CPA-CPL: Scalable Analysis for Concurrent Programs

CPA-CPL：并发程序的可扩展分析

• 批准号: 0811512
• 负责人: Sorin Lerner
• 金额: $ 35万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811512&HistoricalAwards=false
• 关键词: CPA; CPL; Scalable; Analysis; Concurrent

Concurrency is a prevalent feature of almost all critical computing infrastructure, including operating systems, databases, internet-routing tables, and banking systems. Furthermore, several recent trends in technology, such as the widespread adoption of multi-core processors, web-service-oriented architectures, and peer-to-peer systems, are making concurrency more important than ever in mainstream systems. Unfortunately, concurrent systems are hard to build, because programmers have a hard time mentally accounting for the many possible ways in which these concurrent systems can interact. The broad goal of this research is to provide programmers with the tools they need to develop concurrent systems that are reliable, efficient and robust.The investigators will focus their efforts on one particular kind of tool, namely static analysis tools. Indeed, advances in static algorithms for program optimization and error detection have shown that static analysis can dramatically improve the reliability and performance of computer systems. However, most of these algorithmic advances are limited to sequential programs and ignore the challenges introduced by concurrency, where the need for static checking and potential for optimization are the greatest.This research will develop scalable and precise analysis techniques for concurrent programs. Achieving scalability and precision at the same time, however, is difficult because there is a widely acknowledged tension between these two goals. To address this challenge, the general methodology will be to design analyses that are sound and scalable first (likely at the expense of precision), and then to iteratively refine these analyses by empirically identifying the common concurrent programming idioms where our analyses lose precision, and developing analysis techniques targeted at these idioms.

并发是几乎所有关键计算基础架构的普遍特征，包括操作系统，数据库，互联网路由表和银行系统。 此外，技术的最新趋势，例如多核处理器的广泛采用，面向网络服务的体系结构和点对点系统，使同意在主流系统中的重要性更为重要。不幸的是，并发系统很难构建，因为程序员在精神上很难考虑这些并发系统可以交互的许多可能方法。这项研究的广泛目标是为程序员提供他们需要的工具来开发可靠，高效且健壮的并发系统。研究人员将把精力集中在一种特定类型的工具上，即静态分析工具。确实，用于程序优化和错误检测的静态算法的进步表明，静态分析可以显着提高计算机系统的可靠性和性能。但是，大多数这些算法进步仅限于顺序程序，而忽略了并发引入的挑战，在这种情况下，对静态检查的需求和优化的潜力是最大的。这项研究将开发出可相关计划的可扩展和精确的分析技术。但是，同时实现可伸缩性和精度是困难的，因为这两个目标之间存在广泛认可的紧张关系。为了应对这一挑战，一般方法将是设计合理且可扩展的分析（可能是以精度为代价），然后通过凭经验鉴定我们的分析失去精度的常见并发编程成语，并通过经验识别以这些成体为目标的分析技术来迭代地完善这些分析。