xref: /external/google-benchmark/README.md

# benchmark
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A library to support the benchmarking of functions, similar to unit-tests.

[Discussion group](https://groups.google.com/d/forum/benchmark-discuss)

IRC channel: [freenode](https://freenode.net) #googlebenchmark

[Additional Tooling Documentation](docs/tools.md)

[Assembly Testing Documentation](docs/AssemblyTests.md)


## Building

The basic steps for configuring and building the library look like this:

```bash
$ git clone https://github.com/google/benchmark.git
# Benchmark requires Google Test as a dependency. Add the source tree as a subdirectory.
$ git clone https://github.com/google/googletest.git benchmark/googletest
$ mkdir build && cd build
$ cmake -G <generator> [options] ../benchmark
# Assuming a makefile generator was used
$ make
```

Note that Google Benchmark requires Google Test to build and run the tests. This
dependency can be provided two ways:

* Checkout the Google Test sources into `benchmark/googletest` as above.
* Otherwise, if `-DBENCHMARK_DOWNLOAD_DEPENDENCIES=ON` is specified during
  configuration, the library will automatically download and build any required
  dependencies.

If you do not wish to build and run the tests, add `-DBENCHMARK_ENABLE_GTEST_TESTS=OFF`
to `CMAKE_ARGS`.


## Installation Guide

For Ubuntu and Debian Based System

First make sure you have git and cmake installed (If not please install them)

```
sudo apt-get install git cmake
```

Now, let's clone the repository and build it

```
git clone https://github.com/google/benchmark.git
cd benchmark
# If you want to build tests and don't use BENCHMARK_DOWNLOAD_DEPENDENCIES, then
# git clone https://github.com/google/googletest.git
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=RELEASE
make
```

If you need to install the library globally

```
sudo make install
```

## Stable and Experimental Library Versions

The main branch contains the latest stable version of the benchmarking library;
the API of which can be considered largely stable, with source breaking changes
being made only upon the release of a new major version.

Newer, experimental, features are implemented and tested on the
[`v2` branch](https://github.com/google/benchmark/tree/v2). Users who wish
to use, test, and provide feedback on the new features are encouraged to try
this branch. However, this branch provides no stability guarantees and reserves
the right to change and break the API at any time.

## Further knowledge

It may help to read the [Google Test documentation](https://github.com/google/googletest/blob/master/googletest/docs/primer.md)
as some of the structural aspects of the APIs are similar.

## Example usage
### Basic usage
Define a function that executes the code to be measured, register it as a
benchmark function using the `BENCHMARK` macro, and ensure an appropriate `main`
function is available:

```c++
#include <benchmark/benchmark.h>

static void BM_StringCreation(benchmark::State& state) {
  for (auto _ : state)
    std::string empty_string;
}
// Register the function as a benchmark
BENCHMARK(BM_StringCreation);

// Define another benchmark
static void BM_StringCopy(benchmark::State& state) {
  std::string x = "hello";
  for (auto _ : state)
    std::string copy(x);
}
BENCHMARK(BM_StringCopy);

BENCHMARK_MAIN();
```

Don't forget to inform your linker to add benchmark library e.g. through 
`-lbenchmark` compilation flag. Alternatively, you may leave out the 
`BENCHMARK_MAIN();` at the end of the source file and link against 
`-lbenchmark_main` to get the same default behavior.

The benchmark library will measure and report the timing for code within the
`for(...)` loop.

#### Platform-specific libraries
When the library is built using GCC it is necessary to link with the pthread
library due to how GCC implements `std::thread`. Failing to link to pthread will
lead to runtime exceptions (unless you're using libc++), not linker errors. See
[issue #67](https://github.com/google/benchmark/issues/67) for more details. You
can link to pthread by adding `-pthread` to your linker command. Note, you can
also use `-lpthread`, but there are potential issues with ordering of command
line parameters if you use that.

If you're running benchmarks on Windows, the shlwapi library (`-lshlwapi`) is
also required.

If you're running benchmarks on solaris, you'll want the kstat library linked in
too (`-lkstat`).

