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https://github.com/google/benchmark.git
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395 lines
13 KiB
Markdown
395 lines
13 KiB
Markdown
# benchmark
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[](https://travis-ci.org/google/benchmark)
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[](https://ci.appveyor.com/project/google/benchmark/branch/master)
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[](https://coveralls.io/r/google/benchmark)
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A library to support the benchmarking of functions, similar to unit-tests.
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Discussion group: https://groups.google.com/d/forum/benchmark-discuss
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IRC channel: https://freenode.net #googlebenchmark
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## Example usage
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### Basic usage
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Define a function that executes the code to be measured.
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```c++
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static void BM_StringCreation(benchmark::State& state) {
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while (state.KeepRunning())
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std::string empty_string;
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}
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// Register the function as a benchmark
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BENCHMARK(BM_StringCreation);
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// Define another benchmark
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static void BM_StringCopy(benchmark::State& state) {
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std::string x = "hello";
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while (state.KeepRunning())
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std::string copy(x);
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}
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BENCHMARK(BM_StringCopy);
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BENCHMARK_MAIN();
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```
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### Passing arguments
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Sometimes a family of benchmarks can be implemented with just one routine that
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takes an extra argument to specify which one of the family of benchmarks to
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run. For example, the following code defines a family of benchmarks for
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measuring the speed of `memcpy()` calls of different lengths:
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```c++
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static void BM_memcpy(benchmark::State& state) {
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char* src = new char[state.range_x()];
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char* dst = new char[state.range_x()];
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memset(src, 'x', state.range_x());
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while (state.KeepRunning())
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memcpy(dst, src, state.range_x());
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state.SetBytesProcessed(int64_t(state.iterations()) *
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int64_t(state.range_x()));
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delete[] src;
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delete[] dst;
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}
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BENCHMARK(BM_memcpy)->Arg(8)->Arg(64)->Arg(512)->Arg(1<<10)->Arg(8<<10);
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```
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The preceding code is quite repetitive, and can be replaced with the following
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short-hand. The following invocation will pick a few appropriate arguments in
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the specified range and will generate a benchmark for each such argument.
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```c++
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BENCHMARK(BM_memcpy)->Range(8, 8<<10);
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```
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By default the arguments in the range are generated in multiples of eight and
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the command above selects [ 8, 64, 512, 4k, 8k ]. In the following code the
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range multiplier is changed to multiples of two.
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```c++
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BENCHMARK(BM_memcpy)->RangeMultiplier(2)->Range(8, 8<<10);
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```
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Now arguments generated are [ 8, 16, 32, 64, 128, 256, 512, 1024, 2k, 4k, 8k ].
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You might have a benchmark that depends on two inputs. For example, the
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following code defines a family of benchmarks for measuring the speed of set
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insertion.
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```c++
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static void BM_SetInsert(benchmark::State& state) {
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while (state.KeepRunning()) {
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state.PauseTiming();
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std::set<int> data = ConstructRandomSet(state.range_x());
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state.ResumeTiming();
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for (int j = 0; j < state.range_y(); ++j)
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data.insert(RandomNumber());
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}
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}
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BENCHMARK(BM_SetInsert)
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->ArgPair(1<<10, 1)
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->ArgPair(1<<10, 8)
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->ArgPair(1<<10, 64)
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->ArgPair(1<<10, 512)
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->ArgPair(8<<10, 1)
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->ArgPair(8<<10, 8)
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->ArgPair(8<<10, 64)
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->ArgPair(8<<10, 512);
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```
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The preceding code is quite repetitive, and can be replaced with the following
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short-hand. The following macro will pick a few appropriate arguments in the
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product of the two specified ranges and will generate a benchmark for each such
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pair.
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```c++
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BENCHMARK(BM_SetInsert)->RangePair(1<<10, 8<<10, 1, 512);
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```
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For more complex patterns of inputs, passing a custom function to `Apply` allows
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programmatic specification of an arbitrary set of arguments on which to run the
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benchmark. The following example enumerates a dense range on one parameter,
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and a sparse range on the second.
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```c++
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static void CustomArguments(benchmark::internal::Benchmark* b) {
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for (int i = 0; i <= 10; ++i)
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for (int j = 32; j <= 1024*1024; j *= 8)
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b->ArgPair(i, j);
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}
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BENCHMARK(BM_SetInsert)->Apply(CustomArguments);
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```
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### Calculate asymptotic complexity (Big O)
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Asymptotic complexity might be calculated for a family of benchmarks. The
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following code will calculate the coefficient for the high-order term in the
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running time and the normalized root-mean square error of string comparison.
