From region inference to von Neumann machines via region representation inference

Abstract

Region Inference is a technique for implementing programming languages that are based on typed call-by-value lambda calculus, such as Standard ML. The mathematical runtime model of region inference uses a stack of regions, each of which can contain an unbounded number of values. This paper is concerned with mapping the mathematical model onto real machines. This is done by composing region inference with Region Representation Inference, which gradually refines region information till it is directly implementable on conventional von Neumann machines. The performance of a new region-based ML compiler is compared to the performance of Standard ML of New Jersey, a state-of-the-art ML compiler.