### Passing arguments
Sometimes a family of benchmarks can be implemented with just one routine that
takes an extra argument to specify which one of the family of benchmarks to
run. For example, the following code defines a family of benchmarks for
measuring the speed of `memcpy()` calls of different lengths:

```c++
static void BM_memcpy(benchmark::State& state) {
  char* src = new char[state.range(0)];
  char* dst = new char[state.range(0)];
  memset(src, 'x', state.range(0));
  for (auto _ : state)
    memcpy(dst, src, state.range(0));
  state.SetBytesProcessed(int64_t(state.iterations()) *
                          int64_t(state.range(0)));
  delete[] src;
  delete[] dst;
}
BENCHMARK(BM_memcpy)->Arg(8)->Arg(64)->Arg(512)->Arg(1<<10)->Arg(8<<10);
```

The preceding code is quite repetitive, and can be replaced with the following
short-hand. The following invocation will pick a few appropriate arguments in
the specified range and will generate a benchmark for each such argument.

```c++
BENCHMARK(BM_memcpy)->Range(8, 8<<10);
```

By default the arguments in the range are generated in multiples of eight and
the command above selects [ 8, 64, 512, 4k, 8k ]. In the following code the
range multiplier is changed to multiples of two.

```c++
BENCHMARK(BM_memcpy)->RangeMultiplier(2)->Range(8, 8<<10);
```
Now arguments generated are [ 8, 16, 32, 64, 128, 256, 512, 1024, 2k, 4k, 8k ].

You might have a benchmark that depends on two or more inputs. For example, the
following code defines a family of benchmarks for measuring the speed of set
insertion.

```c++
static void BM_SetInsert(benchmark::State& state) {
  std::set<int> data;
  for (auto _ : state) {
    state.PauseTiming();
    data = ConstructRandomSet(state.range(0));
    state.ResumeTiming();
    for (int j = 0; j < state.range(1); ++j)
      data.insert(RandomNumber());
  }
}
BENCHMARK(BM_SetInsert)
    ->Args({1<<10, 128})
    ->Args({2<<10, 128})
    ->Args({4<<10, 128})
    ->Args({8<<10, 128})
    ->Args({1<<10, 512})
    ->Args({2<<10, 512})
    ->Args({4<<10, 512})
    ->Args({8<<10, 512});
```

The preceding code is quite repetitive, and can be replaced with the following
short-hand. The following macro will pick a few appropriate arguments in the
product of the two specified ranges and will generate a benchmark for each such
pair.

```c++
BENCHMARK(BM_SetInsert)->Ranges({{1<<10, 8<<10}, {128, 512}});
```

For more complex patterns of inputs, passing a custom function to `Apply` allows
programmatic specification of an arbitrary set of arguments on which to run the
benchmark. The following example enumerates a dense range on one parameter,
and a sparse range on the second.

```c++
static void CustomArguments(benchmark::internal::Benchmark* b) {
  for (int i = 0; i <= 10; ++i)
    for (int j = 32; j <= 1024*1024; j *= 8)
      b->Args({i, j});
}
BENCHMARK(BM_SetInsert)->Apply(CustomArguments);
```

### Calculate asymptotic complexity (Big O)
Asymptotic complexity might be calculated for a family of benchmarks. The
following code will calculate the coefficient for the high-order term in the
running time and the normalized root-mean square error of string comparison.

```c++
static void BM_StringCompare(benchmark::State& state) {
  std::string s1(state.range(0), '-');
  std::string s2(state.range(0), '-');
  for (auto _ : state) {
    benchmark::DoNotOptimize(s1.compare(s2));
  }
  state.SetComplexityN(state.range(0));
}
BENCHMARK(BM_StringCompare)
    ->RangeMultiplier(2)->Range(1<<10, 1<<18)->Complexity(benchmark::oN);
```

As shown in the following invocation, asymptotic complexity might also be
calculated automatically.

```c++
BENCHMARK(BM_StringCompare)
    ->RangeMultiplier(2)->Range(1<<10, 1<<18)->Complexity();
```

The following code will specify asymptotic complexity with a lambda function,
that might be used to customize high-order term calculation.

```c++
BENCHMARK(BM_StringCompare)->RangeMultiplier(2)
    ->Range(1<<10, 1<<18)->Complexity([](int64_t n)->double{return n; });
```

### Templated benchmarks
Templated benchmarks work the same way: This example produces and consumes
messages of size `sizeof(v)` `range_x` times. It also outputs throughput in the
absence of multiprogramming.

```c++
template <class Q> void BM_Sequential(benchmark::State& state) {
  Q q;
  typename Q::value_type v;
  for (auto _ : state) {
    for (int i = state.range(0); i--; )
      q.push(v);
    for (int e = state.range(0); e--; )
      q.Wait(&v);
  }
  // actually messages, not bytes:
  state.SetBytesProcessed(
      static_cast<int64_t>(state.iterations())*state.range(0));
}
BENCHMARK_TEMPLATE(BM_Sequential, WaitQueue<int>)->Range(1<<0, 1<<10);
```

