Stephen M. Blackburn

The DaCapo benchmarks: java benchmarking development and analysis

Since benchmarks drive computer science research and industry product development, which ones we use and how we evaluate them are key questions for the community. Despite complex runtime tradeoffs due to dynamic compilation and garbage collection required for Java programs, many evaluations still use methodologies developed for C, C++, and Fortran. SPEC, the dominant purveyor of benchmarks, compounded this problem by institutionalizing these methodologies for their Java benchmark suite. This paper recommends benchmarking selection and evaluation methodologies, and introduces the DaCapo benchmarks, a set of open source, client-side Java benchmarks. We demonstrate that the complex interactions of (1) architecture, (2) compiler, (3) virtual machine, (4) memory management, and (5) application require more extensive evaluation than C, C++, and Fortran which stress (4) much less, and do not require (3). We use and introduce new value, time-series, and statistical metrics for static and dynamic properties such as code complexity, code size, heap composition, and pointer mutations. No benchmark suite is definitive, but these metrics show that DaCapo improves over SPEC Java in a variety of ways, including more complex code, richer object behaviors, and more demanding memory system requirements. This paper takes a step towards improving methodologies for choosing and evaluating benchmarks to foster innovation in system design and implementation for Java and other managed languages.

The Jikes research virtual machine project: building an open-source research community

This paper describes the evolution of the JikesTM Research Virtual Machine project from an IBM internal research project, called Jalapeno, into an open-source project. After summarizing the original goals of the project, we discuss the motivation for releasing it as an open-source project and the activities performed to ensure the success of the project. Throughout, we highlight the unique challenges of developing and maintaining an open-source project designed specifically to support a research community.

Oil and water? High performance garbage collection in Java with MMTk

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.

The garbage collection advantage: improving program locality

As improvements in processor speed continue to outpace improvements in cache and memory speed, poor locality increasingly degrades performance. Because copying garbage collectors move objects, they have an opportunity to improve locality. However, no static copying order is guaranteed to match program traversal orders. This paper introduces <i>online object reordering</i> (OOR) which includes a new dynamic, online class analysis for Java that detects program traversal patterns and exploits them in a copying collector. OOR uses run-time method sampling that drives just-in-time (JIT) compilation. For each <i>hot</i> (frequently executed) method, OOR analysis identifies the hot field accesses. At garbage collection time, the OOR collector then copies referents of hot fields together with their parent. Enhancements include static analysis to exclude accesses in cold basic blocks, heuristics that decay heat to respond to phase changes, and a separate space for hot objects. The overhead of OOR is on average negligible and always less than 2% on Java benchmarks in Jikes RVM with MMTk. We compare program performance of OOR to static class-oblivious copying orders (e.g., breadth and depth first). Performance variation due to static orders is often low, but can be up to 25%. In contrast, OOR matches or improves upon the best static order since its history-based copying tunes memory layout to program traversal.

Immix: a mark-region garbage collector with space efficiency, fast collection, and mutator performance

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.

Beltway: getting around garbage collection gridlock

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 increasing reliance on garbage collected languages such as Java requires that the collector perform well. We show that the generality of Beltway enables us to design and implement new collectors that are robust to variations in heap size and improve total execution time over the best generational copying collectors of which we are aware by up to 40%, and on average by 5 to 10%, for small to moderate heap sizes. New garbage collection algorithms are rare, and yet we define not just one, but a new family of collectors that subsumes previous work. This generality enables us to explore a larger design space and build better collectors.

Ulterior reference counting: fast garbage collection without a long wait

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 mark-sweep, 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.

Wake up and smell the coffee: evaluation methodology for the 21st century

Evaluation methodology underpins all innovation in experimental computer science. It requires relevant workloads, appropriate experimental design, and rigorous analysis. Unfortunately, methodology is not keeping pace with the changes in our field. The rise of managed languages such as Java, C#, and Ruby in the past decade and the imminent rise of commodity multicore architectures for the next decade pose new methodological challenges that are not yet widely understood. This paper explores the consequences of our collective inattention to methodology on innovation, makes recommendations for addressing this problem in one domain, and provides guidelines for other domains. We describe benchmark suite design, experimental design, and analysis for evaluating Java applications. For example, we introduce new criteria for measuring and selecting diverse applications for a benchmark suite. We show that the complexity and nondeterminism of the Java runtime system make experimental design a first-order consideration, and we recommend mechanisms for addressing complexity and nondeterminism. Drawing on these results, we suggest how to adapt methodology more broadly. To continue to deliver innovations, our field needs to significantly increase participation in and funding for developing sound methodological foundations.

Pretenuring for Java

Pretenuring can reduce copying costs in garbage collectors by allocating long-lived objects into regions that the garbage collector with rarely, if ever, collect. We extend previous work on pretenuring as follows. (1) We produce pretenuring advice that is neutral with respect to the garbage collector algorithm and configuration. We thus can and do combine advice from different applications. We find that predictions using object lifetimes at each allocation site in Java prgroams are accurate, which simplifies the pretenuring implementation. (2) We gather and apply advice to applications and the Jalapeño JVM, a compiler and run-time system for Java written in Java. Our results demonstrate that building combined advice into Jalapeño from different application executions improves performance regardless of the application Jalapeño is compiling and executing. This build-time advice thus gives user applications some benefits of pretenuring without any application profiling. No previous work pretenures in the run-time system. (3) We find that application-only advice also improves performance, but that the combination of build-time and application-specific advice is almost always noticeably better. (4) Our same advice improves the performance of generational and Older First colleciton, illustrating that it is collector neutral.

Error-free garbage collection traces: how to cheat and not get caught

Programmers are writing a large and rapidly growing number of programs in object-oriented languages such as Java that require garbage collection (GC). To explore the design and evaluation of GC algorithms quickly, researchers are using simulation based on traces of object allocation and lifetime behavior. The brute force method generates perfect traces using a whole-heap GC at every potential GC point in the program. Because this process is prohibitively expensive, researchers often use granulated traces by collecting only periodically, e.g., every 32K bytes of allocation.We extend the state of the art for simulating GC algorithms in two ways. First, we present a systematic methodology and results on the effects of trace granularity for a variety of copying GC algorithms. We show that trace granularity often distorts GC performance results compared with perfect traces, and that some GC algorithms are more sensitive to this effect than others. Second, we introduce and measure the performance of a new precise algorithm for generating GC traces which is over 800 times faster than the brute force method. Our algorithm, called Merlin, frequently timestamps objects and later uses the timestamps of dead objects to reconstruct precisely when they died. It performs only periodic garbage collections and achieves high accuracy at low cost, eliminating any reason to use granulated traces.