picobench is a tiny (micro) microbenchmarking library in a single header file.
It's designed to be easy to use and integrate and fast to compile while covering the most common features of a microbenchmarking library.
Here's the complete code of a microbenchmark which compares adding elements to a std::vector with and without using reserve:
#define PICOBENCH_IMPLEMENT_WITH_MAIN
#include "picobench/picobench.hpp"
#include <vector>
#include <cstdlib> // for rand
// Benchmarking function written by the user:
static void rand_vector(picobench::state& s)
{
std::vector<int> v;
for (auto _ : s)
{
v.push_back(rand());
}
}
PICOBENCH(rand_vector); // Register the above function with picobench
// Another benchmarking function:
static void rand_vector_reserve(picobench::state& s)
{
std::vector<int> v;
v.reserve(s.iterations());
for (auto _ : s)
{
v.push_back(rand());
}
}
PICOBENCH(rand_vector_reserve);The output of this benchmark might look like this:
Name (* = baseline) | Dim | Total ms | ns/op |Baseline| Ops/second
--------------------------|--------:|----------:|--------:|-------:|----------:
rand_vector * | 8 | 0.001 | 167 | - | 5974607.9
rand_vector_reserve | 8 | 0.000 | 55 | 0.329 | 18181818.1
rand_vector * | 64 | 0.004 | 69 | - | 14343343.8
rand_vector_reserve | 64 | 0.002 | 27 | 0.400 | 35854341.7
rand_vector * | 512 | 0.017 | 33 | - | 30192239.7
rand_vector_reserve | 512 | 0.012 | 23 | 0.710 | 42496679.9
rand_vector * | 4096 | 0.181 | 44 | - | 22607850.9
rand_vector_reserve | 4096 | 0.095 | 23 | 0.527 | 42891848.9
rand_vector * | 8196 | 0.266 | 32 | - | 30868196.3
rand_vector_reserve | 8196 | 0.207 | 25 | 0.778 | 39668749.5
...which tells us that we see a noticeable performance gain when we use reserve but the effect gets less prominent for bigger numbers of elements inserted.
To use picobench, you need to include picobench.hpp by either copying it inside your project or adding this repo as a submodule to yours.
In one compilation unit (.cpp file) in the module (typically the benchmark executable) in which you use picobench, you need to define PICOBENCH_IMPLEMENT_WITH_MAIN (or PICOBENCH_IMPLEMENT if you want to write your own main function).
A benchmark is a function which you're written with the signature void (picobench::state& s). You need to register the function with the macro PICOBENCH(func_name) where the only argument is the function's name as shown in the example above.
The library will run the benchmark function several times with different numbers of iterations, to simulate different problem spaces, then collect the results in a report.
Typically a benchmark has a loop. To run the loop, use the picobench::state argument in a range-based for loop in your function. The time spent looping is measured for the benchmark. You can have initialization/deinitialization code outside of the loop and it won't be measured.
You can have multiple benchmarks in multiple files. All of them will be run when the executable starts.
Use state::iterations as shown in the example to make initialization based on how many iterations the loop will make.
If you don't want the automatic time measurement, you can use state::start_timer and state::stop_timer to manually measure it, or use the RAII class picobench::scope for semi-automatic measurement.
Here's an example of a couple of benchmarks, which does not use the range-based for loop for time measurement:
void my_func(); // Function you want to benchmark
static void benchmark_my_func(picobench::state& s)
{
s.start_timer(); // Manual start
for (int i=0; i<s.iterations(); ++i)
my_func();
s.stop_timer(); // Manual stop
}
PICOBENCH(benchmark_my_func);
void my_func2();
static void benchmark_my_func2(picobench::state& s)
{
custom_init(); // Some user-defined initialization
picobench::scope scope(s); // Constructor starts measurement. Destructor stops it
for (int i=0; i<s.iterations(); ++i)
my_func2();
}
PICOBENCH(benchmark_my_func2);If you write your own main function, you need to add the following to it in order to run the benchmarks:
picobench::runner runner;
// Optionally parse command line
runner.parse_cmd_line(argc, argv);
return runner.run();For even finer control of the run, instead of run you might call the functions explicitly:
picobench::runner runner;
// Optionally parse command line
runner.parse_cmd_line(argc, argv);
if (runner.should_run()) // Cmd line may have disabled benchmarks
{
runner.run_benchmarks();
auto report = runner.generate_report();
// Then to output the data in the report use
report.to_text(std::cout); // Default
// or
report.to_text_concise(std::cout); // No iterations breakdown
// or
report.to_csv(std::cout); // Otputs in csv format. Most detailed
}Instead of std::cout you may want to use another std::ostream instance of your choice.
