This paper presents Capriccio, a scalable thread package for use with high-concurrency servers. While recent work has advocated event-based systems, we believe that threadbased systems can provide a simpler programming model that achieves equivalent or superior performance.

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 present the design and implementation of a new garbage collection framework that significantly generalizes existing copying collectors. The Beltway framework exploits and separates object age and incrementality. It groups objects in one or more increments on queues called belts, collects belts independently, and collects increments on a belt in first-in-first-out order. We show that Beltway configurations, selected by command line options, act and perform the same as semi-space, generational, and older-first collectors, and encompass all previous copying collectors of which we are aware.

The deployment of Java as a concurrent programming language has created a critical need for high-performance, concurrent, and incremental multiprocessor garbage collection. We present the Recycler, a fully concurrent pure reference counting garbage collector that we have implemented in the Jalapeno Java virtual machine running on shared memory multiprocessors.

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.

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.

Garbage collection offers numerous software engineering advantages, but interacts poorly with virtual memory managers. Existing garbage collectors require far more pages than the application's working set and touch pages without regard to which ones are in memory, especially during full-heap garbage collection. The resulting paging can cause throughput to plummet and pause times to spike up to seconds or even minutes. We present a garbage collector that avoids paging. This bookmarking collector cooperates with the virtual memory manager to guide its eviction decisions. Using summary information ("bookmarks") recorded from evicted pages, the collector can perform in-memory full-heap collections. In the absence of memory pressure, the bookmarking collector matches the throughput of the best collector we tested while running in smaller heaps. In the face of memory pressure, it improves throughput by up to a factor of five and reduces pause times by up to a factor of 45 over the next best collector. Compared to a collector that consistently provides high throughput (generational mark-sweep), the bookmarking collector reduces pause times by up to 218x and improves throughput by up to 41x. Bookmarking collection thus provides greater utilization of available physical memory than other collectors while matching or exceeding their throughput.

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.

Advances in operating systems and languages have brought the ideal of reasonably-bounded execution time closer to developers who need such assurances for real-time and embedded systems applications. Recently, extensions to the Java libraries and virtual machine have been proposed in an emerging standard, which provides for specification of release times, execution costs, and deadlines for a restricted class of threads. To use such features, the code executing in the thread must never reference storage that could be subject to garbage collection. The new standard provides for regionlike, stack-allocated areas (scopes) of storage that are ignored by garbage collection and deallocated en masse. It now falls to the developer to adapt ordinary Java code to use the real-time Java scoped memory regions. Unfortunately, it is difficult to determine manually how to map object instantiations to scopes. Moreover, if ordinary Java code is modified to effect instantiations in scopes, the resulting code is difficult to read, maintain, and reuse. Static analysis can yield scopes that are correct across all program executions, but such analysis is necessarily conservative in nature. If too many objects appear to live forever under such analysis, then developers cannot rely on static analysis alone to form reasonable scopes. In this paper we present an approach for automatically determining appropriate storage scopes for Java objects, based on dynamic analysis—observed object lifetimes and object referencing behavior. While such analysis is perhaps unsafe across all program executions, our analysis can be coupled with static analysis to bracket object lifetimes, with the truth lying somewhere in between. We provide experimental results that show the memory regions discovered by our technique.

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.