Three macros are provided for adding benchmark templates.

```c++
#ifdef BENCHMARK_HAS_CXX11
#define BENCHMARK_TEMPLATE(func, ...) // Takes any number of parameters.
#else // C++ < C++11
#define BENCHMARK_TEMPLATE(func, arg1)
#endif
#define BENCHMARK_TEMPLATE1(func, arg1)
#define BENCHMARK_TEMPLATE2(func, arg1, arg2)
```

### A Faster KeepRunning loop

In C++11 mode, a ranged-based for loop should be used in preference to
the `KeepRunning` loop for running the benchmarks. For example:

```c++
static void BM_Fast(benchmark::State &state) {
  for (auto _ : state) {
    FastOperation();
  }
}
BENCHMARK(BM_Fast);
```

The reason the ranged-for loop is faster than using `KeepRunning`, is
because `KeepRunning` requires a memory load and store of the iteration count
ever iteration, whereas the ranged-for variant is able to keep the iteration count
in a register.

For example, an empty inner loop of using the ranged-based for method looks like:

```asm
# Loop Init
  mov rbx, qword ptr [r14 + 104]
  call benchmark::State::StartKeepRunning()
  test rbx, rbx
  je .LoopEnd
.LoopHeader: # =>This Inner Loop Header: Depth=1
  add rbx, -1
  jne .LoopHeader
.LoopEnd:
```

Compared to an empty `KeepRunning` loop, which looks like:

```asm
.LoopHeader: # in Loop: Header=BB0_3 Depth=1
  cmp byte ptr [rbx], 1
  jne .LoopInit
.LoopBody: # =>This Inner Loop Header: Depth=1
  mov rax, qword ptr [rbx + 8]
  lea rcx, [rax + 1]
  mov qword ptr [rbx + 8], rcx
  cmp rax, qword ptr [rbx + 104]
  jb .LoopHeader
  jmp .LoopEnd
.LoopInit:
  mov rdi, rbx
  call benchmark::State::StartKeepRunning()
  jmp .LoopBody
.LoopEnd:
```

Unless C++03 compatibility is required, the ranged-for variant of writing
the benchmark loop should be preferred.  

## Passing arbitrary arguments to a benchmark
In C++11 it is possible to define a benchmark that takes an arbitrary number
of extra arguments. The `BENCHMARK_CAPTURE(func, test_case_name, ...args)`
macro creates a benchmark that invokes `func`  with the `benchmark::State` as
the first argument followed by the specified `args...`.
The `test_case_name` is appended to the name of the benchmark and
should describe the values passed.

```c++
template <class ...ExtraArgs>
void BM_takes_args(benchmark::State& state, ExtraArgs&&... extra_args) {
  [...]
}
// Registers a benchmark named "BM_takes_args/int_string_test" that passes
// the specified values to `extra_args`.
BENCHMARK_CAPTURE(BM_takes_args, int_string_test, 42, std::string("abc"));
```
Note that elements of `...args` may refer to global variables. Users should
avoid modifying global state inside of a benchmark.

## Using RegisterBenchmark(name, fn, args...)

The `RegisterBenchmark(name, func, args...)` function provides an alternative
way to create and register benchmarks.
`RegisterBenchmark(name, func, args...)` creates, registers, and returns a
pointer to a new benchmark with the specified `name` that invokes
`func(st, args...)` where `st` is a `benchmark::State` object.

Unlike the `BENCHMARK` registration macros, which can only be used at the global
scope, the `RegisterBenchmark` can be called anywhere. This allows for
benchmark tests to be registered programmatically.

Additionally `RegisterBenchmark` allows any callable object to be registered
as a benchmark. Including capturing lambdas and function objects.

For Example:
```c++
auto BM_test = [](benchmark::State& st, auto Inputs) { /* ... */ };

int main(int argc, char** argv) {
  for (auto& test_input : { /* ... */ })
      benchmark::RegisterBenchmark(test_input.name(), BM_test, test_input);
  benchmark::Initialize(&argc, argv);
  benchmark::RunSpecifiedBenchmarks();
}
```

### Multithreaded benchmarks
In a multithreaded test (benchmark invoked by multiple threads simultaneously),
it is guaranteed that none of the threads will start until all have reached
the start of the benchmark loop, and all will have finished before any thread
exits the benchmark loop. (This behavior is also provided by the `KeepRunning()`
API) As such, any global setup or teardown can be wrapped in a check against the thread
index:

```c++
static void BM_MultiThreaded(benchmark::State& state) {
  if (state.thread_index == 0) {
    // Setup code here.
  }
  for (auto _ : state) {
    // Run the test as normal.
  }
  if (state.thread_index == 0) {
    // Teardown code here.
  }
}
BENCHMARK(BM_MultiThreaded)->Threads(2);
```

If the benchmarked code itself uses threads and you want to compare it to
single-threaded code, you may want to use real-time ("wallclock") measurements
for latency comparisons:

```c++
BENCHMARK(BM_test)->Range(8, 8<<10)->UseRealTime();
```

Without `UseRealTime`, CPU time is used by default.

