ABSTRACT In systems that support garbage collection, a tension exists between collecting garbage too frequently and not collecting garbage frequently enough. Garbage collection that occurs too frequently may introduce unnecessary overheads at the risk of not collecting much garbage during each cycle. On the other hand, collecting garbage too infrequently can result in applications that execute with a large amount of virtual memory (i.e., with a large footprint) and suffer from increased execution times due to paging. In this paper, we use a large collection of JavaTMapplications and the highly tuned and widely used Boehm-DemersWeiser (BDW) conservative mark-and-sweep garbage collector to experimentally examine the extent to which the frequency of garbage collection impacts an application's execution time, footprint, and pause times. We use these results to devise some guidelines for controlling garbage collection and heap growth in a conservative garbage collector in order to minimize application execution times. Then we describe new strategies for controlling garbage collection and heap growth that impact not only the frequency with which garbage collection occurs but also the points at which garbage collection occurs. Experimental results demonstrate that, when compared with the existing approach used in the standard BDW collector, our new strategy can significantly reduce application execution times. Our goal is to obtain a better understanding of how to control garbage collection and heap growth for an individual application executing in isolation. These results can be applied in a number of high-performance computing and server environments, in addition to some single-user environments. This work should also provide insights into how

Java performance is far from being trivial to benchmark because it is affected by various factors such as the Java application, its input, the virtual machine, the garbage collector, the heap size, etc. In addition, non-determinism at run-time causes the execution time of a Java program to differ from run to run. There are a number of sources of non-determinism such as Just-In-Time (JIT) compilation and optimization in the virtual machine (VM) driven by timerbased method sampling, thread scheduling, garbage collection, and various system effects. There exist a wide variety of Java performance evaluation methodologies used by researchers and benchmarkers. These methodologies differ from each other in a number of ways. Some report average performance over a number of runs of the same experiment; others report the best or second best performance observed; yet others report the worst. Some iterate the benchmark multiple times within a single VM invocation; others consider multiple VM invocations and iterate a single benchmark execution; yet others consider multiple VM invocations and iterate the benchmark multiple times. This paper shows that prevalent methodologies can be misleading, and can even lead to incorrect conclusions. The reason is that the data analysis is not statistically rigorous. In this paper, we present a survey of existing Java performance evaluation methodologies and discuss the importance of statistically rigorous data analysis for dealing with non-determinism. We advocate approaches to quantify startup as well as steady-state performance, and, in addition, we provide the JavaStats software to automatically obtain performance numbers in a rigorous manner. Although this paper focuses on Java performance evaluation, many of the issues addressed in this paper also apply to other programming languages and systems that build on a managed runtime system.

Concurrent garbage collectors are notoriously hard to design, implement, and verify. We present a framework for the automatic exploration of a space of concurrent mark-and-sweep collectors. In our framework, the designer specifies a set of “building blocks” from which algorithms can be constructed. These blocks reflect the designer’s insights about the coordination between the collector and the mutator. Given a set of building blocks, our framework automatically explores a space of algorithms, using model checking with abstraction to verify algorithms in the space. We capture the intuition behind some common mark-and-sweep algorithms using a set of building blocks. We utilize our framework to automatically explore a space of more than 1, 600, 000 algorithms built from these blocks, and derive over 100 correct finegrained algorithms with various space, synchronization, and precision tradeoffs.

Constructing correct concurrent garbage collection algorithms is notoriously hard. Numerous such algorithms have been proposed, implemented, and deployed – and yet the relationship among them in terms of speed and precision is poorly understood, and the validation of one algorithm does not carry over to others. As programs with low latency requirements written in garbagecollected languages become part of society’s mission-critical infrastructure, it is imperative that we raise the level of confidence in the correctness of the underlying system, and that we understand the trade-offs inherent in our algorithmic choice. In this paper we present correctness-preserving transformations that can be applied to an initial abstract concurrent garbage collection algorithm which is simpler, more precise, and easier to prove correct than algorithms used in practice — but also more expensive and with less concurrency. We then show how both pre-existing and new algorithms can be synthesized from the abstract algorithm by a series of our transformations. We relate the algorithms formally using a new definition of precision, and informally with respect to overhead and concurrency. This provides many insights about the nature of concurrent collection, allows the direct synthesis of new and useful algorithms, reduces the burden of proof to a single simple algorithm, and lays the groundwork for the automated synthesis of correct concurrent collectors. 1.

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.

Parallel and concurrent garbage collectors are increasingly employed by managed runtime environments (MREs) to maintain scalability, as multi-core architectures and multi-threaded applications become pervasive. Moreover, state-of-the-art MREs commonly implement compaction to eliminate heap fragmentation and enable fast linear object allocation. Our empirical analysis of object demographics reveals that unreachable objects in the heap tend to form clusters large enough to be effectively managed at the granularity of virtual memory pages. Even though processes can manipulate the mapping of the virtual address space through the standard operating system (OS) interface on most platforms, extant parallel/concurrent compactors do not do so to exploit this clustering behavior and instead achieve compaction by performing, relatively expensive, object moving and pointer adjustment. We introduce the Mapping Collector (MC), which leverages virtual memory operations to reclaim and consolidate free space without moving objects and updating pointers. MC is a nearly-singlephase compactor that is simpler and more efficient than previously reported compactors that comprise two to four phases. Through effective MRE-OS coordination, MC maintains the simplicity of a non-moving collector while providing efficient parallel and concurrent compaction. We implement both stop-the-world and concurrent MC in a generational garbage collection framework within the open-source HotSpot Java Virtual Machine. Our experimental evaluation using a multiprocessor indicates that MC significantly increases throughput and scalability as well as reduces pause times, relative to stateof-the-art, parallel and concurrent compactors.

A multi-tasking virtual machine (MVM) executes multiple programs in isolation, within a single operating system process. The goal of a MVM is to improve startup time, overall system throughput, and performance, by effective reuse and sharing of system resources across programs (tasks). However, multitasking also mandates a memory management system capable of offering a guarantee of isolation with respect to garbage collection costs, accounting of memory usage, and timely reclamation of heap resources upon task termination. To this end, we investigate and evaluate, novel task-aware extensions to a state-of-the-art MVM garbage collector (GC). Our task-aware GC exploits the generational garbage collection hypothesis, in the context of multiple tasks, to provide performance isolation by maintaining task-private young generations. Task aware GC facilitates concurrent per-task allocation and promotion, and minimizes synchronization and scanning overhead. In addition, we efficiently track per-task heap usage to enable GC-free reclamation upon task termination. Moreover, we couple these techniques with a light-weight synchronization mechanism that enables pertask minor collection, concurrently with allocation by other tasks. We empirically evaluate the efficiency, scalability, and throughput that our task-aware GC system enables. Categories and Subject Descriptors D.3.4 [Programming Languages]: Processors—Memory management (garbage collection)