Combining region inference and garbage collection

Abstract

This paper describes a memory discipline that combines region-based memory management and copying garbage collection by extending Cheney's copying garbage collection algorithm to work with regions. The paper presents empirical evidence that region inference very significantly reduces the number of garbage collections; and evidence that the fastest execution is obtained by using regions alone, without garbage collection. The memory discipline is implemented for Standard ML in the ML Kit compiler and measurements show that for a variety of benchmark programs, code generated by the compiler is as efficient, both with respect to execution time and memory usage, as programs compiled with Standard ML of New Jersey, another state-of-the-art Standard ML compiler.