## Controlling timers
Normally, the entire duration of the work loop (`for (auto _ : state) {}`)
is measured. But sometimes, it is nessesary to do some work inside of
that loop, every iteration, but without counting that time to the benchmark time.
That is possible, althought it is not recommended, since it has high overhead.

```c++
static void BM_SetInsert_With_Timer_Control(benchmark::State& state) {
  std::set<int> data;
  for (auto _ : state) {
    state.PauseTiming(); // Stop timers. They will not count until they are resumed.
    data = ConstructRandomSet(state.range(0)); // Do something that should not be measured
    state.ResumeTiming(); // And resume timers. They are now counting again.
    // The rest will be measured.
    for (int j = 0; j < state.range(1); ++j)
      data.insert(RandomNumber());
  }
}
BENCHMARK(BM_SetInsert_With_Timer_Control)->Ranges({{1<<10, 8<<10}, {128, 512}});
```

## Manual timing
For benchmarking something for which neither CPU time nor real-time are
correct or accurate enough, completely manual timing is supported using
the `UseManualTime` function.

When `UseManualTime` is used, the benchmarked code must call
`SetIterationTime` once per iteration of the benchmark loop to
report the manually measured time.

An example use case for this is benchmarking GPU execution (e.g. OpenCL
or CUDA kernels, OpenGL or Vulkan or Direct3D draw calls), which cannot
be accurately measured using CPU time or real-time. Instead, they can be
measured accurately using a dedicated API, and these measurement results
can be reported back with `SetIterationTime`.

```c++
static void BM_ManualTiming(benchmark::State& state) {
  int microseconds = state.range(0);
  std::chrono::duration<double, std::micro> sleep_duration {
    static_cast<double>(microseconds)
  };

  for (auto _ : state) {
    auto start = std::chrono::high_resolution_clock::now();
    // Simulate some useful workload with a sleep
    std::this_thread::sleep_for(sleep_duration);
    auto end   = std::chrono::high_resolution_clock::now();

    auto elapsed_seconds =
      std::chrono::duration_cast<std::chrono::duration<double>>(
        end - start);

    state.SetIterationTime(elapsed_seconds.count());
  }
}
BENCHMARK(BM_ManualTiming)->Range(1, 1<<17)->UseManualTime();
```

### Preventing optimisation
To prevent a value or expression from being optimized away by the compiler
the `benchmark::DoNotOptimize(...)` and `benchmark::ClobberMemory()`
functions can be used.

```c++
static void BM_test(benchmark::State& state) {
  for (auto _ : state) {
      int x = 0;
      for (int i=0; i < 64; ++i) {
        benchmark::DoNotOptimize(x += i);
      }
  }
}
```

`DoNotOptimize(<expr>)` forces the  *result* of `<expr>` to be stored in either
memory or a register. For GNU based compilers it acts as read/write barrier
for global memory. More specifically it forces the compiler to flush pending
writes to memory and reload any other values as necessary.

Note that `DoNotOptimize(<expr>)` does not prevent optimizations on `<expr>`
in any way. `<expr>` may even be removed entirely when the result is already
known. For example:

```c++
  /* Example 1: `<expr>` is removed entirely. */
  int foo(int x) { return x + 42; }
  while (...) DoNotOptimize(foo(0)); // Optimized to DoNotOptimize(42);

  /*  Example 2: Result of '<expr>' is only reused */
  int bar(int) __attribute__((const));
  while (...) DoNotOptimize(bar(0)); // Optimized to:
  // int __result__ = bar(0);
  // while (...) DoNotOptimize(__result__);
```

The second tool for preventing optimizations is `ClobberMemory()`. In essence
`ClobberMemory()` forces the compiler to perform all pending writes to global
memory. Memory managed by block scope objects must be "escaped" using
`DoNotOptimize(...)` before it can be clobbered. In the below example
`ClobberMemory()` prevents the call to `v.push_back(42)` from being optimized
away.

```c++
static void BM_vector_push_back(benchmark::State& state) {
  for (auto _ : state) {
    std::vector<int> v;
    v.reserve(1);
    benchmark::DoNotOptimize(v.data()); // Allow v.data() to be clobbered.
    v.push_back(42);
    benchmark::ClobberMemory(); // Force 42 to be written to memory.
  }
}
```

Note that `ClobberMemory()` is only available for GNU or MSVC based compilers.

