Context: Superoptimization is a synthesis technique that, given a loop-free sequence of instructions, searches for an equivalent sequence that is optimal wrt. an objective function. Superoptimization of Ethereum smart contracts aims at minimizing the size of their bytecode and the gas consumption of executing the contract’s functions. The search for the optimal solution poses huge computational demands –as the search space to find the optimal sequence is exponential on the given size-bound – being the main challenge for superoptimization today to scale up to real, industrial software. Even if the underlying problem for finding the optimal solution is decidable, practical tools often prioritize efficiency over completeness. This means they might be implemented to find a sub-optimal solution or even time out. Objective: This work aims at leveraging superoptimization to a real setting: Ethereum blockchain. This paper proposes a neural-guided superoptimization (NGS) approach which incorporates deep neural networks using (supervised) learning into superoptimization to improve scalability by predicting: (1) if a sequence is already optimal and hence the search can be skipped; (2) the size-bound for the optimal solution in order to reduce the search space. Method: We have downloaded over 13,000 smart contracts deployed on the blockchain for training and testing the machine learning models, and a disjoint set with 100 of the smart contracts with more transactions to prove our scalability gains and impact for the Ethereum community. Results: Incorporating DNNs resulted in a 16x overall speedup (12x for gas) with only 12% optimization loss (14% for gas), or a 3-4x speedup with no optimization loss. For the 100 analyzed contracts, this approach reduced the average compilation time to 3 min per contract and achieved monetary savings of $1.24M. Conclusions: The integration of machine learning models mitigates several limitations of traditional superoptimization by drastically reducing execution times while maintaining most of the original optimization gains.

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.

Abstract Superoptimization is a compilation technique that searches for the optimal sequence of instructions semantically equivalent to a given (loop-free) initial sequence. With the advent of SMT solvers, it has been successfully applied to LLVM code (to reduce the number of instructions) and to Ethereum EVM bytecode (to reduce its gas consumption). Both applications, when proven practical, have left out memory operations and thus missed important optimization opportunities. A main challenge to superoptimization today is handling memory operations while remaining scalable. We present $$\textsf {GASOL}^{v2}$$ GASOL v 2 , a gas and bytes-size superoptimization tool for Ethereum smart contracts, that leverages a previous Max-SMT approach for only stack optimization to optimize also wrt. memory and storage. $$\textsf {GASOL}^{v2}$$ GASOL v 2 can be used to optimize the size in bytes, aligned with the optimization criterion used by the Solidity compiler , and it can also be used to optimize gas consumption. Our experiments on 12,378 blocks from 30 randomly selected real contracts achieve gains of 16.42% in gas wrt. the previous version of the optimizer without memory handling, and gains of 3.28% in bytes-size over code already optimized by .

Smart contracts are programs deployed on a blockchain. They are executed for a monetary fee paid in gas —a clear optimization target for smart contract compilers. Because smart contracts are a young, fast-moving field without (manually) fine-tuned compilers, they highly benefit from automated and adaptable approaches, especially as smart contracts are effectively immutable, and as such need a high level of assurance. This makes them an ideal domain for applying formal methods. Super-optimization is a technique to find the best translation of a block of instructions by trying all possible sequences of instructions that produce the same result. We present a framework for super-optimizing smart contracts based on Max-SMT with two main ingredients: (1) a stack functional specification extracted from the basic blocks of a smart contract, which is simplified using rules capturing the semantics of arithmetic, bit-wise, and relational operations, and (2) the synthesis of optimized blocks , which finds—by means of an efficient SMT encoding—basic blocks with minimal gas cost whose stack functional specification is equal (modulo commutativity) to the extracted one. We implemented our framework in the tool syrup 2.0 . Through large-scale experiments on real-world smart contracts, we analyze performance improvements for different SMT encodings, as well as tradeoffs between quality of optimizations and required optimization time.