This paper presents the design and implementation of a "quasi real-time" garbage collector for Concurrent Caml Light, an implementation of ML with threads. This two-generation system combines a fast, asynchronous copying collector on the young generation with a nondisruptive concurrent marking collector on the old generation. This design crucially relies on the ML compiletime distinction between mutable and immutable objects. 1 Introduction This paper presents the design and implementation of a garbage collector for Concurrent Caml Light, an implementation of the ML language that provides multiple threads of control executing concurrently in a shared address space. Garbage collection --- the automatic reclamation of unused memory space --- is one of the most problematic components of run-time systems for multi-threaded languages. The naive "stop-the-world" approach, where all threads synchronously stop executing the user's program to perform garbage collection, is clearly inadequate,...

This article investigates an ML-like language with byname semantics for polymorphism: polymorphic objects are not evaluated once for all at generalization time, but re-evaluated at each specialization. Unlike the standard ML semantics, the by-name semantics works well with polymorphic references and polymorphic continuations: the naive typing rules for references and for continuations are sound with respect to this semantics. Polymorphism by name leads to a better integration of these imperative features into the ML type discipline. Practical experience shows that it retains most of the eciency and predictability of polymorphism by value.

This article investigates an ML-like language with byname semantics for polymorphism: polymorphic objects are not evaluated once for all at generalization time, but re-evaluated at each specialization. Unlike the standard ML semantics, the by-name semantics works well with polymorphic references and polymorphic continuations: the naive typing rules for references and for continuations are sound with respect to this semantics. Polymorphism by name leads to a better integration of these imperative features into the ML type discipline. Practical experience shows that it retains most of the eciency and predictability of polymorphism by value.