Modern garbage collectors rely on read and write barriers imposed on heap accesses by the mutator, to keep track of references between different regions of the garbage collected heap, and to synchronize actions of the mutator with those of the collector. It has been a long-standing untested assumption that barriers impose significant overhead to garbage-collected applications. As a result, researchers have devoted effort to development of optimization approaches for elimination of unnecessary barriers, or proposed new algorithms for garbage collection that avoid the need for barriers while retaining the capability for independent collection of heap partitions. On the basis of the results presented here, we dispel the assumption that barrier overhead should be a primary motivator for such efforts. We present a

Much prior work has shown that the performance enabled by garbage collection (GC) systems is highly dependent upon the behavior of the application as well as on the available resources. That is, no single GC enables the best performance for all programs and all heap sizes. To address this limitation, we present the design, implementation, and empirical evaluation of a novel Java Virtual Machine (JVM) extension that facilitates dynamic switching between a number of very different and popular garbage collectors. We also show how to exploit this functionality using annotation-guided GC selection and evaluate the system using a large number of benchmarks. In addition, we implement and evaluate a simple heuristic to investigate the efficacy of switching automatically. Our results show that, on average, our annotation-guided system introduces less than 4% overhead and improves performance by 24% over the worstperforming GC (across heap sizes) and by 7% over always using the popular Generational/Mark-Sweep hybrid.

Until recently, the best performing copying garbage collectors used a generational policy which repeatedly collects the very youngest objects, copies any survivors to an older space, and then infrequently collects the older space. A previous study that used garbage collection simulation pointed to potential improvements by using an Older-First copying garbage collection algorithm. The Older-First algorithm sweeps a fixed-sized window through the heap from older to younger objects, and avoids copying the very youngest objects which have not yet had sufficient time to die. We describe and examine here an implementation of the Older-First algorithm in the Jikes RVM for Java. This investigation shows that Older-First can perform as well as the simulation results suggested, and greatly improves total program performance when compared to using a fixed-size nursery generational collector. We further compare Older-First to a flexible-size nursery generational collector in which the nursery occupies all of the heap that does not contain older objects. In these comparisons, the flexible-nursery collector is occasionally the better of the two, but on average the Older-First collector performs the best.

ABSTRACT Concurrent garbage collectors require write barriers to preserveconsistency, but these barriers impose significant direct and indirect costs. While there has been a lot of work on optimizing write barri-ers, we present the first study of their elision in a concurrent collector. We show conditions under which write barriers are redundant,and describe how these conditions can be applied to both incremental update or snapshot-at-the-beginning barriers. We then evaluatethe potential for write barrier elimination with a trace-based limit study, which shows that a significant percentage of write barriersare redundant. On average, 54 % of incremental barriers and 83 % of snapshot barriers are unnecessary.

In this paper, we describe a novel execution environment that can dynamically switch between garbage collection systems. As such, it enables selection of the most appropriate allocator and collector for a given application and underlying resource availability. Our system is novel in that it is able to switch between a wide range of diverse collection systems. It uses program annotations to guide selection of the collection system. In addition, it can automatically identify when to switch collectors when program execution behavior warrants it, i.e., it is adaptive. Our system introduces little overhead and accurately identifies the best collector for a wide range of benchmarks and heap sizes.