SHF: Small: Collaborative Research: ShapeShifting and PubSub for Tailoring Memory Accesses and Communication in Heterogeneous Multiprocessors

SHF：小型：协作研究：用于定制异构多处理器中的内存访问和通信的 ShapeShifting 和 PubSub

• 批准号: 1117147
• 负责人: Margaret Martonosi
• 金额: $ 22.5万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117147&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; ShapeShifting

Over the past decade or more, microprocessors have faced increasing challenges in achieving high-performance for current and emerging software applications while abiding by severe power and thermal limits. In response, industry has turned to approaches that use specialized graphics and computational hardware and complex memory organizations. The end result is that computer systems have become more heterogeneous and complex, in ways that make it difficult for programmers to write efficient and high-performance software. Software tuned to run on one implementation will often not run at all or will perform poorly or unpredictably when ported to even a different implementation in the same chip family. The objective of this research effort is to design and evaluate system and hardware support that tailors memory and data access/movements to improve performance and power efficiency, while also easing the issues of programmability and of tuning software for individual chip characteristics.The two key themes of this work are Shape Shifting and PubSub data abstractions. ShapeShifting refers to optimizations and hardware support structures that allow data to be transformed in layout, in order to support faster access, more efficient use of memory, and other attributes that improve power and performance. In some preliminary experiments, even a software-only implementation of Shape Shifting improves performance by 15%. Pub Sub data abstractions offer methods for individual processors to indicate interest (or disinterest) in updates regarding other program variables. These abstractions form the underpinning for memory optimizations that are tailored to the application?s memory usage patterns. By mitigating false sharing, encouraging coarse-grained fetches, and reducing coherence broadcasts to uninterested cores, PubSub has the potential to improve the power and performance efficiency of multi-core implementations by a factor of 2X or more.The research program is targeting several types of broad impact. First, the simulators and tools developed by this project will be released as free, open-source software. Second, the results can enhance performance and energy efficiency of future parallel hardware. Energy-efficiency is of particular concern from a national economic and strategic standpoint, given the growing electricity consumption of computer systems and the important role of the memory hierarchy in influencing computer power consumption.

在过去的十年或更长时间里，微处理器在为当前和新兴软件应用实现高性能，同时遵守严格的功耗和热限制方面面临着越来越多的挑战。作为回应，业界已转向使用专用图形和计算硬件以及复杂内存组织的方法。 最终结果是计算机系统变得更加异构和复杂，这使得程序员难以编写高效和高性能的软件。 调整为在一种实现上运行的软件通常根本无法运行，或者当移植到同一芯片系列中的不同实现时，其性能会很差或不可预测。这项研究工作的目标是设计和评估系统和硬件支持，以定制内存和数据访问/移动，以提高性能和功效，同时还缓解可编程性和针对单个芯片特性调整软件的问题。两个关键主题这项工作的重点是 Shape Shifting 和 PubSub 数据抽象。 ShapeShifting 是指允许数据在布局上进行转换的优化和硬件支持结构，以支持更快的访问、更有效地使用内存以及提高功耗和性能的其他属性。 在一些初步实验中，即使仅使用软件实现形状变换也能将性能提高 15%。 Pub Sub 数据抽象为各个处理器提供了指示对其他程序变量的更新感兴趣（或不感兴趣）的方法。 这些抽象构成了根据应用程序的内存使用模式定制的内存优化的基础。通过减少错误共享、鼓励粗粒度获取以及减少对不感兴趣核心的一致性广播，PubSub 有可能将多核实现的功耗和性能效率提高 2 倍或更多。该研究计划针对多种类型具有广泛影响。 首先，该项目开发的模拟器和工具将作为免费的开源软件发布。其次，研究结果可以提高未来并行硬件的性能和能源效率。 鉴于计算机系统电力消耗的不断增长以及存储器层次结构在影响计算机功耗方面的重要作用，从国家经济和战略角度来看，能源效率特别值得关注。