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: $ 18.5万
• 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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=694684
• 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，通信和计算基础架构一直在发展，变得更加高效，复杂，集成和网络。较新的移动设备（包括智能机器人或自动驾驶汽车）和服务器通常在其中央处理单元中包含8个或更多核心。这些系统基于异构处理器，具有有效的传统中央处理单元，但也具有针对图形优化（具有数千个内核的GPGPU），网络，信号处理甚至机器学习的协调单元。这些协调单元高度平行，并且通常包含超过80亿个逻辑元素（晶体管）。加上这种复杂性的是对虚拟化的依赖越来越多，它隐藏了硬件的特殊性，从而使应用程序可以在几种不同的处理器模型上运行，但使得性能更加难以分析。结果，即使是一个简单的操作，例如启动电话，进行网络搜索，路由数据包或显示视频框架，也可能涉及多个处理单元上的许多并行内核，可能是在多个服务器上。此外，几秒钟后的同一操作可以由不同的核心和物理服务器以不同的方式提供。因此，了解这些操作的性能变得非常困难，并且严重缺乏该目的的工具。在此项目中，将扩展到许多核系统的跟踪，监视，分析和调试工具，以从所有层中的所有单元（从硬件到应用程序）中的所有单元中有效提取信息，并应对大量（数千个）内核。此外，将开发新的方法和算法来自动化提取的监视数据的分析。结果，在移动设备，云服务器和其他异质计算系统上分布式应用程序的设计师和运营商将拥有手中的工具来快速分析其系统性能，自动或手动发现问题并优化操作。