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：协作研究：RUI：快速，精确的动态种族检测：消除状态和检查冗余，国家计算基础架构利用了从小型移动设备到大型数据中心的整个系统中的多层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，但是线程之间无意间干扰的潜力使得难以确保其可靠性。 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，但是目前它们具有巨大的性能缺点，限制了其广泛采用。 因此，制定更有效的分析至关重要。 这项研究研究了如何提高运行时分析的性能，以检测数据竞赛条件。 数据竞赛也许是线程干扰的最基本类型，当多个线程在没有正确同步的情况下操纵共享内存位置时，就会发生。 这些技术开发了在目标程序执行过程中观察到的利用记忆访问模式，以适应由动态竞赛探测器维护的访问历史记录信息，以及消除冗余检查的新优化，并用单个合并的检查来替换有关相关存储位置的多次检查。 知识分子的优点是对现有技术的实质性提高，并在其他重要的非干扰性能（包括原子性和确定性）的动态分析中可以进行可比的改进。 这样的分析还必须推理有关种族的原因。 更广泛的影响可能会通过更广泛地采用分析工具来检测种族条件和其他并发缺陷，从而有助于国家软件研究基础设施，并增加对学生的科学研究机会和培训的机会，从而改变了多线软件工程的原理和实践。