FoMR: Collaborative Research: Single-Thread Multi-Accelerator Execution to Close the Dennard Scaling Gap

FoMR：协作研究：单线程多加速器执行以缩小 Dennard 缩放差距

• 批准号: 1823447
• 负责人: Karthikeyan Sankaralingam
• 金额: $ 17.5万
• 依托单位: University of Wisconsin-Madison
• 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 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1823447&HistoricalAwards=false
• 关键词: FoMR; Collaborative; Research; Single; Thread

With ever-slowing scaling trends in microprocessor technologies, traditional techniques of improving processor performance are no longer viable, and achieving higher performance requires a dramatically different approach. This project develops a multicore chip microarchitecture using specialized accelerators and code-injection techniques without needing to modify user-level software, compilers and operating systems. The impact of this research is to help steer microprocessor design in novel ways that can help sustain performance improvements, especially for datacenter and big-data computing. This project builds on a recent promising technique involves offloading program phases onto specialized processors (accelerators) which are tuned to execute programs with specific characteristics (i.e., parallelism, control dependence, memory behavior) at extremely high efficiency. There are two main challenges which motivate the major thrusts of this work. The first is the question of how to design a practical system for managing the execution of heterogeneous accelerators and dynamic translation. The second is how to design a set of accelerators which provide integer factors of improvement over general purpose processors' performance and energy efficiency. This work addresses the first challenge by designing a disaggregated translation subsystem, including region detection hardware at each core, a set of disaggregated compiler cores and a translation cache, and a hardware/software layer which dynamically re-maps logical threads to physical cores based on dynamic code properties and load balancing. To design a set of balanced accelerators, this work is analyzing programs to identify key program behaviors, and developing targeted accelerators for each. Finally, this includes the design of synthesis-time resource allocation algorithms which will co-optimize the choice of cache interface, general core attributes, and accelerator execution model.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.

随着微处理器技术的不断放缓，提高处理器性能的传统技术不再可行，实现更高的性能需要一个截然不同的方法。该项目开发了一个多核芯片微架构，使用专门的加速器和代码注入技术，无需修改用户级软件、编译器和操作系统。这项研究的影响是帮助以新颖的方式引导微处理器设计，以帮助维持性能改进，特别是对于数据中心和大数据计算。这个项目建立在一个最近很有前途的技术上，涉及到将程序阶段卸载到专门的处理器（加速器）上，这些处理器被调优以极高的效率执行具有特定特征（即并行性、控制依赖性、内存行为）的程序。有两个主要的挑战激发了这项工作的主要推动力。第一个问题是如何设计一个实用的系统来管理异构加速器和动态翻译的执行。第二是如何设计一套加速器，提供比通用处理器的性能和能源效率的整数因子的改进。这项工作通过设计一个分解的翻译子系统来解决第一个挑战，包括每个核心的区域检测硬件，一组分解的编译器核心和翻译缓存，以及一个硬件/软件层，该层基于动态代码属性和负载平衡动态地将逻辑线程重新映射到物理核心。为了设计一套平衡的加速器，本工作是分析程序以确定关键程序行为，并为每个程序开发有针对性的加速器。最后，设计了综合时间资源分配算法，该算法将共同优化缓存接口的选择、通用核心属性和加速器执行模型。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。