Generating Optimal Code Using Answer Set Programming

This paper presents the Total Optimisation using Answer Set Technology (TOAST) system, which can be used to generate optimal code sequences for machine architectures via a technique known as superoptimisation. Answer set programming (ASP) is utilised as the modelling and computational framework for searching over the large, complex search spaces and for proving the functional equivalence of two code sequences. Experimental results are given showing the progress made in solver performance over the previous few years, along with an outline of future developments to the system and applications within compiler toolchains

Are There Good Mistakes? A Theoretical Analysis of CEGIS

Counterexample-guided inductive synthesis CEGIS is used to synthesize programs from a candidate space of programs. The technique is guaranteed to terminate and synthesize the correct program if the space of candidate programs is finite. But the technique may or may not terminate with the correct program if the candidate space of programs is infinite. In this paper, we perform a theoretical analysis of counterexample-guided inductive synthesis technique. We investigate whether the set of candidate spaces for which the correct program can be synthesized using CEGIS depends on the counterexamples used in inductive synthesis, that is, whether there are good mistakes which would increase the synthesis power. We investigate whether the use of minimal counterexamples instead of arbitrary counterexamples expands the set of candidate spaces of programs for which inductive synthesis can successfully synthesize a correct program. We consider two kinds of counterexamples: minimal counterexamples and history bounded counterexamples. The history bounded counterexample used in any iteration of CEGIS is bounded by the examples used in previous iterations of inductive synthesis. We examine the relative change in power of inductive synthesis in both cases. We show that the synthesis technique using minimal counterexamples MinCEGIS has the same synthesis power as CEGIS but the synthesis technique using history bounded counterexamples HCEGIS has different power than that of CEGIS, but none dominates the other.Comment: In Proceedings SYNT 2014, arXiv:1407.493

Populating the Peephole Optimizer of a Smart Contract Compiler

Developing compiler optimizations, especially for new, rapidly evolving smart contract languages, can be onerous and error-prone, but is especially important for smart contracts, where deployment and execution directly translate to monetary cost and which cannot change once deployed. One common optimization technique is the use of peephole optimizations, replacement rules that are applied using pattern-matching. These rules are normally constructed using human expertise, which is both time-consuming and far from systematic in exploring opportunities for optimization. In this work we propose a pipeline to automatically populate the peephole optimizer of a smart contract compiler. We apply superoptimization to an existing code base to obtain sequences of instructions, which can be replaced by cheaper, observationally equivalent instructions. We then generate peephole optimization rules by extracting the underlying patterns of these optimizations. We provide a case study of our approach and a prototype implementation for bytecode of the Ethereum Virtual Machine, the tool ppltr, which combines the superoptimizer ebso and the rule generator sorg. Then we evaluate our approach by generating and applying nearly 1k peephole optimization rules extracted from 2k optimizations obtained from deployed bytecode

Quanto: optimizing quantum circuits with automatic generation of circuit identities

Existing quantum compilers focus on mapping a logical quantum circuit to a quantum device and its native quantum gates. Only simple circuit identities are used to optimize the quantum circuit during the compilation process. This approach misses more complex circuit identities, which could be used to optimize the quantum circuit further. We propose Quanto, the first quantum optimizer that automatically generates circuit identities. Quanto takes as input a gate set and generates provably correct circuit identities for the gate set. Quanto’s automatic generation of circuit identities includes single-qubit and two-qubit gates, which leads to a new database of circuit identities, some of which are novel to the best of our knowledge. In addition to the generation of new circuit identities, Quanto’s optimizer applies such circuit identities to quantum circuits and finds optimized quantum circuits that have not been discovered by other quantum compilers, including IBM Qiskit and Cambridge Quantum Computing Tket. Quanto’s database of circuit identities could be applied to improve existing quantum compilers and Quanto can be used to generate identity databases for new gate sets