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

职务名称：SHF：小型：合作研究：RUI：快速精确的动态竞争检测：消除状态并检查冗余国家计算基础设施在从小型移动的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，但线程之间可能会发生无意干扰，因此难以确保其可靠性。 用于自动检测干扰问题的运行时分析工具可以帮助提高软件可靠性，但它们目前具有显著的性能缺陷，限制了它们的广泛采用。 因此，开展更有效的分析至关重要。 本研究探讨如何提高运行时分析的性能，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。该项目解决了动态竞争检测的空间和时间开销。 所开发的技术利用在目标程序的执行期间观察到的存储器访问模式来自适应地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于用单个合并检查替换对相关存储器位置的多个检查的新优化。 智力的优点是实现了显着的性能改进现有的技术，并使其他重要的非干扰特性，包括原子性和确定性的动态分析的可比改进。 这种分析也必须考虑种族因素。 更广泛的影响可能会改变多线程软件工程的原则和实践，使更广泛地采用分析工具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。

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.