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: 1823005
• 负责人: Bo Wu
• 金额: $ 49.99万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1823005&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众核处理器来加速并行应用。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通常远非理想。以前的研究主要集中在应用程序代码，以确定可扩展性的瓶颈，忽略了这样一个事实，即应用程序代码与众多的外部组件，包括内存分配器，第三方运行时库，和操作系统的交互。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。该项目的新颖之处在于，它可以精确定位隐藏在整个堆栈的不同组件中的可伸缩性问题，并自动修复可伸缩性瓶颈。该项目的影响是显着提高多核处理器上运行的应用程序的性能，从而加速科学发现和节能。该项目旨在从用户空间系统地查明和解决整个软件堆栈的所有组件中潜在的软件竞争。由于多核和众核硬件的普遍使用，所提出的方法是迫切的。此外，根据Amdahl定律，任何组件中的小程度的潜在竞争都可能大大限制这些现代硬件上的加速潜力。该研究计划设计低开销的分析器来获取系统调用，内存分配器行为和组件之间所有交互事件的运行时信息，以及分析器来自动查明可扩展性瓶颈的根本原因。通过一个运行时优化器，研究的目的是修复识别的可伸缩性问题，而无需程序员的干预。该项目有可能大大减少软件优化的人工工作，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

Automatic Irregularity-Aware Fine-Grained Workload Partitioning on Integrated Architectures

集成架构上的自动不规则感知细粒度工作负载分区

• DOI: 10.1109/tkde.2019.2940184
• 发表时间: 2021-03
• 期刊: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
• 影响因子: 8.9
• 作者: Zhang Feng;Zhai Jidong;Wu Bo;He Bingsheng;Chen Wenguang;Du Xiaoyong
• 通讯作者: Du Xiaoyong

AutoMine: harmonizing high-level abstraction and high performance for graph mining

• DOI: 10.1145/3341301.3359633
• 发表时间: 2019-10
• 期刊: Proceedings of the 27th ACM Symposium on Operating Systems Principles
• 作者: Daniel Mawhirter;Bo Wu