### Set time unit manually
If a benchmark runs a few milliseconds it may be hard to visually compare the
measured times, since the output data is given in nanoseconds per default. In
order to manually set the time unit, you can specify it manually:

```c++
BENCHMARK(BM_test)->Unit(benchmark::kMillisecond);
```

### Reporting the mean, median and standard deviation by repeated benchmarks
By default each benchmark is run once and that single result is reported.
However benchmarks are often noisy and a single result may not be representative
of the overall behavior. For this reason it's possible to repeatedly rerun the
benchmark.

The number of runs of each benchmark is specified globally by the
`--benchmark_repetitions` flag or on a per benchmark basis by calling
`Repetitions` on the registered benchmark object. When a benchmark is run more
than once the mean, median and standard deviation of the runs will be reported.

Additionally the `--benchmark_report_aggregates_only={true|false}`,
`--benchmark_display_aggregates_only={true|false}` flags or
`ReportAggregatesOnly(bool)`, `DisplayAggregatesOnly(bool)` functions can be
used to change how repeated tests are reported. By default the result of each
repeated run is reported. When `report aggregates only` option is `true`,
only the aggregates (i.e. mean, median and standard deviation, maybe complexity
measurements if they were requested) of the runs is reported, to both the
reporters - standard output (console), and the file.
However when only the `display aggregates only` option is `true`,
only the aggregates are displayed in the standard output, while the file
output still contains everything.
Calling `ReportAggregatesOnly(bool)` / `DisplayAggregatesOnly(bool)` on a
registered benchmark object overrides the value of the appropriate flag for that
benchmark.

## User-defined statistics for repeated benchmarks
While having mean, median and standard deviation is nice, this may not be
enough for everyone. For example you may want to know what is the largest
observation, e.g. because you have some real-time constraints. This is easy.
The following code will specify a custom statistic to be calculated, defined
by a lambda function.

```c++
void BM_spin_empty(benchmark::State& state) {
  for (auto _ : state) {
    for (int x = 0; x < state.range(0); ++x) {
      benchmark::DoNotOptimize(x);
    }
  }
}

BENCHMARK(BM_spin_empty)
  ->ComputeStatistics("max", [](const std::vector<double>& v) -> double {
    return *(std::max_element(std::begin(v), std::end(v)));
  })
  ->Arg(512);
```

## Fixtures
Fixture tests are created by
first defining a type that derives from `::benchmark::Fixture` and then
creating/registering the tests using the following macros:

* `BENCHMARK_F(ClassName, Method)`
* `BENCHMARK_DEFINE_F(ClassName, Method)`
* `BENCHMARK_REGISTER_F(ClassName, Method)`

For Example:

```c++
class MyFixture : public benchmark::Fixture {
public:
  void SetUp(const ::benchmark::State& state) {
  }

  void TearDown(const ::benchmark::State& state) {
  }
};

BENCHMARK_F(MyFixture, FooTest)(benchmark::State& st) {
   for (auto _ : st) {
     ...
  }
}

BENCHMARK_DEFINE_F(MyFixture, BarTest)(benchmark::State& st) {
   for (auto _ : st) {
     ...
  }
}
/* BarTest is NOT registered */
BENCHMARK_REGISTER_F(MyFixture, BarTest)->Threads(2);
/* BarTest is now registered */
```

### Templated fixtures
Also you can create templated fixture by using the following macros:

* `BENCHMARK_TEMPLATE_F(ClassName, Method, ...)`
* `BENCHMARK_TEMPLATE_DEFINE_F(ClassName, Method, ...)`

For example:
```c++
template<typename T>
class MyFixture : public benchmark::Fixture {};

BENCHMARK_TEMPLATE_F(MyFixture, IntTest, int)(benchmark::State& st) {
   for (auto _ : st) {
     ...
  }
}

BENCHMARK_TEMPLATE_DEFINE_F(MyFixture, DoubleTest, double)(benchmark::State& st) {
   for (auto _ : st) {
     ...
  }
}

BENCHMARK_REGISTER_F(MyFixture, DoubleTest)->Threads(2);
```

## User-defined counters

You can add your own counters with user-defined names. The example below
will add columns "Foo", "Bar" and "Baz" in its output:

```c++
static void UserCountersExample1(benchmark::State& state) {
  double numFoos = 0, numBars = 0, numBazs = 0;
  for (auto _ : state) {
    // ... count Foo,Bar,Baz events
  }
  state.counters["Foo"] = numFoos;
  state.counters["Bar"] = numBars;
  state.counters["Baz"] = numBazs;
}
```

The `state.counters` object is a `std::map` with `std::string` keys
and `Counter` values. The latter is a `double`-like class, via an implicit
conversion to `double&`. Thus you can use all of the standard arithmetic
assignment operators (`=,+=,-=,*=,/=`) to change the value of each counter.

