Causal profiling

[jrmadsen]

• Adds causal profiling capability

Causal Profiling

If you speed up a given block of code by X%, the application will execute Y% faster

Causal profiling directs parallel application developers to where they should focus their optimization efforts by quantifying the potential impact of optimizations. Causal profiling is rooted in the concept that software execution speed is relative: speeding up a block of code by X% is mathematically equivalent to that block of code running at its current speed if all the other code running slower by X%. Thus, causal profiling works by performing experiments on blocks of code during program execution which insert pauses to slow down all other concurrently running code. During post-processing, these experiments are translated into calculations for the potential impact of speeding up this block of code.

Consider the following C++ code executing foo and bar concurrently in two different threads where foo is 30% faster than bar (ideally):

constexpr size_t FOO_N =  7 * 1000000000UL;
constexpr size_t BAR_N = 10 * 1000000000UL;

void foo()
{
    for(volatile size_t i = 0; i < FOO_N; ++i) {}
}

void bar()
{
    for(volatile size_t i = 0; i < BAR_N; ++i) {}
}

int main()
{
    auto _threads = { std::thread{ foo },
                      std::thread{ bar } };

    for(auto& itr : _threads)
        itr.join();
}

No matter how many optimizations are applied to foo, the application will always require the same amount of time because the end-to-end performance is limited by bar. However, a 5% speedup in bar will result in the end-to-end performance improving by 5% and this trend will continue linearly (10% speedup in bar yields 10% speedup in end-to-end performance, and so on) up to 30% speedup, at which point, bar executes as fast as foo; any speedup to bar beyond 30% will still only yield an end-to-end performance speedup of 30% since the application will be limited by performance of foo, as demonstrated below in the causal profiling visualization:

The full details of the causal profiling methodology can be found in the paper Coz: Finding Code that Counts with Causal Profiling.