Automated monitoring and debugging of large scale manycore heterogeneous systems

大规模众核异构系统的自动监控和调试

• 批准号: 507883-2016
• 负责人: Dagenais, Michel
• 金额: $ 9.25万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=696820
• 关键词: Automated; monitoring; debugging; large; scale

The communication and computing infrastructure has evolved through the years, getting more efficient, sophisticated, integrated and networked. Newer mobile devices (including smart robots or autonomous cars) and servers often contain 8 or more cores in their central processing unit. These systems are based on heterogeneous processors, with efficient traditional central processing units, but also with co-processing units optimised for graphics (GPGPUs with thousands of cores), networking, signal processing or even for Machine Learning. These co-processing units are highly parallel and often contain over 8 billion logic elements (transistors) each. Adding to this complexity is the increasing reliance on virtualisation, which hides the specificities of the hardware, allowing an application to run on several different processor models, but makes the performance more difficult to analyse. As a result, even a simple operation such as initiating a phone call, making a Web search, routing a packet or displaying a video frame, can involve many parallel cores on more than one processing unit, possibly on several servers. Moreover, the same operation, a few seconds later, may be served in a different way by different cores and physical servers. Therefore, understanding the performance of these operations has become extremely difficult and the tools for that purpose are severely lacking. In this project, the tracing, monitoring, profiling and debugging tools for manycore systems will be extended to efficiently extract information from all units in all layers, from the hardware to the application, and cope with the large number (several thousands) of cores. Furthermore, new methods and algorithms will be developed to automate the analysis of the extracted monitoring data. As a result, the designers and operators of distributed applications on mobile devices, cloud servers and other heterogeneous computing systems, will have the tools in hand to quickly analyse their system performance, automatically or manually find problems, and optimise operations.

多年来，通信和计算基础设施不断发展，变得更加高效、复杂、集成和网络化。较新的移动的设备（包括智能机器人或自动汽车）和服务器通常在其中央处理器中包含8个或更多个核心。这些系统基于异构处理器，具有高效的传统中央处理单元，但也具有针对图形（具有数千个核心的GPGPU），网络，信号处理甚至机器学习优化的协处理单元。这些协处理单元是高度并行的，通常每个单元包含超过80亿个逻辑元件（晶体管）。增加这种复杂性的是对虚拟化的日益依赖，虚拟化隐藏了硬件的特殊性，允许应用程序在几种不同的处理器型号上运行，但使性能更难以分析。 因此，即使是一个简单的操作，如发起电话呼叫，进行Web搜索，路由数据包或显示视频帧，也可能涉及多个处理单元上的许多并行内核，可能在多个服务器上。此外，相同的操作在几秒钟后可能由不同的核心和物理服务器以不同的方式提供服务。因此，了解这些行动的业绩变得极为困难，而且严重缺乏这方面的工具。在这个项目中，用于众核系统的跟踪、监控、分析和调试工具将被扩展，以有效地从从所有层的所有单元中提取信息，从硬件到应用程序，并科普大量（数千个）核。此外，将开发新的方法和算法，以自动分析提取的监测数据。因此，移动的设备、云服务器和其他异构计算系统上的分布式应用程序的设计者和运营商将拥有快速分析其系统性能、自动或手动发现问题并优化操作的工具。