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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.(More)
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(More)
The paper gives an overview of region-based memory management. The emphasis of the paper is on the dynamic aspects of execution, specifically memory management. We illustrate how three static program analyses can be used for inferring memory management directives. They are <i>region inference, physical size inference</i>, and <i>storage mode analysis</i>.(More)