| commit | 2a3ad8c6e87c921610448a4db80326ca920d4a5f | [log] [tgz] |
|---|---|---|
| author | Dominic Hamon <dma+github@stripysock.com> | Fri Jun 04 15:50:08 2021 +0100 |
| committer | Dominic Hamon <dma+github@stripysock.com> | Fri Jun 04 15:50:08 2021 +0100 |
| tree | fa64ac264c3e11c443a9cac5eda29140fda3b108 | |
| parent | dae05dfca3e2031546e741801ac959a1838badc9 [diff] |
don't override libc++
A library to benchmark code snippets, similar to unit tests. Example:
#include <benchmark/benchmark.h> static void BM_SomeFunction(benchmark::State& state) { // Perform setup here for (auto _ : state) { // This code gets timed SomeFunction(); } } // Register the function as a benchmark BENCHMARK(BM_SomeFunction); // Run the benchmark BENCHMARK_MAIN();
To get started, see Requirements and Installation. See Usage for a full example and the User Guide for a more comprehensive feature overview.
It may also help to read the Google Test documentation as some of the structural aspects of the APIs are similar.
IRC channels:
Additional Tooling Documentation
Assembly Testing Documentation
The library can be used with C++03. However, it requires C++11 to build, including compiler and standard library support.
The following minimum versions are required to build the library:
See Platform-Specific Build Instructions.
This describes the installation process using cmake. As pre-requisites, you'll need git and cmake installed.
See dependencies.md for more details regarding supported versions of build tools.
# Check out the library. $ 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 # Go to the library root directory $ cd benchmark # Make a build directory to place the build output. $ cmake -E make_directory "build" # Generate build system files with cmake. $ cmake -E chdir "build" cmake -DCMAKE_BUILD_TYPE=Release ../ # or, starting with CMake 3.13, use a simpler form: # cmake -DCMAKE_BUILD_TYPE=Release -S . -B "build" # Build the library. $ cmake --build "build" --config Release
This builds the benchmark and benchmark_main libraries and tests. On a unix system, the build directory should now look something like this:
/benchmark
/build
/src
/libbenchmark.a
/libbenchmark_main.a
/test
...
Next, you can run the tests to check the build.
$ cmake -E chdir "build" ctest --build-config Release
If you want to install the library globally, also run:
sudo cmake --build "build" --config Release --target install
Note that Google Benchmark requires Google Test to build and run the tests. This dependency can be provided two ways:
benchmark/googletest as above.-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.
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, add -DCMAKE_BUILD_TYPE=Release when generating the build system files, as shown above. The use of --config Release in build commands is needed to properly support multi-configuration tools (like Visual Studio for example) and can be skipped for other build systems (like Makefile).
To enable link-time optimisation, also add -DBENCHMARK_ENABLE_LTO=true when generating the build system files.
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.
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. 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.
Define a function that executes the code to measure, register it as a benchmark function using the BENCHMARK macro, and ensure an appropriate main function is available:
#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();
To run the benchmark, compile and link against the benchmark library (libbenchmark.a/.so). If you followed the build steps above, this library will be under the build directory you created.
# Example on linux after running the build steps above. Assumes the # `benchmark` and `build` directories are under the current directory. $ g++ mybenchmark.cc -std=c++11 -isystem benchmark/include \ -Lbenchmark/build/src -lbenchmark -lpthread -o mybenchmark
Alternatively, link against the benchmark_main library and remove BENCHMARK_MAIN(); above to get the same behavior.
The compiled executable will run all benchmarks by default. Pass the --help flag for option information or see the guide below.
If using CMake, it is recommended to link against the project-provided benchmark::benchmark and benchmark::benchmark_main targets using target_link_libraries. It is possible to use find_package to import an installed version of the library.
find_package(benchmark REQUIRED)
Alternatively, add_subdirectory will incorporate the library directly in to one's CMake project.
add_subdirectory(benchmark)
Either way, link to the library as follows.
target_link_libraries(MyTarget benchmark::benchmark)
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 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.
The shlwapi library (-lshlwapi) is required to support a call to CPUInfo which reads the registry. Either add shlwapi.lib under [ Configuration Properties > Linker > Input ], or use the following:
// Alternatively, can add libraries using linker options. #ifdef _WIN32 #pragma comment ( lib, "Shlwapi.lib" ) #ifdef _DEBUG #pragma comment ( lib, "benchmarkd.lib" ) #else #pragma comment ( lib, "benchmark.lib" ) #endif #endif
Can also use the graphical version of CMake:
CMake GUI.Where to build the binaries, same path as source plus build.CMAKE_INSTALL_PREFIX, same path as source plus install.Configure, Generate, Open Project.See instructions for building with Visual Studio. Once built, right click on the solution and change the build to Intel.