In multithreaded benchmarks, each counter is set on the calling thread only.
When the benchmark finishes, the counters from each thread will be summed;
the resulting sum is the value which will be shown for the benchmark.

The `Counter` constructor accepts three parameters: the value as a `double`
; a bit flag which allows you to show counters as rates, and/or as per-thread
iteration, and/or as per-thread averages, and/or iteration invariants;
and a flag specifying the 'unit' - i.e. is 1k a 1000 (default,
`benchmark::Counter::OneK::kIs1000`), or 1024
(`benchmark::Counter::OneK::kIs1024`)?

```c++
  // sets a simple counter
  state.counters["Foo"] = numFoos;

  // Set the counter as a rate. It will be presented divided
  // by the duration of the benchmark.
  state.counters["FooRate"] = Counter(numFoos, benchmark::Counter::kIsRate);

  // Set the counter as a thread-average quantity. It will
  // be presented divided by the number of threads.
  state.counters["FooAvg"] = Counter(numFoos, benchmark::Counter::kAvgThreads);

  // There's also a combined flag:
  state.counters["FooAvgRate"] = Counter(numFoos,benchmark::Counter::kAvgThreadsRate);

  // This says that we process with the rate of state.range(0) bytes every iteration:
  state.counters["BytesProcessed"] = Counter(state.range(0), benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::OneK::kIs1024);
```

When you're compiling in C++11 mode or later you can use `insert()` with
`std::initializer_list`:

```c++
  // With C++11, this can be done:
  state.counters.insert({{"Foo", numFoos}, {"Bar", numBars}, {"Baz", numBazs}});
  // ... instead of:
  state.counters["Foo"] = numFoos;
  state.counters["Bar"] = numBars;
  state.counters["Baz"] = numBazs;
```

### Counter reporting

When using the console reporter, by default, user counters are are printed at
the end after the table, the same way as ``bytes_processed`` and
``items_processed``. This is best for cases in which there are few counters,
or where there are only a couple of lines per benchmark. Here's an example of
the default output:

```
------------------------------------------------------------------------------
Benchmark                        Time           CPU Iterations UserCounters...
------------------------------------------------------------------------------
BM_UserCounter/threads:8      2248 ns      10277 ns      68808 Bar=16 Bat=40 Baz=24 Foo=8
BM_UserCounter/threads:1      9797 ns       9788 ns      71523 Bar=2 Bat=5 Baz=3 Foo=1024m
BM_UserCounter/threads:2      4924 ns       9842 ns      71036 Bar=4 Bat=10 Baz=6 Foo=2
BM_UserCounter/threads:4      2589 ns      10284 ns      68012 Bar=8 Bat=20 Baz=12 Foo=4
BM_UserCounter/threads:8      2212 ns      10287 ns      68040 Bar=16 Bat=40 Baz=24 Foo=8
BM_UserCounter/threads:16     1782 ns      10278 ns      68144 Bar=32 Bat=80 Baz=48 Foo=16
BM_UserCounter/threads:32     1291 ns      10296 ns      68256 Bar=64 Bat=160 Baz=96 Foo=32
BM_UserCounter/threads:4      2615 ns      10307 ns      68040 Bar=8 Bat=20 Baz=12 Foo=4
BM_Factorial                    26 ns         26 ns   26608979 40320
BM_Factorial/real_time          26 ns         26 ns   26587936 40320
BM_CalculatePiRange/1           16 ns         16 ns   45704255 0
BM_CalculatePiRange/8           73 ns         73 ns    9520927 3.28374
BM_CalculatePiRange/64         609 ns        609 ns    1140647 3.15746
BM_CalculatePiRange/512       4900 ns       4901 ns     142696 3.14355
```

