This paper describes a memory management discipline for programs that perform dynamic memory allocation and de-allocation. At runtime, all values are put into regions. The store consists of a stack of regions. All points of region allocation and deallocation are inferred automatically, using a type and effect based program analysis. The scheme does not assume the presence of a garbage collector. The scheme was first presented by Tofte and Talpin (1994); subsequently, it has been tested in The ML Kit with Regions, a region-based, garbage-collection free implementation of the Standard ML Core language, which includes recursive datatypes, higher-order functions and updatable references (Birkedal et al. 96, Elsman and Hallenberg 95). This paper defines a region-based dynamic semantics for a skeletal programming language extracted from Standard ML. We present the inference system which specifies where regions can be allocated and de-allocated and a detailed proof that the system is sound wi...

We describe a system that supports source-level integration of ML-like functional language code with ANSI C or Ada83 code. The system works by translating the functional code into type-correct, "vanilla" C or Ada; it offers simple, efficient, type-safe inter-operation between new functional code components and "legacy" third-generationlanguage components. Our translator represents a novel synthesis of techniques including user-parameterized specification of primitive types and operators; removal of polymorphism by code specialization; removal of higher-order functions using closure datatypes and interpretation; and aggressive optimization of the resulting first-order code, which can be viewed as encoding the result of a closure analysis. Programs remain fully typed at every stage of the translation process, using only simple, standard type systems. Target code runs at speeds comparable to the output of current optimizing ML compilers, even though handicapped by a conservative garbage collector.

We present a translation scheme for the polymorphically typed call-by-value λ-calculus. All runtime values, including function closures, are put into regions. The store consists of a stack of regions. Region inference and effect inference are used to infer where regions can be allocated and de-allocated. Recursive functions are handled using a limited form of polymorphic recursion. The translation is proved correct with respect to a store semantics, which models a regionbased run-time system. Experimental results suggest that regions tend to be small, that region allocation is frequent and that overall memory demands are usually modest, even without garbage collection. 1

We present a stack-allocation scheme for the call-by-value lambda calculus typed according to Milner's polymorphic type discipline. All the values a program produces, including function closures, are put into regions at runtime. Regions are allocated and deallocated in a stack-like manner. Region inference and effect inference is used to infer where regions can be allocated and deallocated. By allowing a limited form of polymorphic recursion in region types, the region inference is able to distinguish between the life-times of different invocations of a function, even when the function is recursive. The region analysis is eager in its attempt to reclaim memory as quickly as possible. The main result of this report is that region inference is safe, a result which entails that regions really can be deallocated, when region inference says they can. We give detailed proofs of this claim, which (in various forms) has been advanced several times in the literature. An algorithm for implementi...