Increasingly popular languages such as Java and C # require efficient garbage collection. This paper presents the design, implementation, and evaluation of MMTk, a Memory Management Toolkit for and in Java. MMTk is an efficient, composable, extensible, and portable framework for building garbage collectors. MMTk uses design patterns and compiler cooperation to combine modularity and efficiency. The resulting system is more robust, easier to maintain, and has fewer defects than monolithic collectors. Experimental comparisons with monolithic Java and C implementations reveal MMTk has significant performance advantages as well. Performance critical system software typically uses monolithic C at the expense of flexibility. Our results refute common wisdom that only this approach attains efficiency, and suggest that performance critical software can embrace modular design and high-level languages. 1

We introduce a new family of connectivity-based garbage collectors (Cbgc) that are based on potential objectconnectivity properties. The key feature of these collectors is that the placement of objects into partitions is determined by performing one of several forms of connectivity analyses on the program. This enables partial garbage collections, as in generational collectors, but without the need for any write barrier.

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

General purpose garbage collectors have yet to combine short pause times with high throughput. For example, generational collectors can achieve high throughput. They have modest average pause times, but occasionally collect the whole heap and consequently incur long pauses. At the other extreme, concurrent collectors, including reference counting, attain short pause times but with significant performance penalties. This paper introduces a new hybrid collector that combines copying generational collection for the young objects and reference counting the old objects to achieve both goals. It restricts copying and reference counting to the object demographics for which they perform well. Key to our algorithm is a generalization of deferred reference counting we call Ulterior Reference Counting. Ulterior reference counting safely ignores mutations to select heap objects. We compare a generational reference counting hybrid with pure reference counting, pure marksweep, and hybrid generational mark-sweep collectors. This new collector combines excellent throughput, matching a high performance generational mark-sweep hybrid, with low maximum pause times.

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.

Copying garbage collectors have a number of advantages over noncopying collectors, including cheap allocation and avoiding fragmentation. However, in order to provide completeness (the guarantee to reclaim each garbage object eventually), standard copying collectors require space equal to twice the size of the maximum live data for a program. We present a mark-copy collection algorithm (MC) that extends generational copying collection and significantly reduces the heap space required to run a program. MC reduces space overhead by 75–85 % compared with standard copying garbage collectors, increasing the range of applications that can use copying garbage collection. We show that when MC is given the same amount of space as a generational copying collector, it improves total execution time of Java benchmarks significantly in tight heaps, and by 5–10 % in moderate size heaps. We also compare the performance of MC with a (non-generational) mark-sweep collector and a hybrid copying/mark-sweep generational collector. We find that MC can run in heaps comparable in size to the minimum heap space required by mark-sweep. We also find that for most benchmarks MC is significantly faster than mark-sweep in small and moderate size heaps. When compared with the hybrid collector, MC improves total execution time by about 5 % for some benchmarks, partly by increasing the speed of execution of the application code.

Java is becoming the main software platform for consumer and embedded devices such as mobile phones, PDAs, TV set-top boxes, and in-vehicle systems. Since many of these systems are memory constrained, it is extremely important to keep the memory footprint of Java applications under control.

Current user-mode machine simulators typically do not support simulation of dynamic compilation, threads, or garbage collection, all of which Java Virtual Machines (JVMs) require. In this paper, we describe, evaluate, and validate Dynamic SimpleScalar (DSS). DSS is a tool that simulates Java programs running on a JVM, using just-in-time compilation, executing on a simulated multi-way issue, out-of-order execution superscalar processor with a sophisticated memory system. We describe the implementation of the minimal support necessary for simulating a JVM in SimpleScalar, including signals, thread scheduling, synchronization, and dynamic code generation, all required by a JVM. We validate our simulator using IBM Research's Jikes RVM, a state-of-the-art JVM that runs Submitting to the First Annual IEEE/ACM International Symposium On Code Generation and Optimization.

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.

Programmers are increasingly choosing managed languages for modern applications, which tend to allocate many short-to-medium lived small objects. The garbage collector therefore directly determines program performance by making a classic space-time tradeoff that seeks to provide space efficiency, fast reclamation, and mutator performance. The three canonical tracing garbage collectors: semi-space, mark-sweep, and mark-compact each sacrifice one objective. This paper describes a collector family, called mark-region, and introduces opportunistic defragmentation, which mixes copying and marking in a single pass. Combining both, we implement immix, a novel high performance garbage collector that achieves all three performance objectives. The key insight is to allocate and reclaim memory in contiguous regions, at a coarse block grain when possible and otherwise in groups of finer grain lines. We show that immix outperforms existing canonical algorithms, improving total application performance by 7 to 25 % on average across 20 benchmarks. As the mature space in a generational collector, immix matches or beats a highly tuned generational collector, e.g. it improves jbb2000 by 5%. These innovations and the identification of a new family of collectors open new opportunities for garbage collector design.