CSR: Small: Scalable Applications Start with Scalable Virtual Machine Services

CSR：小型：可扩展的应用程序从可扩展的虚拟机服务开始

• 批准号: 0917191
• 负责人: Kathryn McKinley
• 金额: $ 50万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0917191&HistoricalAwards=false
• 关键词: CSR; Small; Scalable; Applications; Start

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The goal of this proposal is to devise and implement a scalable virtual machine that includes scalable garbage collectors, profilers and just-in-time (JIT) compilers.As hardware vendors deliver chip multiprocessors (CMPs) as the next-generation general-purpose computing substrate, programmers are increasingly choosing managed languages for their next generation applications. The virtual machine (VM) stands between the two, integrating them together. However, VMs are not yet capable of providing scalable performance to applications on parallel systems composed of CMPs. VMs are limited in part because key features, such as dynamic profiling, compilation, and garbage collection, are often not themselves scalable. This VM scalability bottleneck is an impediment to application scaling. For example, because VMs are oblivious to application data partitioning, they can introduce unnecessary communication such as false sharing. This project seeks to solve this problem by designing and implementing scalable virtual machine (SVM) services. SVM includes novel dynamic profilers, just-in-time (JIT) compilers, and garbage collectors that are themselves scalable and support parallel applications. The project explores how to design and build a framework for parallel dynamic analysis, JIT compilation, and garbage collection that uses novel cost models of the application, analysis, JIT, and collector work to optimize for scalable high-performance. In particular, analysis, JIT, and garbage collector work is divided and scheduled in novel ways to mirror application partitions and threads.SVM will be developed within a Java Virtual Machine, but the algorithms will apply to other managed languages such as C\#, JavaScript, Python, and Ruby. The investigators will make both SVM and parallel applications publicly available, adding to the national research infrastructure.

该奖项是根据2009年美国复苏和再投资法案资助的（Public Law 111-5）.该提案的目标是设计和实现一个可扩展的虚拟机，包括可扩展的垃圾收集器、分析器和即时（JIT）编译器。随着硬件供应商提供芯片多处理器（CMP）作为下一代通用计算基底，程序员越来越多地为他们的下一代应用程序选择托管语言。虚拟机（VM）介于两者之间，将它们集成在一起。然而，虚拟机还不能提供可扩展的性能的应用程序组成的CMP并行系统。VM受到限制的部分原因是关键功能（如动态分析、编译和垃圾收集）本身通常不可伸缩。这种VM可伸缩性瓶颈是应用程序扩展的一个障碍。例如，由于VM不注意应用程序数据分区，因此它们可能会引入不必要的通信，例如错误共享。该项目旨在通过设计和实现可扩展的虚拟机（SVM）服务来解决这个问题。SVM包括新颖的动态分析器、即时（JIT）编译器和垃圾收集器，它们本身是可扩展的，并支持并行应用程序。 该项目探索如何设计和构建一个并行动态分析，JIT编译和垃圾收集的框架，使用应用程序，分析，JIT和收集器工作的新成本模型来优化可扩展的高性能。 特别是，分析、JIT和垃圾收集器工作以新颖的方式进行划分和调度，以镜像应用程序分区和线程。SVM将在Java虚拟机中开发，但算法将适用于其他托管语言，如C\#、JavaScript、Python和Ruby。 研究人员将公开SVM和并行应用程序，增加国家研究基础设施。