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Integrate nonius to provide more advanced benchmarking
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| # Authoring benchmarks | ||
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| Writing benchmarks is not easy. Catch simplifies certain aspects but you'll | ||
| always need to take care about various aspects. Understanding a few things about | ||
| the way Catch runs your code will be very helpful when writing your benchmarks. | ||
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| First off, let's go over some terminology that will be used throughout this | ||
| guide. | ||
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| - *User code*: user code is the code that the user provides to be measured. | ||
| - *Run*: one run is one execution of the user code. | ||
| - *Sample*: one sample is one data point obtained by measuring the time it takes | ||
| to perform a certain number of runs. One sample can consist of more than one | ||
| run if the clock available does not have enough resolution to accurately | ||
| measure a single run. All samples for a given benchmark execution are obtained | ||
| with the same number of runs. | ||
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| ## Execution procedure | ||
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| Now I can explain how a benchmark is executed in Catch. There are three main | ||
| steps, though the first does not need to be repeated for every benchmark. | ||
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| 1. *Environmental probe*: before any benchmarks can be executed, the clock's | ||
| resolution is estimated. A few other environmental artifacts are also estimated | ||
| at this point, like the cost of calling the clock function, but they almost | ||
| never have any impact in the results. | ||
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| 2. *Estimation*: the user code is executed a few times to obtain an estimate of | ||
| the amount of runs that should be in each sample. This also has the potential | ||
| effect of bringing relevant code and data into the caches before the actual | ||
| measurement starts. | ||
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| 3. *Measurement*: all the samples are collected sequentially by performing the | ||
| number of runs estimated in the previous step for each sample. | ||
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| This already gives us one important rule for writing benchmarks for Catch: the | ||
| benchmarks must be repeatable. The user code will be executed several times, and | ||
| the number of times it will be executed during the estimation step cannot be | ||
| known beforehand since it depends on the time it takes to execute the code. | ||
| User code that cannot be executed repeatedly will lead to bogus results or | ||
| crashes. | ||
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| ## Benchmark specification | ||
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| Benchmarks can be specified anywhere inside a Catch test case. | ||
| There is a simple and a slightly more advanced version of the `BENCHMARK` macro. | ||
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| Let's have a look how a naive Fibonacci implementation could be benchmarked: | ||
| ```c++ | ||
| std::uint64_t Fibonacci(std::uint64_t number) { | ||
| return number < 2 ? 1 : Fibonacci(number - 1) + Fibonacci(number - 2); | ||
| } | ||
| ``` | ||
| Now the most straight forward way to benchmark this function, is just adding a `BENCHMARK` macro to our test case: | ||
| ```c++ | ||
| TEST_CASE("Fibonacci") { | ||
| CHECK(Fibonacci(0) == 1); | ||
| // some more asserts.. | ||
| CHECK(Fibonacci(5) == 8); | ||
| // some more asserts.. | ||
| // now let's benchmark: | ||
| BENCHMARK("Fibonacci 20") { | ||
| return Fibonacci(20); | ||
| }; | ||
| BENCHMARK("Fibonacci 25") { | ||
| return Fibonacci(25); | ||
| }; | ||
| BENCHMARK("Fibonacci 30") { | ||
| return Fibonacci(30); | ||
| }; | ||
| BENCHMARK("Fibonacci 35") { | ||
| return Fibonacci(35); | ||
| }; | ||
| } | ||
| ``` | ||
| There's a few things to note: | ||
| - As `BENCHMARK` expands to a lambda expression it is necessary to add a semicolon after | ||
| the closing brace (as opposed to the first experimental version). | ||
| - The `return` is a handy way to avoid the compiler optimizing away the benchmark code. | ||
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| Running this already runs the benchmarks and outputs something similar to: | ||
| ``` | ||
| ------------------------------------------------------------------------------- | ||
| Fibonacci | ||
| ------------------------------------------------------------------------------- | ||
| C:\path\to\Catch2\Benchmark.tests.cpp(10) | ||
| ............................................................................... | ||
| benchmark name samples iterations estimated | ||
| mean low mean high mean | ||
| std dev low std dev high std dev | ||
| ------------------------------------------------------------------------------- | ||
| Fibonacci 20 100 416439 83.2878 ms | ||
| 2 ns 2 ns 2 ns | ||
| 0 ns 0 ns 0 ns | ||
| Fibonacci 25 100 400776 80.1552 ms | ||
| 3 ns 3 ns 3 ns | ||
| 0 ns 0 ns 0 ns | ||
| Fibonacci 30 100 396873 79.3746 ms | ||
| 17 ns 17 ns 17 ns | ||
| 0 ns 0 ns 0 ns | ||
| Fibonacci 35 100 145169 87.1014 ms | ||
| 468 ns 464 ns 473 ns | ||
| 21 ns 15 ns 34 ns | ||
| ``` | ||
|
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| ### Advanced benchmarking | ||
| The simplest use case shown above, takes no arguments and just runs the user code that needs to be measured. | ||
| However, if using the `BENCHMARK_ADVANCED` macro and adding a `Catch::Benchmark::Chronometer` argument after | ||
| the macro, some advanced features are available. The contents of the simple benchmarks are invoked once per run, | ||
| while the blocks of the advanced benchmarks are invoked exactly twice: | ||
| once during the estimation phase, and another time during the execution phase. | ||
|
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||
| ```c++ | ||
| BENCHMARK("simple"){ return long_computation(); }; | ||
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| BENCHMARK_ADVANCED("advanced")(Catch::Benchmark::Chronometer meter) { | ||
| set_up(); | ||
| meter.measure([] { return long_computation(); }); | ||
| }; | ||
| ``` | ||
| These advanced benchmarks no longer consist entirely of user code to be measured. | ||