As mentioned above report.to_text_concise(ostream) outputs a report without the iterations breakdown. With the first example of benchmarking adding elements to a std::vector, the output would be this:
Name (* = baseline) | ns/op | Baseline | Ops/second
--------------------------|--------:|---------:|-----------:
rand_vector * | 36 | - | 27427782.7
rand_vector_reserve | 24 | 0.667 | 40754573.7
Note that in this case the information that the effect of using reserve gets less prominent with more elements is lost.
You can optionally create suites of benchmarks. If you don't, all benchmarks in the module are assumed to be in the default suite.
To create a suite, write PICOBENCH_SUITE("suite name in quotes"); and then every benchmark below this line will be a part of this suite. You can have benchmakrs in many files in the same suite. Just use the same string for its name.
All benchmarks in a suite are assumed to be related and one of them is dubbed a "baseline". In the report at the end, all others will be compared to it.
By default the first benchmark added to a suite is the baseline, but you can change this by adding .baseline() to the registration like so: PICOBENCH(my_benchmark).baseline().
Sometimes the code being benchmarked is very sensitive to external factors such as syscalls (which include memory allocation and deallocation). Those external factors can have take greatly different times between runs. In such cases several samples of a benchmark might be needed to more precisely measure the time it takes to complete. By default the library makes two samples of each benchmark, but you can change this by adding .samples(n) to the registration like so: PICOBENCH(my_benchmark).samples(10).
Note that the time written to the report is the one of the fastest sample.
You can set a result for a benchmark using state::set_result. Here is an example of this:
void my_benchmark(picobench::state& s)
{
int sum = 0;
for (int i : s)
{
sum += myfunc(i);
}
s.set_result(sum);
}By default results are not used. You can think of them as data sinks. Optionally however you can use them in two ways.
- Compare across samples: By calling
runner::set_compare_results_across_samplesyou will make the library compare results between the different samples of a benchmark and trigger an error if they differ. - Compare across benchmarks: By calling
runner::set_compare_results_across_benchmarksyou can make a more complex comparison which will compare the results from all benchmarks in a suite. You can use this if you compare different ways of calculating the same result.
By default results are compared by simple equality, but you can introduce you own function as an argument to runner::generate_report here is an example:
void my_benchmark(picobench::state& s)
{
my_vector2 result;
for (auto _ : s)
result += my_vector_op();
s.set_result(
// new to preserve value past this function
reinterpret_cast<result_t>(new my_vector2(result)));
}
bool compare_vectors(result_t a, result_t b)
{
auto v1 = reinterpret_cast<my_vector2*>(a);
auto v2 = reinterpret_cast<my_vector2*>(b);
return v1->x == v2->x && v1->y == v2->y;
}
...
auto report = runner.generate_report(compare_vectors);
Other characteristics of a benchmark are:
- Iterations: (or "problem spaces") a vector of integers describing the set of iterations to be made for a benchmark. Set with
.iterations({i1, i2, i3...}). The default is {8, 64, 512, 4096, 8196}. - Label: a string which is used for this benchmark in the report instead of the function name. Set with
.label("my label") - User data: a user defined number (
uintptr_t) assinged to a benchmark which can be accessed bystate::user_data
You can combine the options by concatenating them like this: PICOBENCH(my_func).label("My Function").samples(2).iterations({1000, 10000, 50000});
If you write your own main function, you can set the default iterations and samples for all benchmarks with runner::set_default_state_iterations and runner::set_default_samples before calling runner::run_benchmarks.
If you parse the command line or use the library-provided main function you can also set the iterations and samples with command line args:
--iters=1000,5000,10000will set the iterations for benchmarks which don't explicitly override them--samples=5will set the samples for benchmarks which don't explicitly override them
If you're using the library-provided main function, it will also handle the following command line arguments:
--out-fmt=<txt|con|csv>- sets the output report format to either full text, concise text or csv.--output=<filename>- writes the output report to a given file--compare-results- will compare results from benchmarks and trigger an error if they don't match.
- The runner randomizes the benchmarks. To have the same order on every run and every platform, set an integer seed to
runner::run_benchmarks.
Here's another example of a custom main function incporporating the above:
#define PICOBENCH_IMPLEMENT
#include "picobench/picobench.hpp"
...
int main()
{
// User-defined code which makes global initializations
custom_global_init();
picobench::runner runner;
// Disregard command-line for simplicity
// Two sets of iterations
runner.set_default_state_iterations({10000, 50000});
// One sample per benchmark because the huge numbers are expected to compensate
// for external factors
runner.set_default_samples(1);
// Run the benchmarks with some seed which guarantees the same order every time
auto report = runner.run_benchmarks(123);
// Output to some file
report.to_csv(ofstream("my.csv"));
return 0;
}Contributions in the form of issues and pull requests are welcome.
This software is distributed under the MIT Software License.
See accompanying file LICENSE.txt or copy here.
Copyright © 2017-2024 Borislav Stanimirov