If this doesn't suit you, you can print each counter as a table column by
passing the flag `--benchmark_counters_tabular=true` to the benchmark
application. This is best for cases in which there are a lot of counters, or
a lot of lines per individual benchmark. Note that this will trigger a
reprinting of the table header any time the counter set changes between
individual benchmarks. Here's an example of corresponding output when
`--benchmark_counters_tabular=true` is passed:

```
---------------------------------------------------------------------------------------
Benchmark                        Time           CPU Iterations    Bar   Bat   Baz   Foo
---------------------------------------------------------------------------------------
BM_UserCounter/threads:8      2198 ns       9953 ns      70688     16    40    24     8
BM_UserCounter/threads:1      9504 ns       9504 ns      73787      2     5     3     1
BM_UserCounter/threads:2      4775 ns       9550 ns      72606      4    10     6     2
BM_UserCounter/threads:4      2508 ns       9951 ns      70332      8    20    12     4
BM_UserCounter/threads:8      2055 ns       9933 ns      70344     16    40    24     8
BM_UserCounter/threads:16     1610 ns       9946 ns      70720     32    80    48    16
BM_UserCounter/threads:32     1192 ns       9948 ns      70496     64   160    96    32
BM_UserCounter/threads:4      2506 ns       9949 ns      70332      8    20    12     4
--------------------------------------------------------------
Benchmark                        Time           CPU Iterations
--------------------------------------------------------------
BM_Factorial                    26 ns         26 ns   26392245 40320
BM_Factorial/real_time          26 ns         26 ns   26494107 40320
BM_CalculatePiRange/1           15 ns         15 ns   45571597 0
BM_CalculatePiRange/8           74 ns         74 ns    9450212 3.28374
BM_CalculatePiRange/64         595 ns        595 ns    1173901 3.15746
BM_CalculatePiRange/512       4752 ns       4752 ns     147380 3.14355
BM_CalculatePiRange/4k       37970 ns      37972 ns      18453 3.14184
BM_CalculatePiRange/32k     303733 ns     303744 ns       2305 3.14162
BM_CalculatePiRange/256k   2434095 ns    2434186 ns        288 3.1416
BM_CalculatePiRange/1024k  9721140 ns    9721413 ns         71 3.14159
BM_CalculatePi/threads:8      2255 ns       9943 ns      70936
```
Note above the additional header printed when the benchmark changes from
``BM_UserCounter`` to ``BM_Factorial``. This is because ``BM_Factorial`` does
not have the same counter set as ``BM_UserCounter``.

## Exiting Benchmarks in Error

When errors caused by external influences, such as file I/O and network
communication, occur within a benchmark the
`State::SkipWithError(const char* msg)` function can be used to skip that run
of benchmark and report the error. Note that only future iterations of the
`KeepRunning()` are skipped. For the ranged-for version of the benchmark loop
Users must explicitly exit the loop, otherwise all iterations will be performed.
Users may explicitly return to exit the benchmark immediately.

The `SkipWithError(...)` function may be used at any point within the benchmark,
including before and after the benchmark loop.

For example:

```c++
static void BM_test(benchmark::State& state) {
  auto resource = GetResource();
  if (!resource.good()) {
      state.SkipWithError("Resource is not good!");
      // KeepRunning() loop will not be entered.
  }
  for (state.KeepRunning()) {
      auto data = resource.read_data();
      if (!resource.good()) {
        state.SkipWithError("Failed to read data!");
        break; // Needed to skip the rest of the iteration.
     }
     do_stuff(data);
  }
}

static void BM_test_ranged_fo(benchmark::State & state) {
  state.SkipWithError("test will not be entered");
  for (auto _ : state) {
    state.SkipWithError("Failed!");
    break; // REQUIRED to prevent all further iterations.
  }
}
```

## Running a subset of the benchmarks

The `--benchmark_filter=<regex>` option can be used to only run the benchmarks
which match the specified `<regex>`. For example:

```bash
$ ./run_benchmarks.x --benchmark_filter=BM_memcpy/32
Run on (1 X 2300 MHz CPU )
2016-06-25 19:34:24
Benchmark              Time           CPU Iterations
----------------------------------------------------
BM_memcpy/32          11 ns         11 ns   79545455
BM_memcpy/32k       2181 ns       2185 ns     324074
BM_memcpy/32          12 ns         12 ns   54687500
BM_memcpy/32k       1834 ns       1837 ns     357143
```

## Runtime and reporting considerations
When the benchmark binary is executed, each benchmark function is run serially.
The number of iterations to run is determined dynamically by running the
benchmark a few times and measuring the time taken and ensuring that the
ultimate result will be statistically stable. As such, faster benchmark
functions will be run for more iterations than slower benchmark functions, and
the number of iterations is thus reported.

In all cases, the number of iterations for which the benchmark is run is
governed by the amount of time the benchmark takes. Concretely, the number of
iterations is at least one, not more than 1e9, until CPU time is greater than
the minimum time, or the wallclock time is 5x minimum time. The minimum time is
set per benchmark by calling `MinTime` on the registered benchmark object.