If you‘re running benchmarks on solaris, you’ll want the kstat library linked in too (-lkstat).
Running a Subset of Benchmarks
Runtime and Reporting Considerations
Calculating Asymptotic Complexity
User-Requested Performance Counters
Disabling CPU Frequency Scaling
The library supports multiple output formats. Use the --benchmark_format=<console|json|csv> flag (or set the BENCHMARK_FORMAT=<console|json|csv> environment variable) 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:
{ "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,
Write benchmark results to a file with the --benchmark_out=<filename> option (or set BENCHMARK_OUT). Specify the output format with --benchmark_out_format={json|console|csv} (or set BENCHMARK_OUT_FORMAT={json|console|csv}). Note that the ‘csv’ reporter is deprecated and the saved .csv file is not parsable by csv parsers.
Specifying --benchmark_out does not suppress the console output.
Benchmarks are executed by running the produced binaries. Benchmarks binaries, by default, accept options that may be specified either through their command line interface or by setting environment variables before execution. For every --option_flag=<value> CLI switch, a corresponding environment variable OPTION_FLAG=<value> exist and is used as default if set (CLI switches always prevails). A complete list of CLI options is available running benchmarks with the --help switch.
The --benchmark_filter=<regex> option (or BENCHMARK_FILTER=<regex> environment variable) can be used to only run the benchmarks that match the specified <regex>. For example:
$ ./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
It is possible to compare the benchmarking results. See Additional Tooling Documentation
Sometimes it's useful to add extra context to the content printed before the results. By default this section includes information about the CPU on which the benchmarks are running. If you do want to add more context, you can use the benchmark_context command line flag:
$ ./run_benchmarks --benchmark_context=pwd=`pwd` Run on (1 x 2300 MHz CPU) pwd: /home/user/benchmark/ Benchmark Time CPU Iterations ---------------------------------------------------- BM_memcpy/32 11 ns 11 ns 79545455 BM_memcpy/32k 2181 ns 2185 ns 324074
You can get the same effect with the API:
benchmark::AddCustomContext("foo", "bar");
Note that attempts to add a second value with the same key will fail with an error message.
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.
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:
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.
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.
BENCHMARK(BM_memcpy)->RangeMultiplier(2)->Range(8, 8<<10);
Now arguments generated are [ 8, 16, 32, 64, 128, 256, 512, 1024, 2k, 4k, 8k ].
The preceding code shows a method of defining a sparse range. The following example shows a method of defining a dense range. It is then used to benchmark the performance of std::vector initialization for uniformly increasing sizes.
static void BM_DenseRange(benchmark::State& state) { for(auto _ : state) { std::vector<int> v(state.range(0), state.range(0)); benchmark::DoNotOptimize(v.data()); benchmark::ClobberMemory(); } } BENCHMARK(BM_DenseRange)->DenseRange(0, 1024, 128);
Now arguments generated are [ 0, 128, 256, 384, 512, 640, 768, 896, 1024 ].
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.
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.
BENCHMARK(BM_SetInsert)->Ranges({{1<<10, 8<<10}, {128, 512}});
Some benchmarks may require specific argument values that cannot be expressed with Ranges. In this case, ArgsProduct offers the ability to generate a benchmark input for each combination in the product of the supplied vectors.
BENCHMARK(BM_SetInsert) ->ArgsProduct({{1<<10, 3<<10, 8<<10}, {20, 40, 60, 80}}) // would generate the same benchmark arguments as BENCHMARK(BM_SetInsert) ->Args({1<<10, 20}) ->Args({3<<10, 20}) ->Args({8<<10, 20}) ->Args({3<<10, 40}) ->Args({8<<10, 40}) ->Args({1<<10, 40}) ->Args({1<<10, 60}) ->Args({3<<10, 60}) ->Args({8<<10, 60}) ->Args({1<<10, 80}) ->Args({3<<10, 80}) ->Args({8<<10, 80});
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.
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);
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.
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.
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.
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.
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.
BENCHMARK(BM_StringCompare)->RangeMultiplier(2) ->Range(1<<10, 1<<18)->Complexity([](benchmark::IterationCount n)->double{return n; });
You can change the benchmark's name as follows:
BENCHMARK(BM_memcpy)->Name("memcpy")->RangeMultiplier(2)->Range(8, 8<<10);
The invocation will execute the benchmark as before using BM_memcpy but changes the prefix in the report to memcpy.
