Numerous optimizations exist for improving the performance of nondeferred reference-counting (RC) garbage collection. Their designs are ad hoc, intended to exploit different count removal opportunities. This paper shows that many of these optimizations can be unified using a notion called overlooking roots. The paper also shows how the notion enables more powerful versions of past optimizations and makes new optimizations possible. While recent static analyses have dramatically improved nondeferred RC performance, margins relative to the deferred variant were still significant in the worst case. With the optimizations made possible by overlooking roots, we show that these margins can be reduced to within 4 % on nearly all programs in a test suite, at even large heap sizes, and to within 23 % in the worst case.

This paper introduces a new abstraction to accelerate the readbarriers and write-barriers used by language runtime systems. We exploit the fact that, dynamically, many barrier executions perform checks but no real work—e.g., in generational garbage collection (GC), frequent checks are needed to detect the creation of intergenerational references, even though such references occur rarely in many workloads. We introduce a form of dynamic filtering that identifies redundant checks by (i) recording checks that have recently been executed, and (ii) detecting when a barrier is repeating one of these checks. We show how this technique can be applied to a variety of algorithms for GC, transactional memory, and language-based security. By supporting dynamic filtering in the instruction set, we show that the fast-paths of these barriers can be streamlined, reducing the impact on the quality of surrounding code. We show how we accelerate the barriers used for generational GC and transactional memory in the Bartok research compiler. With a 2048-entry filter, dynamic filtering eliminates almost all the overhead of the GC write-barriers. Dynamic filtering eliminates around half the overhead of STM over a non-synchronized baseline—even when used with an STM that is already designed for low overhead, and which employs static analyses to avoid redundant operations.

Abstract. We examine the role of transactional memory from two perspectives: that of a programming language with atomic actions and that of implementations of the language. We argue that it is difficult to formulate a clean, separate, and generally useful definition of transactional memory. In both programming-language semantics and implementations, the treatment of atomic actions benefits from being combined with that of other language features. In this respect (as in many others), transactional memory is analogous to garbage collection, which is often coupled with other parts of language runtime systems. 1

Reference counting is a well-known technique for automatic memory management, offering unique advantages over other forms of garbage collection. However, on account of the high costs associated with the maintenance of up-to-date tallies of references from the stack, deferred variants are typically used in modern implementations. This partially sacrifices some of the benefits of nondeferred reference-counting (RC) garbage collection, like the immediate reclamation of garbage and short collector pause times. This paper presents a series of optimizations that target the stack and substantially enhance the throughput of nondeferred RC collection. A key enabler is a new static analysis and optimization called RC subsumption that significantly reduces the overhead of maintaining the stack contribution to reference counts. We report execution time improvements on a benchmark suite of ten C # programs, and show how RC subsumption, aided with other optimizations, improves the performance of nondeferred RC collection by as much as a factor of 10, making possible running times that are within 32 % of that with an advanced traversal-based collector on seven programs, and 19 % of that with a deferred RC collector on eight programs. This is in the context of a baseline RC implementation that is typically at least a factor of 6 slower than the tracing collector and a factor of 5 slower than the deferred RC collector.