SHF: Small: Information-Flow-Based Profiling of Concurrent Applications

SHF：小型：基于信息流的并发应用程序分析

• 批准号: 2006948
• 负责人: Brian Demsky
• 金额: $ 49.96万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006948&HistoricalAwards=false
• 关键词: SHF; Small; Information; Flow; Based

With the multi-core revolution, scaling to multiple cores is critical for performance. Even small fractions of sequential code seriously limit performance gains from parallelism. Critical sections sequentialize the execution and thus increasingly limit performance as the number of cores increases. The classic approach to scaling software to many cores begins with making locks more fine-grained. This is often not sufficient to scale beyond a handful of cores; scaling further often requires data structure implementations to be replaced with new implementations. Existing profilers inform developers where time is spent, but it is hard for developers to judge the impact of poorly selected data structure designs. It is often not clear whether the synchronization overhead is fundamental to the computation or can be addressed by rewriting the implementation.This project develops a new approach to profiling concurrent code based on information flow. Information flow analysis enables this approach to differentiate between concurrency bottlenecks that are fundamental to the computation and bottlenecks that are merely artifacts of the particular implementation. This approach has the potential to not only suggest where synchronization can be optimized but can also identify opportunities to improve performance by rewriting to code to eliminate superfluous conflicts. Previous work focused mostly on detecting conflicting accesses to shared data and thus missed this optimization opportunity. The project will implement this new approach in the FlowProf tool which will be publicly available.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着多核革命的发展，扩展到多核对性能至关重要。 即使是一小部分顺序代码也会严重限制并行性带来的性能提升。 临界区使执行顺序化，因此随着内核数量的增加，性能受到越来越多的限制。 将软件扩展到多个内核的经典方法是从使锁更细粒度开始的。这通常不足以扩展到几个核心之外;进一步扩展通常需要用新的实现替换数据结构实现。 现有的分析器告知开发人员时间花在哪里，但开发人员很难判断选择不当的数据结构设计的影响。 通常不清楚同步开销是计算的基础还是可以通过重写实现来解决。本项目开发了一种新的基于信息流的并发代码分析方法。 信息流分析使这种方法能够区分计算的基本并发瓶颈和仅仅是特定实现的工件的瓶颈。 这种方法不仅有可能建议在哪里可以优化同步，而且还可以通过重写代码来消除多余的冲突来识别提高性能的机会。 以前的工作主要集中在检测对共享数据的冲突访问，因此错过了这个优化机会。 这个奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Checking robustness to weak persistency models

• DOI: 10.1145/3519939.3523723
• 发表时间: 2022-06
• 期刊: Proceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation
• 作者: Hamed Gorjiara;Weiyu Luo;A. Lee;G. Xu;Brian Demsky

Understanding and automatically detecting conflicting interactions between smart home IoT applications

• DOI: 10.1145/3368089.3409682
• 发表时间: 2020-11
• 期刊: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
• 作者: R. Trimananda;S. A. H. Aqajari;Jason Chuang;Brian Demsky;G. Xu;Shan Lu

Stateful Dynamic Partial Order Reduction for Model Checking Event-Driven Applications that Do Not Terminate

用于模型检查、不终止的事件驱动应用程序的有状态动态偏序减少

• 发表时间: 2022
• 期刊: and Abstract Interpretation
• 作者: Trimananda, Rahmadi;Luo, Weiyu;Demsky, Brian;and Xu, Guoqing Harry
• 通讯作者: and Xu, Guoqing Harry

Yashme: detecting persistency races

• DOI: 10.1145/3503222.3507766
• 发表时间: 2022-02
• 期刊: Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems
• 作者: Hamed Gorjiara;G. Xu;Brian Demsky

Jaaru: efficiently model checking persistent memory programs

• DOI: 10.1145/3445814.3446735
• 发表时间: 2021-04
• 期刊: Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems
• 作者: Hamed Gorjiara;G. Xu;Brian Demsky