This example produces and consumes messages of size sizeof(v) range_x times. It also outputs throughput in the absence of multiprogramming.
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.
#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)
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:
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 */
Also you can create templated fixture by using the following macros:
BENCHMARK_TEMPLATE_F(ClassName, Method, ...)BENCHMARK_TEMPLATE_DEFINE_F(ClassName, Method, ...)For example:
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);
You can add your own counters with user-defined names. The example below will add columns “Foo”, “Bar” and “Baz” in its output:
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/or finally inverting the result; and a flag specifying the ‘unit’ - i.e. is 1k a 1000 (default, benchmark::Counter::OneK::kIs1000), or 1024 (benchmark::Counter::OneK::kIs1024)?
// 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. // Meaning: per one second, how many 'foo's are processed? state.counters["FooRate"] = Counter(numFoos, benchmark::Counter::kIsRate); // Set the counter as a rate. It will be presented divided // by the duration of the benchmark, and the result inverted. // Meaning: how many seconds it takes to process one 'foo'? state.counters["FooInvRate"] = Counter(numFoos, benchmark::Counter::kIsRate | benchmark::Counter::kInvert); // 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:
// 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;
When using the console reporter, by default, user counters 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.
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:
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:
BENCHMARK(BM_test)->Range(8, 8<<10)->UseRealTime();
Without UseRealTime, CPU time is used by default.
By default, the CPU timer only measures the time spent by the main thread. If the benchmark itself uses threads internally, this measurement may not be what you are looking for. Instead, there is a way to measure the total CPU usage of the process, by all the threads.
void callee(int i); static void MyMain(int size) { #pragma omp parallel for for(int i = 0; i < size; i++) callee(i); } static void BM_OpenMP(benchmark::State& state) { for (auto _ : state) MyMain(state.range(0)); } // Measure the time spent by the main thread, use it to decide for how long to // run the benchmark loop. Depending on the internal implementation detail may // measure to anywhere from near-zero (the overhead spent before/after work // handoff to worker thread[s]) to the whole single-thread time. BENCHMARK(BM_OpenMP)->Range(8, 8<<10); // Measure the user-visible time, the wall clock (literally, the time that // has passed on the clock on the wall), use it to decide for how long to // run the benchmark loop. This will always be meaningful, an will match the // time spent by the main thread in single-threaded case, in general decreasing // with the number of internal threads doing the work. BENCHMARK(BM_OpenMP)->Range(8, 8<<10)->UseRealTime(); // Measure the total CPU consumption, use it to decide for how long to // run the benchmark loop. This will always measure to no less than the // time spent by the main thread in single-threaded case. BENCHMARK(BM_OpenMP)->Range(8, 8<<10)->MeasureProcessCPUTime(); // A mixture of the last two. Measure the total CPU consumption, but use the // wall clock to decide for how long to run the benchmark loop. BENCHMARK(BM_OpenMP)->Range(8, 8<<10)->MeasureProcessCPUTime()->UseRealTime();
Normally, the entire duration of the work loop (for (auto _ : state) {}) is measured. But sometimes, it is necessary to do some work inside of that loop, every iteration, but without counting that time to the benchmark time. That is possible, although it is not recommended, since it has high overhead.
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}});
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.
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();
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:
BENCHMARK(BM_test)->Unit(benchmark::kMillisecond);
To prevent a value or expression from being optimized away by the compiler the benchmark::DoNotOptimize(...) and benchmark::ClobberMemory() functions can be used.
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:
/* 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.
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.
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.
While having mean, median and standard deviation is nice, this may not be enough for everyone. For example you may want to know what the largest observation is, 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.
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);
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:
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(); benchmark::Shutdown(); }
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. Moreover, if SkipWithError(...) has been used, it is not required to reach the benchmark loop and one may return from the benchmark function early.
For example:
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. } while (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) { auto resource = GetResource(); if (!resource.good()) { state.SkipWithError("Resource is not good!"); return; // Early return is allowed when SkipWithError() has been used. } for (auto _ : state) { auto data = resource.read_data(); if (!resource.good()) { state.SkipWithError("Failed to read data!"); break; // REQUIRED to prevent all further iterations. } do_stuff(data); } }
In C++11 mode, a ranged-based for loop should be used in preference to the KeepRunning loop for running the benchmarks. For example:
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:
# 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:
.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.
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:
sudo cpupower frequency-set --governor performance ./mybench sudo cpupower frequency-set --governor powersave