SPX: Collaborative Research: Pinpointing and Resolving Scalability Culprits Hidden in Different Components of the Whole System Stack

SPX：协作研究：查明并解决隐藏在整个系统堆栈不同组件中的可扩展性问题

• 批准号: 2024253
• 负责人: Tongping Liu
• 金额: $ 41.45万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-11-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024253&HistoricalAwards=false
• 关键词: SPX; Collaborative; Research; Pinpointing; Resolving

Modern computers leverage multi-core or many-core processors to accelerate parallel applications. Unfortunately, the speedup of these applications is typically far from ideal, due to some hidden scalability issues. Previous research mainly focuses on application code to identify scalability bottlenecks, neglecting the fact that the application code interacts with numerous external components, including memory allocator, third-party runtime libraries, and the operating system. Understanding and fixing scalability problems should hence go beyond application code and consider the whole software stack. The project's novelties are to pinpoint scalability culprits hidden in different components of the whole stack and automatically fix the scalability bottlenecks. The project's impacts are significantly improved performance for applications running on multi-core processors and thus accelerated scientific discoveries and energy saving.This project aims to systematically pinpoint and resolve latent software contention in all components of the whole software stack from user space. The proposed approaches are urgent due to the pervasive use of multi-core and many-core hardware. Also, according to Amdahl's law, a small degree of latent contention in any of the components may substantially limit the speedup potential on these modern hardware. The research plans to design low-overhead profilers to obtain runtime information for system calls, memory allocator behaviors, and all interacting events between components, as well as analyzers to automatically pinpoint the root causes of scalability bottlenecks. Through a runtime optimizer, the research aims to fix the identified scalability issues without intervention from the programmer. The project has potential to dramatically reduce manual effort for software optimization and improve performance for parallel applications on modern hardware.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.

现代计算机利用多核或多核处理器来加速并行应用程序。不幸的是，由于一些隐藏的可伸缩性问题，这些应用程序的加速比通常并不理想。以前的研究主要集中在应用程序代码以确定可伸缩性瓶颈，而忽略了应用程序代码与许多外部组件交互的事实，包括内存分配器、第三方运行时库和操作系统。因此，理解和修复可伸缩性问题应该超越应用程序代码，并考虑整个软件堆栈。该项目的创新之处在于找出隐藏在整个堆栈的不同组件中的可伸缩性罪魁祸首，并自动修复可伸缩性瓶颈。该项目的影响是显著提高了运行在多核处理器上的应用程序的性能，从而加速了科学发现和节能。该项目旨在从用户空间系统地准确定位和解决整个软件堆栈中所有组件中潜在的软件争用。由于多核和多核硬件的普遍使用，提出的方法迫在眉睫。此外，根据Amdahl定律，任何组件中的少量潜在争用都可能极大地限制这些现代硬件的加速潜力。该研究计划设计低开销的分析器，以获取系统调用、内存分配器行为和组件之间所有交互事件的运行时信息，以及自动查明可伸缩性瓶颈的根本原因的分析器。通过运行时优化器，该研究旨在修复已识别的可伸缩性问题，而无需程序员干预。该项目有可能极大地减少软件优化的人工工作量，并提高现代硬件上并行应用程序的性能。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

WATCHER: in-situ failure diagnosis

• DOI: 10.1145/3428211
• 发表时间: 2020-11
• 期刊: Proceedings of the ACM on Programming Languages
• 作者: Hongyu Liu;Sam Silvestro;X. Zhang;Jian Huang;Tongping Liu

Deadlock prediction via generalized dependency

• DOI: 10.1145/3533767.3534377
• 发表时间: 2022-07
• 期刊: Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis
• 作者: Jinpeng Zhou;Hanmei Yang;J. Lange;Tongping Liu

CachePerf: A Unified Cache Miss Classifier via Hybrid Hardware Sampling

CachePerf：通过混合硬件采样的统一缓存未命中分类器

• DOI: 10.1145/3547353.3526954
• 发表时间: 2022
• 期刊: ACM SIGMETRICS Performance Evaluation Review
• 作者: Zhou, Jin;Tang, Steven;Yang, Hanmei;Liu, Tongping
• 通讯作者: Liu, Tongping

Prober: Practically Defending Overflows with Page Protection

• DOI: 10.1145/3324884.3416533
• 发表时间: 2020-09
• 期刊: 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)
• 作者: Hongyu Liu;Ruiqin Tian;Tongping Liu;Bin Ren

NumaPerf: predictive NUMA profiling

NumaPerf：预测 NUMA 分析

• DOI: 10.1145/3447818.3460361
• 发表时间: 2021
• 期刊: Proceedings of the ACM International Conference on Supercomputing
• 作者: Zhao, Xin;Zhou, Jin;Guan, Hui;Wang, Wei;Liu, Xu;Liu, Tongping
• 通讯作者: Liu, Tongping