Given a loop-free sequence of instructions, superoptimization techniques use a constraint solver to search for an equivalent sequence that is optimal for a desired objective. The complexity of the search grows exponentially with the length of the solution being constructed and the problem becomes intractable for large sequences of instructions. This paper presents a new approach to superoptimizing stack-bytecode via three novel components: (1) a greedy algorithm to refine the bound on the length of the optimal solution; (2) a new representation of the optimization problem as a set of weighted soft clauses in MaxSAT; (3) a series of domain-specific dominance and redundant constraints to reduce the search space for optimal solutions. We have developed a tool, named S uper S tack , which can be used to find optimal code translations of modern stack-based bytecode, namely WebAssembly or Ethereum bytecode. Experimental evaluation on more than 500,000 sequences shows the proposed greedy, constraint-based and SAT combination is able to greatly increase optimization gains achieved by existing superoptimizers and reduce to at least a fourth the optimization time.

@article{AlbertBHIRS24,
  title = {SuperStack: Superoptimization of Stack-Bytecode via Greedy, Constraint-Based, and SAT Techniques},
  author = {Elvira Albert and Maria Garcia de la Banda and Alejandro Hernández-Cerezo and Alexey Ignatiev and Albert Rubio and Peter J. Stuckey},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {8},
  number = {PLDI},
  year = {2024},
  doi = {10.1145/3656435}
}