Monte Carlo Benchmarking

23 February 2015

SNAPL rejected my crazy abstract, so I’m sharing my craziness with the world instead.

I submitted both a paper and an abstract to SNAPL, a “new kind of PL conference, focused on big-picture questions rather than concrete technical results”. I’m pretty excited that the paper Adrian Sampson and I wrote with our advisor Luis Ceze was accepted, along with two other papers from the kick-ass UW PLSE group. But my one-page abstract was just too crazy for SNAPL. Bruised but not defeated, I present my completely insane abstract about random benchmarking.

Monte Carlo Benchmarking

We all like decisive claims in our papers: “Mipso1 speeds up programs by 2×”. Strong claims put stakes in the ground for our readers: if you do what we say, these are the results you’ll see. Performance measurement is similar to opinion polling: journalists would like to say “Obama will [win/lose] the election”, but evidence from opinion polls is not so definitive. To generalise from opinion poll to conclusion, we have to consider both how the question was asked, and to whom. The same is true in performance measurement.

For computer systems, the how means measuring the right thing in the right way, and is comparatively well studied. There is extensive work on measuring systems without bias. For example, Curtsinger and Berger’s Stabilizer controls variables such as code and data layout, using randomisation to mitigate alignment and cache biases. Work continues to encourage more researchers to embrace sound methodology, but we are making progress.

Less well understood is of whom we ask questions. For opinion polling the challenge is clear – a sample of 100 young males from San Francisco does not generalise to the entire voting population. For computer systems, the whom is the set of programs we measure. Today, these programs are usually a benchmark suite.

Benchmark Suites

Benchmark suites give a common basis for evaluating our work, and for comparing our work to prior results. But benchmark suites do not help defend a claim that our work “speeds up programs by 2×”. The problem is that the space of programs is very large, and we do not know whether the set of benchmark programs fairly reflects the diverse possibilities. In opinion polling, this would be like lacking demographic data; for example, if a random sample of the US population was 70% Californian, we wouldn’t know if that was a fair reflection of the US. We are making strides in broadening the diversity of benchmark programs.

I argue that we should go to the next level and generate random benchmarks. Random benchmarks best reflect the space of all possible programs, and so provide the right evidence (by the law of large numbers) for strong claims about overall speedups. Generated the right way, random benchmarks also enable more nuanced and intuitive evaluations by reweighting the benchmarks.

Random Benchmarks as Synthesis

“Random benchmarks” seem a little crazy, because the space of programs is so large, but work on program synthesis should give us hope.

Stochastic superoptimisation optimises segments of code by performing stochastic search over all programs up to a fixed length. The search algorithm is MCMC – it samples random programs, guided by a cost function that encodes each program’s correctness (distance to the target function being optimised) and performance.

We can generate benchmarks with stochastic superoptimisation to estimate expected speedup for an arbitrary program. Let X be a random variable for the speedup our fictional Mipso system creates. We can approximate the expected speedup EP[X] over the space P of all programs by Monte Carlo integration. Stochastic superoptimisation draws random samples from P that we need, with a simple uniform cost function. For each sampled program p we measure its speedup xp. By the law of large numbers, the sample average s = Σ xp / N$ estimates EP[X], with the accuracy improving as N → ∞. The central limit theorem gives confidence intervals for the estimate. Random benchmarks make strong claims about the average over all programs, whereas benchmark suites only allow claims about the average over the suite itself.

The Right Level of Randomness

We must choose the target language whose components we will generate randomly. Stochastic superoptimisation randomises individual x86 instructions in short programs (≈ 50). This scope may not expose interesting macro behaviour. We could work at higher levels, as with compiler fuzzers, or language models learned from real code.

At the other extreme, we could randomise over programs: randomly download programs from GitHub to use as benchmarks.

Weighted Randomisation

Stochastic superoptimisation with a uniform cost function is naive. If we have a program P, and another P’ which is P with a no-op appended, should P and P’ get equal weight when measuring performance? The cost function should use heuristics to avoid similar programs (this is the opposite of stochastic superoptimisation, which gives higher weight to closer programs).

In fact, the cost function is a powerful and underexplored benchmarking abstraction. Should we weight programs by how many people use them? A 5% speedup in libc’s quicksort is more useful to the world than a 5% speedup in my CS 101 project, but a uniform cost function weights them equally.

We could monitor live application clusters to determine which segments of code are hot (like a profiler), and build random benchmarks weighted by the frequency of code segment execution.

Random Benchmarks Already Exist!

Some fields already embrace random benchmarks. For example, the SAT Competition includes a random benchmark category alongside real application benchmarks. Just as random benchmarks do not supplant application benchmarks in SAT, neither should they do so in our work. Benchmark suites expose particular interesting behaviours. For example in Java, poor performance on SPECjvm98’s mpegaudio benchmark likely indicates degraded mutator performance, since that benchmark does little to no garbage collection.


It’s worth reflecting on whether benchmark suites help us reach our research goals. Monte Carlo benchmarks are an extreme idea to re-emphasise this question. They give us the data to make strong claims that benchmark suites alone cannot defend.

  1. A fictional system I made up; apologies if it already exists!