Synthesizing an instruction selection rule library from semantic specifications

Abstract

Instruction selection is the part of a compiler that transforms intermediate representation (IR) code into machine code. Instruction selectors build on a library of hundreds if not thousands of rules. Creating and maintaining these rules is a tedious and error-prone manual process. In this paper, we present a fully automatic approach to create provably correct rule libraries from formal specifications of the instruction set architecture and the compiler IR. We use a hybrid approach that combines enumerative techniques with template-based counterexample-guided inductive synthesis (CEGIS). Thereby, we overcome several shortcomings of existing approaches, which were not able to handle complex instructions in a reasonable amount of time. In particular, we efficiently model memory operations. Our tool synthesized a large part of the integer arithmetic rules for the x86 architecture within a few days where existing techniques could not deliver a substantial rule library within weeks. Using the rule library, we generate a prototype instruction selector that produces code on par with a manually-tuned instruction selector. Furthermore, using 63012 test cases generated from the rule library, we identified 29498 rules that both Clang and GCC miss.