Empirical analysis of a LISP system
Empirical analysis of a LISP system
Tenuring policies for generation-based storage reclamation
OOPSLA '88 Conference proceedings on Object-oriented programming systems, languages and applications
A real-time garbage collector based on the lifetimes of objects
Communications of the ACM
Generation Scavenging: A non-disruptive high performance storage reclamation algorithm
SDE 1 Proceedings of the first ACM SIGSOFT/SIGPLAN software engineering symposium on Practical software development environments
Demonic memory for process histories
PLDI '89 Proceedings of the ACM SIGPLAN 1989 Conference on Programming language design and implementation
Design of the opportunistic garbage collector
OOPSLA '89 Conference proceedings on Object-oriented programming systems, languages and applications
Using key object opportunism to collect old objects
OOPSLA '91 Conference proceedings on Object-oriented programming systems, languages, and applications
Some issues and strategies in heap management and memory hierarchies
ACM SIGPLAN Notices
Using lifetime predictors to improve memory allocation performance
PLDI '93 Proceedings of the ACM SIGPLAN 1993 conference on Programming language design and implementation
Allocation-phase aware thread scheduling policies to improve garbage collection performance
Proceedings of the 6th international symposium on Memory management
Microphase: an approach to proactively invoking garbage collection for improved performance
Proceedings of the 22nd annual ACM SIGPLAN conference on Object-oriented programming systems and applications
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Opportunistic garbage collection is a non-incremental generation-based garbage collection system. It attempts to minimize the probability of disruptive pauses by careful scheduling of scavenges. Scavenge pauses are hidden in pauses created by the running program or by the user; they are also shortened by scheduling them at low points in the stack height, where live data tend to be at a minimum. These heuristics can be surprisingly simple and cheap to implement --- user input primitives provide an effective hook from which to invoke the scheduling routine, since they tend to correspond both to local stack minima and to computational pause boundaries.An additional mechanism is proposed to detect times when it is safe to scavenge an intermediate generation, based on the amount of data surviving from a new-generation scavenge. This mechanism can be used reliably in certain cases, or heuristically in a larger class of cases.