Despite some clear advantages and recent advances, reference counting remains a poor cousin to high-performance tracing garbage collectors. The advantages of reference counting include a) immediacy of reclamation, b) incrementality, and c) local scope of its operations. After decades of languishing with hopelessly bad performance, recent work narrowed the gap between reference counting and the fastest tracing collectors to within 10%. Though a major advance, this gap remains a substantial barrier to adoption in performance-conscious application domains.Our work identifies heap organization as the principal source of the remaining performance gap. We present the design, implementation, and analysis of a new collector, RC Immix, that replaces reference counting's traditional free-list heap organization with the line and block heap structure introduced by the Immix collector. The key innovations of RC Immix are 1) to combine traditional reference counts with per-line live object counts to identify reusable memory and 2) to eliminate fragmentation by integrating copying with reference counting of new objects and with backup tracing cycle collection. In RC Immix, reference counting offers efficient collection and the line and block heap organization delivers excellent mutator locality and efficient allocation. With these advances, RC Immix closes the 10% performance gap, outperforming a highly tuned production generational collector. By removing the performance barrier, this work transforms reference counting into a serious alternative for meeting high performance objectives for garbage collected languages.

Reference counting and tracing are the two fundamental approaches that have underpinned garbage collection since 1960. However, despite some compelling advantages, reference counting is almost completely ignored in implementations of high performance systems today. In this paper we take a detailed look at reference counting to understand its behavior and to improve its performance. We identify key design choices for reference counting and analyze how the behavior of a wide range of benchmarks might affect design decisions. As far as we are aware, this is the first such quantitative study of reference counting. We use insights gleaned from this analysis to introduce a number of optimizations that significantly improve the performance of reference counting.We find that an existing modern implementation of reference counting has an average 30% overhead compared to tracing, and that in combination, our optimizations are able to completely eliminate that overhead. This brings the performance of reference counting on par with that of a well tuned mark-sweep collector. We keep our in-depth analysis of reference counting as general as possible so that it may be useful to other garbage collector implementers. Our finding that reference counting can be made directly competitive with well tuned mark-sweep should shake the community's prejudices about reference counting and perhaps open new opportunities for exploiting reference counting's strengths, such as localization and immediacy of reclamation.

Reference counting and tracing are the two fundamental approaches that have underpinned garbage collection since 1960. However, despite some compelling advantages, reference counting is almost completely ignored in implementations of high performance systems today. In this paper we take a detailed look at reference counting to understand its behavior and to improve its performance. We identify key design choices for reference counting and analyze how the behavior of a wide range of benchmarks might affect design decisions. As far as we are aware, this is the first such quantitative study of reference counting. We use insights gleaned from this analysis to introduce a number of optimizations that significantly improve the performance of reference counting.We find that an existing modern implementation of reference counting has an average 30% overhead compared to tracing, and that in combination, our optimizations are able to completely eliminate that overhead. This brings the performance of reference counting on par with that of a well tuned mark-sweep collector. We keep our in-depth analysis of reference counting as general as possible so that it may be useful to other garbage collector implementers. Our finding that reference counting can be made directly competitive with well tuned mark-sweep should shake the community's prejudices about reference counting and perhaps open new opportunities for exploiting reference counting's strengths, such as localization and immediacy of reclamation.

Garbage collectors are exact or conservative. An exact collector identifies all references precisely and may move referents and update references, whereas a conservative collector treats one or more of stack, register, and heap references as ambiguous. Ambiguous references constrain collectors in two ways.(1) Since they may be pointers, the collectors must retain referents. (2) Since they may be values, the collectors cannot modify them, pinning their referents.We explore conservative collectors for managed languages, with ambiguous stacks and registers. We show that for Java benchmarks they retain and pin remarkably few heap objects: <0.01% are falsely retained and 0.03% are pinned. The larger effect is collector design. Prior conservative collectors (1) use mark-sweep and unnecessarily forgo moving all objects, or (2) use mostly copying and pin entire pages. Compared to generational collection, overheads are substantial: 12% and 45% respectively. We introduce high performance conservative Immix and reference counting (RC). Immix is a mark-region collector with fine linegrain pinning and opportunistic copying of unambiguous referents. Deferred RC simply needs an object map to deliver the first conservative RC. We implement six exact collectors and their conservative counterparts. Conservative Immix and RC come within 2 to 3% of their exact counterparts. In particular, conservative RC Immix is slightly faster than a well-tuned exact generational collector. These findings show that for managed languages, conservative collection is compatible with high performance.

Despite some clear advantages and recent advances, reference counting remains a poor cousin to high-performance tracing garbage collectors. The advantages of reference counting include a) immediacy of reclamation, b) incrementality, and c) local scope of its operations. After decades of languishing with hopelessly bad performance, recent work narrowed the gap between reference counting and the fastest tracing collectors to within 10%. Though a major advance, this gap remains a substantial barrier to adoption in performance-conscious application domains. Our work identifies heap organization as the principal source of the remaining performance gap. We present the design, implementation, and analysis of a new collector, RC Immix, that replaces reference counting's traditional free-list heap organization with the line and block heap structure introduced by the Immix collector. The key innovations of RC Immix are 1) to combine traditional reference counts with per-line live object counts to identify reusable memory and 2) to eliminate fragmentation by integrating copying with reference counting of new objects and with backup tracing cycle collection. In RC Immix, reference counting offers efficient collection and the line and block heap organization delivers excellent mutator locality and efficient allocation. With these advances, RC Immix closes the 10% performance gap, matching the performance of a highly tuned production generational collector. By removing the performance barrier, this work transforms reference counting into a serious alternative for meeting high performance objectives for garbage collected languages.