| In these cases, the code to be measured is provided via the | ||
| `Catch::Benchmark::Chronometer::measure` member function. This allows you to set up any | ||
| kind of state that might be required for the benchmark but is not to be included | ||
| in the measurements, like making a vector of random integers to feed to a | ||
| sorting algorithm. | ||
| A single call to `Catch::Benchmark::Chronometer::measure` performs the actual measurements | ||
| by invoking the callable object passed in as many times as necessary. Anything | ||
| that needs to be done outside the measurement can be done outside the call to | ||
| `measure`. | ||
| The callable object passed in to `measure` can optionally accept an `int` | ||
| parameter. | ||
| ```c++ | ||
| meter.measure([](int i) { return long_computation(i); }); | ||
| ``` | ||
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| If it accepts an `int` parameter, the sequence number of each run will be passed | ||
| in, starting with 0. This is useful if you want to measure some mutating code, | ||
| for example. The number of runs can be known beforehand by calling | ||
| `Catch::Benchmark::Chronometer::runs`; with this one can set up a different instance to be | ||
| mutated by each run. | ||
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| ```c++ | ||
| std::vector<std::string> v(meter.runs()); | ||
| std::fill(v.begin(), v.end(), test_string()); | ||
| meter.measure([&v](int i) { in_place_escape(v[i]); }); | ||
| ``` | ||
| Note that it is not possible to simply use the same instance for different runs | ||
| and resetting it between each run since that would pollute the measurements with | ||
| the resetting code. | ||
| It is also possible to just provide an argument name to the simple `BENCHMARK` macro to get | ||
| the same semantics as providing a callable to `meter.measure` with `int` argument: | ||
| ```c++ | ||
| BENCHMARK("indexed", i){ return long_computation(i); }; | ||
| ``` | ||
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| ### Constructors and destructors | ||
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| All of these tools give you a lot mileage, but there are two things that still | ||
| need special handling: constructors and destructors. The problem is that if you | ||
| use automatic objects they get destroyed by the end of the scope, so you end up | ||
| measuring the time for construction and destruction together. And if you use | ||
| dynamic allocation instead, you end up including the time to allocate memory in | ||
| the measurements. | ||
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| To solve this conundrum, Catch provides class templates that let you manually | ||
| construct and destroy objects without dynamic allocation and in a way that lets | ||
| you measure construction and destruction separately. | ||
|
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| ```c++ | ||
| BENCHMARK_ADVANCED("construct")(Catch::Benchmark::Chronometer meter) | ||
| { | ||
| std::vector<Catch::Benchmark::storage_for<std::string>> storage(meter.runs()); | ||
| meter.measure([&](int i) { storage[i].construct("thing"); }); | ||
| }) | ||
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| BENCHMARK_ADVANCED("destroy", [](Catch::Benchmark::Chronometer meter) | ||
| { | ||
| std::vector<Catch::Benchmark::destructable_object<std::string>> storage(meter.runs()); | ||
| for(auto&& o : storage) | ||
| o.construct("thing"); | ||
| meter.measure([&](int i) { storage[i].destruct(); }); | ||
| }) | ||
| ``` | ||
| `Catch::Benchmark::storage_for<T>` objects are just pieces of raw storage suitable for `T` | ||
| objects. You can use the `Catch::Benchmark::storage_for::construct` member function to call a constructor and | ||
| create an object in that storage. So if you want to measure the time it takes | ||
| for a certain constructor to run, you can just measure the time it takes to run | ||
| this function. | ||
| When the lifetime of a `Catch::Benchmark::storage_for<T>` object ends, if an actual object was | ||
| constructed there it will be automatically destroyed, so nothing leaks. | ||
| If you want to measure a destructor, though, we need to use | ||
| `Catch::Benchmark::destructable_object<T>`. These objects are similar to | ||
| `Catch::Benchmark::storage_for<T>` in that construction of the `T` object is manual, but | ||
| it does not destroy anything automatically. Instead, you are required to call | ||
| the `Catch::Benchmark::destructable_object::destruct` member function, which is what you | ||
| can use to measure the destruction time. | ||
| ### The optimizer | ||
| Sometimes the optimizer will optimize away the very code that you want to | ||
| measure. There are several ways to use results that will prevent the optimiser | ||
| from removing them. You can use the `volatile` keyword, or you can output the | ||
| value to standard output or to a file, both of which force the program to | ||
| actually generate the value somehow. | ||
| Catch adds a third option. The values returned by any function provided as user | ||
| code are guaranteed to be evaluated and not optimised out. This means that if | ||
| your user code consists of computing a certain value, you don't need to bother | ||
| with using `volatile` or forcing output. Just `return` it from the function. | ||
| That helps with keeping the code in a natural fashion. | ||
| Here's an example: | ||
| ```c++ | ||
| // may measure nothing at all by skipping the long calculation since its | ||
| // result is not used | ||
| BENCHMARK("no return"){ long_calculation(); }; | ||
| // the result of long_calculation() is guaranteed to be computed somehow | ||
| BENCHMARK("with return"){ return long_calculation(); }; | ||
| ``` | ||
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| However, there's no other form of control over the optimizer whatsoever. It is | ||
| up to you to write a benchmark that actually measures what you want and doesn't | ||
| just measure the time to do a whole bunch of nothing. | ||
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| To sum up, there are two simple rules: whatever you would do in handwritten code | ||
| to control optimization still works in Catch; and Catch makes return values | ||
| from user code into observable effects that can't be optimized away. | ||
|
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| <i>Adapted from nonius' documentation.</i> |
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