CAREER:Cross-Core Learning in Future Manycore Systems

职业：未来众核系统中的跨核学习

• 批准号: 1916817
• 负责人: Abhishek Bhattacharjee
• 金额: $ 12.66万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916817&HistoricalAwards=false
• 关键词: CAREER; Cross; Core; Learning; Future; CAREER; Cross; Core; Learning; Future

As computing devices solve increasingly complex and diverse problems, engineers seek to design processors that provide higher performance, while remaining energy-efficient for environmental reasons. To achieve this, processor vendors have embraced manycore devices, where thousands of cores cooperate on a single chip to solve large-scale problems in a parallel manner. They have further incorporated heterogeneity, combining cores with different architectures on a single chip in a bid to provide ever-increasing performance per watt. This project boosts the search for higher energy-efficient performance by inventing novel cross-core learning techniques. Cores in current chips individually learn about the behavior of parallel programs in order to run programs more efficiently in the future, devoting complex and power-hungry hardware structures to do this. However, this research observes that parallel programs tend to exercise the hardware structures of different cores in correlated ways, meaning that the behavior of the program run on one core can be communicated to other cores for various performance and power benefits. As such, this form of intelligent cross-core information exchange is effective in achieving high performance per watt across computing domains from datacenters to embedded systemsIn this light, this research provides techniques to deduce how similarly a parallel program's various threads exercise their cores' hardware structures (looking at a range of different programmer, compiler, and architectural mechanisms to do so). When this is detected, cross-core learning hardware gleans the information that is most useful to exchange to improve performance or power, and then transmits this information among heterogeneous cores using low-overhead hardware/software techniques. This project develops a lightweight runtime software layer to orchestrate this information exchange, relying on dedicated hardware support when necessary. Through developing this framework, cross-core learning is applied to a number of specific cases, ranging from higher-performance manycore cache prefetching and branch prediction, to performance and power-management techniques for interrupts and exceptions in scale-out systems, as well as thread and instruction scheduling. Furthermore, this project heavily disseminates knowledge on how to design and program large-scale manycore systems (or scale-out systems) by involving students at the graduate, undergraduate, and high-school levels through active research and coursework. Overall, this work impacts the engineering community and broader society by: (1) helping to achieve high-performance, but also energy-efficient and environmentally-friendly computing systems; (2) providing academics and chip designers a design methodology and infrastructure to study manycore design; (3) broadening the participation of underrepresented groups in computer science; (4) educating graduate, undergraduate, and high-school students on parallel programming for manycore systems.

随着计算设备解决越来越复杂和多样化的问题，工程师试图设计提供更高性能的处理器，同时出于环境原因保持节能。为了实现这一目标，处理器供应商采用了许多核心设备，其中成千上万的核心在单个芯片上合作，以平行的方式解决大规模问题。他们进一步融合了异质性，将核心与单个芯片上的不同架构结合在一起，以提供每瓦的越来越多的性能。该项目通过发明新颖的跨核学习技术来增强对更高节能性能的搜索。当前芯片中的核心单独了解并行程序的行为，以便将来更有效地运行程序，从而致力于复杂而渴望的硬件结构。但是，这项研究观察到，并行程序倾向于以相关的方式行使不同核心的硬件结构，这意味着该程序在一个核心上运行的行为可以传达给其他核心，以获得各种性能和功率好处。因此，这种形式的智能跨核信息交换可以有效地在从数据中心到嵌入式系统中的计算域中每瓦的高性能实现高性能，这项研究提供了推断平行程序的各种线程的类似方法，以推断出核心的硬件结构（查看不同程序员，编译器，编译器，构造机构的范围）。当检测到这一点时，跨核学习硬件会收集最有用的信息，以提高性能或功率，然后使用低超过头的硬件/软件技术在异质核心之间传输此信息。该项目开发了一个轻巧的运行时软件层来协调此信息交换，并在必要时依靠专用的硬件支持。通过开发此框架，将跨核学习应用于许多特定情况，范围从较高性能的许多核心缓存预取预约和分支预测，再到规模范围系统中的中断和异常的性能和功率管理技术，以及线程和指令计划。 此外，该项目通过积极的研究和课程与研究生，本科生和高中级别的学生参与，大量传播了有关如何设计和编程大规模多核系统（或扩展系统）的知识。总体而言，这项工作会影响工程社区和更广泛的社会：（1）帮助实现高性能，但也可以节能且环保的计算系统； （2）为学者和芯片设计师提供设计方法和基础架构来研究Manuscore设计； （3）扩大代表性不足的小组参与计算机科学的参与； （4）教育毕业生，本科和高中生，以针对许多核心系统进行平行编程。