Better Than Optimal: Fast Identification of Custom Instruction Candidates

Asymptotically optimal algorithms do not always yield the fastest practical algorithm on realistic cases. We examine Gutin etal.´s recently published optimal algorithm for enumerating the set of convex subgraphs under input/output constraints with application to custom instruction identification. We show that (i) suppressing some of the machinery in their algorithm results in a sub-optimal algorithm which is significantly faster in practice on real-world examples and that (ii) the constants of proportionality in the running time for both optimal and sub-optimal versions can be significantly improved by using additional output set filtering constraints.