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

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

• 批准号: 1116673
• 负责人: Kevin Skadron
• 金额: $ 22.5万
• 依托单位: University of Virginia Main Campus
• 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=1116673&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 ShapeShifting 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 ShapeShifting improves performance by 15%-2.8X. PubSub 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，微处理器在满足严格的功率和热量限制的同时，为当前和新兴的软件应用程序实现高性能，面临着越来越多的挑战。作为回应，业界已经转向使用专门的图形和计算硬件以及复杂的内存组织的方法。最终的结果是，计算机系统变得更加异质和复杂，这使得程序员很难编写高效和高性能的软件。调整为在一个实现上运行的软件通常根本不会运行，或者当移植到同一芯片系列中的不同实现时，性能会很差或无法预测。这项研究的目标是设计和评估定制内存和数据访问/移动的系统和硬件支持，以提高性能和电源效率，同时缓解可编程性和针对单个芯片特性调整软件的问题。这项工作的两个关键主题是形状移位和PubSub数据抽象。变形是指允许在布局中转换数据的优化和硬件支持结构，以便支持更快的访问、更高效的内存使用以及其他提高功率和性能的属性。在一些初步实验中，即使是仅由软件实现的形状变换也可以将性能提高15%-2.8倍。PubSub数据抽象为各个处理器提供了表示对有关其他程序变量的更新感兴趣(或不感兴趣)的方法。这些抽象构成了为应用程序量身定做的内存优化的基础-S内存使用模式。通过减少虚假共享，鼓励粗粒度获取，减少对不感兴趣的内核的一致性广播，PubSub有可能将多核实现的能力和性能效率提高2倍或更多。研究计划针对几种类型的广泛影响。首先，该项目开发的模拟器和工具将作为自由、开源软件发布。其次，该结果可以提高未来并行硬件的性能和能效。从国家经济和战略的角度来看，能源效率尤其令人关切，因为计算机系统的电力消耗不断增加，而且存储层次结构在影响计算机电力消耗方面发挥着重要作用。