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```c++
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static void BM_StringCompare(benchmark::State& state) {
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std::string s1(state.range_x(), '-');
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std::string s2(state.range_x(), '-');
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while (state.KeepRunning())
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benchmark::DoNotOptimize(s1.compare(s2));
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}
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BENCHMARK(BM_StringCompare)
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->RangeMultiplier(2)->Range(1<<10, 1<<18)->Complexity(benchmark::oN);
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```
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As shown in the following invocation, asymptotic complexity might also be
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calculated automatically.
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```c++
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BENCHMARK(BM_StringCompare)
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->RangeMultiplier(2)->Range(1<<10, 1<<18)->Complexity(benchmark::oAuto);
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```
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### Templated benchmarks
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Templated benchmarks work the same way: This example produces and consumes
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messages of size `sizeof(v)` `range_x` times. It also outputs throughput in the
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absence of multiprogramming.
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```c++
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template <class Q> int BM_Sequential(benchmark::State& state) {
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Q q;
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typename Q::value_type v;
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while (state.KeepRunning()) {
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for (int i = state.range_x(); i--; )
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q.push(v);
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for (int e = state.range_x(); e--; )
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q.Wait(&v);
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}
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// actually messages, not bytes:
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state.SetBytesProcessed(
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static_cast<int64_t>(state.iterations())*state.range_x());
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}
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BENCHMARK_TEMPLATE(BM_Sequential, WaitQueue<int>)->Range(1<<0, 1<<10);
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```
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Three macros are provided for adding benchmark templates.
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```c++
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#if __cplusplus >= 201103L // C++11 and greater.
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#define BENCHMARK_TEMPLATE(func, ...) // Takes any number of parameters.
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#else // C++ < C++11
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#define BENCHMARK_TEMPLATE(func, arg1)
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#endif
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#define BENCHMARK_TEMPLATE1(func, arg1)
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#define BENCHMARK_TEMPLATE2(func, arg1, arg2)
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```
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### Multithreaded benchmarks
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In a multithreaded test (benchmark invoked by multiple threads simultaneously),
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it is guaranteed that none of the threads will start until all have called
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`KeepRunning`, and all will have finished before KeepRunning returns false. As
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such, any global setup or teardown can be wrapped in a check against the thread
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index:
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```c++
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static void BM_MultiThreaded(benchmark::State& state) {
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if (state.thread_index == 0) {
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// Setup code here.
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}
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while (state.KeepRunning()) {
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// Run the test as normal.
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}
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if (state.thread_index == 0) {
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// Teardown code here.
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}
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}
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BENCHMARK(BM_MultiThreaded)->Threads(2);
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```
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If the benchmarked code itself uses threads and you want to compare it to
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single-threaded code, you may want to use real-time ("wallclock") measurements
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for latency comparisons:
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```c++
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BENCHMARK(BM_test)->Range(8, 8<<10)->UseRealTime();
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```
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Without `UseRealTime`, CPU time is used by default.
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## Manual timing
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For benchmarking something for which neither CPU time nor real-time are
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correct or accurate enough, completely manual timing is supported using
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the `UseManualTime` function.
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When `UseManualTime` is used, the benchmarked code must call
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`SetIterationTime` once per iteration of the `KeepRunning` loop to
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report the manually measured time.
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An example use case for this is benchmarking GPU execution (e.g. OpenCL
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or CUDA kernels, OpenGL or Vulkan or Direct3D draw calls), which cannot
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be accurately measured using CPU time or real-time. Instead, they can be
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measured accurately using a dedicated API, and these measurement results
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can be reported back with `SetIterationTime`.
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```c++
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static void BM_ManualTiming(benchmark::State& state) {
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int microseconds = state.range_x();
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std::chrono::duration<double, std::micro> sleep_duration {
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static_cast<double>(microseconds)
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};
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while (state.KeepRunning()) {
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auto start = std::chrono::high_resolution_clock::now();
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// Simulate some useful workload with a sleep
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std::this_thread::sleep_for(sleep_duration);
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auto end = std::chrono::high_resolution_clock::now();
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auto elapsed_seconds =
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std::chrono::duration_cast<std::chrono::duration<double>>(
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end - start);
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state.SetIterationTime(elapsed_seconds.count());
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}
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}
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BENCHMARK(BM_ManualTiming)->Range(1, 1<<17)->UseManualTime();
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```
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### Preventing optimisation
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To prevent a value or expression from being optimized away by the compiler
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the `benchmark::DoNotOptimize(...)` function can be used.
