SHF: Small: Collaborative Research: RUI: Fast and Precise Dynamic Race Detection: Eliminating State and Checking Redundancy

SHF：小型：协作研究：RUI：快速精确的动态竞争检测：消除状态并检查冗余

• 批准号: 1421051
• 负责人: Stephen Freund
• 金额: $ 19.9万
• 依托单位: Williams College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421051&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; RUI

Title: SHF:Small:Collaborative Research:RUI: Fast and Precise Dynamic Race Detection: Eliminating State and Checking RedundancyThe national computing infrastructure utilizes multicore processors and multiprocessor hardware across the entire spectrum of systems from small mobile devices to huge data centers. These systems can execute many software threads of control concurrently to improve responsiveness and performance, but the potential for unintentional interference between threads makes it difficult to ensure their reliability. Run-time analysis tools for automatically detecting interference problems can help improve software reliability, but they currently have significant performance shortcomings limiting their widespread adoption. Developing more efficient analyses is thus essential. This research examines how to improve the performance of run-time analyses for detecting data race conditions. Data races are perhaps the most fundamental type of thread interference and occur when multiple threads manipulate a shared memory location without proper synchronization.The project addresses both the space and time overhead of dynamic race detection. The techniques developed leverage memory access patterns observed during a target program's execution to adaptively compress the access history information maintained by a dynamic race detector, as well as new optimizations for eliminating redundant checks and for replacing multiple checks on related memory locations with a single coalesced check. The intellectual merits are achieving substantial performance improvements over existing techniques and enabling comparable improvements in dynamic analyses for other important non-interference properties, including atomicity and determinism. Such analyses must also reason about races. The broader impacts are potentially transforming the principles and practices of multithreaded software engineering by enabling more widespread adoption of analysis tools to detect race conditions and other concurrency defects, contributing to the national software research infrastructure, and increasing access to science research opportunities and training for students.

标题：SHF：小型：协作研究：RUI：快速精确的动态竞争检测：消除状态和检查冗余国家计算基础设施在从小型移动设备到大型数据中心的整个系统范围内使用多核处理器和多处理器硬件。 这些系统可以同时执行许多软件控制线程，以提高响应能力和性能，但线程之间潜在的无意干扰使其难以确保其可靠性。 用于自动检测干扰问题的运行时分析工具有助于提高软件可靠性，但它们目前存在显着的性能缺陷，限制了其广泛采用。 因此，开发更有效的分析至关重要。 这项研究探讨了如何提高运行时分析的性能以检测数据竞争条件。 数据竞争可能是最基本的线程干扰类型，当多个线程在没有适当同步的情况下操作共享内存位置时，就会发生数据竞争。该项目解决了动态竞争检测的空间和时间开销。 开发的技术利用目标程序执行期间观察到的内存访问模式来自适应压缩由动态竞争检测器维护的访问历史信息，以及用于消除冗余检查和用单个合并检查替换相关内存位置上的多个检查的新优化。 其智力优点是比现有技术实现了显着的性能改进，并在其他重要的非干扰特性（包括原子性和确定性）的动态分析中实现了类似的改进。 此类分析还必须对种族进行推理。 更广泛的影响可能会改变多线程软件工程的原则和实践，使分析工具能够更广泛地采用来检测竞争条件和其他并发缺陷，为国家软件研究基础设施做出贡献，并增加学生获得科学研究机会和培训的机会。