Andrei Alexanrescu has a talk on doing this - he calls them metaparameters e.g. where a hybrid sort chooses to change algorithm.

One library I have exploits the fact that D templates are embarrassingly better than C++'s, so you can actually benchmark a template against it's parameters in a clean manner without overhead - that could be anything from a size_t parameter for a sort or a datastructure for example.

        enum cpuidRange = iota(1, 10).map!(ctfeRepeater).array;
        @TemplateBenchmark!(0, cpuidRange) 
        @FunctionBenchmark!("Measure", iota(1, 10), (_) => [1, 2, 3, 4])(meas) 
        static int sum(string asmLine)(inout int[] input)
        {
            int tmp;
            foreach (i; input)
            {
                tmp += i;
                mixin("asm { ", asmLine, ";}");
            }
            return tmp;
        }

This made-up (pointless) benchmark measures how insert a number of cpuid instructions into the loop of a summing function affects it's runtime. My library writes the code from your specification as above to generate the instantiations and loop to measure the performance. As you might guess, the answer is a lot (CPUID is slow and serializing).

edit: https://github.com/maxhaton/chimpfella - I haven't bothered to add pmc support yet