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```c++
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static void BM_test(benchmark::State& state) {
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while (state.KeepRunning()) {
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int x = 0;
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for (int i=0; i < 64; ++i) {
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benchmark::DoNotOptimize(x += i);
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}
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}
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}
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```
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### Set time unit manually
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If a benchmark runs a few milliseconds it may be hard to visually compare the
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measured times, since the output data is given in nanoseconds per default. In
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order to manually set the time unit, you can specify it manually:
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```c++
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BENCHMARK(BM_test)->Unit(benchmark::kMillisecond);
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```
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## Controlling number of iterations
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In all cases, the number of iterations for which the benchmark is run is
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governed by the amount of time the benchmark takes. Concretely, the number of
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iterations is at least one, not more than 1e9, until CPU time is greater than
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the minimum time, or the wallclock time is 5x minimum time. The minimum time is
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set as a flag `--benchmark_min_time` or per benchmark by calling `MinTime` on
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the registered benchmark object.
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## Fixtures
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Fixture tests are created by
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first defining a type that derives from ::benchmark::Fixture and then
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creating/registering the tests using the following macros:
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* `BENCHMARK_F(ClassName, Method)`
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* `BENCHMARK_DEFINE_F(ClassName, Method)`
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* `BENCHMARK_REGISTER_F(ClassName, Method)`
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For Example:
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```c++
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class MyFixture : public benchmark::Fixture {};
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BENCHMARK_F(MyFixture, FooTest)(benchmark::State& st) {
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while (st.KeepRunning()) {
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...
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}
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}
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BENCHMARK_DEFINE_F(MyFixture, BarTest)(benchmark::State& st) {
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while (st.KeepRunning()) {
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...
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}
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}
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/* BarTest is NOT registered */
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BENCHMARK_REGISTER_F(MyFixture, BarTest)->Threads(2);
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/* BarTest is now registered */
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```
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## Output Formats
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The library supports multiple output formats. Use the
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`--benchmark_format=<tabular|json>` flag to set the format type. `tabular` is
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the default format.
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The Tabular format is intended to be a human readable format. By default
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the format generates color output. Context is output on stderr and the
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tabular data on stdout. Example tabular output looks like:
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```
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Benchmark Time(ns) CPU(ns) Iterations
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----------------------------------------------------------------------
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BM_SetInsert/1024/1 28928 29349 23853 133.097kB/s 33.2742k items/s
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BM_SetInsert/1024/8 32065 32913 21375 949.487kB/s 237.372k items/s
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BM_SetInsert/1024/10 33157 33648 21431 1.13369MB/s 290.225k items/s
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```
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The JSON format outputs human readable json split into two top level attributes.
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The `context` attribute contains information about the run in general, including
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information about the CPU and the date.
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The `benchmarks` attribute contains a list of ever benchmark run. Example json
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output looks like:
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``` json
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{
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"context": {
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"date": "2015/03/17-18:40:25",
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"num_cpus": 40,
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"mhz_per_cpu": 2801,
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"cpu_scaling_enabled": false,
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"build_type": "debug"
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},
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"benchmarks": [
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{
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"name": "BM_SetInsert/1024/1",
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"iterations": 94877,
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"real_time": 29275,
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"cpu_time": 29836,
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"bytes_per_second": 134066,
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"items_per_second": 33516
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},
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{
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"name": "BM_SetInsert/1024/8",
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"iterations": 21609,
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"real_time": 32317,
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"cpu_time": 32429,
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"bytes_per_second": 986770,
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"items_per_second": 246693
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},
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{
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"name": "BM_SetInsert/1024/10",
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"iterations": 21393,
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"real_time": 32724,
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"cpu_time": 33355,
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"bytes_per_second": 1199226,
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"items_per_second": 299807
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}
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]
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}
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```
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The CSV format outputs comma-separated values. The `context` is output on stderr
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and the CSV itself on stdout. Example CSV output looks like:
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```
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name,iterations,real_time,cpu_time,bytes_per_second,items_per_second,label
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"BM_SetInsert/1024/1",65465,17890.7,8407.45,475768,118942,
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"BM_SetInsert/1024/8",116606,18810.1,9766.64,3.27646e+06,819115,
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"BM_SetInsert/1024/10",106365,17238.4,8421.53,4.74973e+06,1.18743e+06,
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```
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## Debug vs Release
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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:
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```
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cmake -DCMAKE_BUILD_TYPE=Release
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```
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To enable link-time optimisation, use
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```
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cmake -DCMAKE_BUILD_TYPE=Release -DBENCHMARK_ENABLE_LTO=true
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```
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## Linking against the library
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When using gcc, it is necessary to link against pthread to avoid runtime exceptions. This is due to how gcc implements std::thread. See [issue #67](https://github.com/google/benchmark/issues/67) for more details.
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