Fast and flexible instruction selection with on-demand tree-parsing automata

Abstract

Tree parsing as supported by code generator generators like BEG, burg, iburg, lburg and ml-burg is a popular instruction selection method. There are two existing approaches for implementing tree parsing: dynamic programming, and tree-parsing automata; each approach has its advantages and disadvantages. We propose a new implementation approach that combines the advantages of both existing approaches: we start out with dynamic programming at compile time, but at every step we generate a state for a tree-parsing automaton, which is used the next time a tree matching the state is found, turning the instruction selector into a fast tree-parsing automaton. We have implemented this approach in the Gforth code generator. The implementation required little effort and reduced the startup time of Gforth by up to a factor of 2.5.