobenchmark.h:155
FuncInput input
Definition: nanobenchmark.h:149