Average timings are then reported over the iterations run. If multiple
repetitions are requested using the `--benchmark_repetitions` command-line
option, or at registration time, the benchmark function will be run several
times and statistical results across these repetitions will also be reported.

As well as the per-benchmark entries, a preamble in the report will include
information about the machine on which the benchmarks are run.

### Output Formats
The library supports multiple output formats. Use the
`--benchmark_format=<console|json|csv>` flag to set the format type. `console`
is the default format.

The Console format is intended to be a human readable format. By default
the format generates color output. Context is output on stderr and the
tabular data on stdout. Example tabular output looks like:
```
Benchmark                               Time(ns)    CPU(ns) Iterations
----------------------------------------------------------------------
BM_SetInsert/1024/1                        28928      29349      23853  133.097kB/s   33.2742k items/s
BM_SetInsert/1024/8                        32065      32913      21375  949.487kB/s   237.372k items/s
BM_SetInsert/1024/10                       33157      33648      21431  1.13369MB/s   290.225k items/s
```

The JSON format outputs human readable json split into two top level attributes.
The `context` attribute contains information about the run in general, including
information about the CPU and the date.
The `benchmarks` attribute contains a list of every benchmark run. Example json
output looks like:
```json
{
  "context": {
    "date": "2015/03/17-18:40:25",
    "num_cpus": 40,
    "mhz_per_cpu": 2801,
    "cpu_scaling_enabled": false,
    "build_type": "debug"
  },
  "benchmarks": [
    {
      "name": "BM_SetInsert/1024/1",
      "iterations": 94877,
      "real_time": 29275,
      "cpu_time": 29836,
      "bytes_per_second": 134066,
      "items_per_second": 33516
    },
    {
      "name": "BM_SetInsert/1024/8",
      "iterations": 21609,
      "real_time": 32317,
      "cpu_time": 32429,
      "bytes_per_second": 986770,
      "items_per_second": 246693
    },
    {
      "name": "BM_SetInsert/1024/10",
      "iterations": 21393,
      "real_time": 32724,
      "cpu_time": 33355,
      "bytes_per_second": 1199226,
      "items_per_second": 299807
    }
  ]
}
```

The CSV format outputs comma-separated values. The `context` is output on stderr
and the CSV itself on stdout. Example CSV output looks like:
```
name,iterations,real_time,cpu_time,bytes_per_second,items_per_second,label
"BM_SetInsert/1024/1",65465,17890.7,8407.45,475768,118942,
"BM_SetInsert/1024/8",116606,18810.1,9766.64,3.27646e+06,819115,
"BM_SetInsert/1024/10",106365,17238.4,8421.53,4.74973e+06,1.18743e+06,
```

### Output Files
The library supports writing the output of the benchmark to a file specified
by `--benchmark_out=<filename>`. The format of the output can be specified
using `--benchmark_out_format={json|console|csv}`. Specifying
`--benchmark_out` does not suppress the console output.

## Result comparison

It is possible to compare the benchmarking results. See [Additional Tooling Documentation](docs/tools.md)

## Debug vs Release
By default, benchmark builds as a debug library. You will see a warning in the
output when this is the case. To build it as a release library instead, use:

```
cmake -DCMAKE_BUILD_TYPE=Release
```

To enable link-time optimisation, use

```
cmake -DCMAKE_BUILD_TYPE=Release -DBENCHMARK_ENABLE_LTO=true
```

If you are using gcc, you might need to set `GCC_AR` and `GCC_RANLIB` cmake
cache variables, if autodetection fails.

If you are using clang, you may need to set `LLVMAR_EXECUTABLE`,
`LLVMNM_EXECUTABLE` and `LLVMRANLIB_EXECUTABLE` cmake cache variables.

## Compiler Support

Google Benchmark uses C++11 when building the library. As such we require
a modern C++ toolchain, both compiler and standard library.

The following minimum versions are strongly recommended build the library:

* GCC 4.8
* Clang 3.4
* Visual Studio 2013
* Intel 2015 Update 1

Anything older *may* work.

Note: Using the library and its headers in C++03 is supported. C++11 is only
required to build the library.

## Disable CPU frequency scaling
If you see this error:
```
***WARNING*** CPU scaling is enabled, the benchmark real time measurements may be noisy and will incur extra overhead.
```
you might want to disable the CPU frequency scaling while running the benchmark:
```bash
sudo cpupower frequency-set --governor performance
./mybench
sudo cpupower frequency-set --governor powersave
```