Scalable Concurrent Compacting Garbage Collection for Commodity Multi-Core Processors

适用于商品多核处理器的可扩展并发压缩垃圾收集

• 批准号: 0702240
• 负责人: Antony Hosking
• 金额: --
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-15 至 2011-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0702240&HistoricalAwards=false
• 关键词: Scalable; Concurrent; Compacting; Garbage; Collection

Concurrency is an increasingly important area of study as commodity parallel multi-core processors (such as Intel's Core processors) enter the mainstream.At the same time, mainstream programming languages such as Java and C# support both concurrent programming and automatic memory management (so-called "garbage collection" or GC). Ordinary programmers expect their programming platforms to yield both responsiveness and performance for their applications running on commodity parallel computers. This project explores new approaches to GC that exploit hardware parallelism to retain and improve desirable space/time performance characteristics for programs. It will benefit the hardware industry by exploiting their new multi-core offerings, while contributing to economic and national security by enhancing usability, performance, and reliability of software. The project will produce high-demand graduates skilled in the art and science of concurrent programming, able to exploit parallelism in the production of more reliable high-performance application systems, from the desktop to the data center, so expanding the capabilities of the national technical work-force.While efficient concurrent non-moving collectors are ubiquitous in production programming languages, efficient non-intrusive compaction is still missing. Without compaction (whether by the GC or separately), maximizing memory utilization and locality, and minimizing memory fragmentation is difficult. Retaining responsiveness and exploiting parallelism, means reconciling compaction with concurrency. The project is devising new approaches for automatic compaction of dynamically allocated data, with emphasis on techniques that allow compaction to occur concurrently (i.e., in parallel) with the application. Concurrent compaction approaches based on concurrent copying (i.e., moving) GC as well as standalone concurrent compaction (in concert with non-moving GC) are being designed, implemented, and evaluated for established benchmark programs using metrics that measure responsiveness, application utilization, and throughput.

随着商用并行多核处理器（例如英特尔的酷睿处理器）进入主流，并发成为一个越来越重要的研究领域。同时，Java和C#等主流编程语言同时支持并发编程和自动内存管理（所谓的“垃圾收集”或GC）。 普通程序员希望他们的编程平台能够为其在商用并行计算机上运行的应用程序提供响应能力和性能。 该项目探索了新的 GC 方法，利用硬件并行性来保留和改进程序所需的空间/时间性能特征。 它将通过利用新的多核产品使硬件行业受益，同时通过增强软件的可用性、性能和可靠性为经济和国家安全做出贡献。 该项目将培养精通并发编程艺术和科学的高需求毕业生，能够利用并行性来生产更可靠的高性能应用系统（从桌面到数据中心），从而扩大国家技术劳动力的能力。虽然高效的并发非移动收集器在生产编程语言中无处不在，但高效的非侵入式压缩仍然缺失。如果没有压缩（无论是通过 GC 还是单独压缩），则很难最大化内存利用率和局部性并最小化内存碎片。保持响应能力并利用并行性意味着协调压缩与并发性。 该项目正在设计自动压缩动态分配数据的新方法，重点是允许压缩与应用程序同时（即并行）发生的技术。 基于并发复制（即移动）GC 以及独立并发压缩（与非移动 GC 配合）的并发压缩方法正在设计、实现和评估，以使用衡量响应性、应用程序利用率和吞吐量的指标来建立基准程序。