  We engineer a new probabilistic Monte-Carlo algorithm for isomorphism
testing. Most notably, as opposed to all other solvers, it implicitly exploits
the presence of symmetries without explicitly computing them.
  We provide extensive benchmarks, showing that the algorithm outperforms all
state-of-the-art solutions for isomorphism testing on most inputs from the de
facto standard benchmark library for isomorphism testing. On many input types,
our data not only show improved running times by an order of magnitude, but
also reflect a better asymptotic behavior.
  Our results demonstrate that, with current algorithms, isomorphism testing is
in practice easier than the related problems of computing the automorphism
group or canonically labeling a graph. The results also show that probabilistic
algorithms for isomorphism testing can be engineered to outperform
deterministic approaches, even asymptotically.
