Code benchmarking in Haskell – some thoughts about random data generation

In my last post I showed you how to use criterion library to write benchmarks for Haskell code. In tutorial project that I created to demonstrate my ideas I decided to generate random data for benchmarking. Bryan O’Sullivan has commented on my approach that “the code (…) that generates random inputs on every run would be a good antipattern for performance testing.” After giving some thought to his words I think I see his point.

The code that Bryan refers to looks like this:

main :: IO ()
main = newStdGen >>= defaultMainWith benchConfig (return ()) . benchmarks
 
benchmarks :: RandomGen g => g -> [Benchmark]
benchmarks = ...

Each time a benchmark suite is run a different random numbers generator is created with newStdGen. This generator is then used by the benchmarks function to create values used for benchmarking. When I designed this I made an assumption that values of the data don’t influence the flow of computations. I believe that this holds for the shift functions I benchmarked in my tutorial. It doesn’t really matter what values are in the shifted list. As long as lists have the same length on different runs of the benchmark the results are comparable, but if you want to have the same random values generated on each run you can create a StdGen based on a seed that you supply. The modified main function would look like this:

main = return (mkStdGen 123456) >>= defaultMainWith benchConfig (return ()) . benchmarks

What happens however when data values do influence the flow of computation? In that case you definitely don’t want newStdGen, as it would make results of benchmark incomparable between different runs: you wouldn’t know if the speed-up is caused by changes in the code or data. It is also very likely that you don’t want to use mkStdGen. Why? Well, you would certainly get results comparable between different runs. The problem is that you wouldn’t know the characteristics of the data used for this particular benchmark. For example let’s assume that your algorithm executes faster when the data it processes contains many zeros. You benchmark the algorithm with random values created with a fixed StdGen and get a very good result. But how many zeros were in the data used for benchmarking? Perhaps 50% of input were zeros? You don’t know that. In this case you definitely want to prepare your own input data sets (e.g. one with many zeros and one without any) to measure the performance of your code based on input it receives. I guess Bryan is right here – careless use of random data generation for benchmarking can be a shot in the foot.

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