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.

在过去的十年或以上，微处理器在遵守严重的功率和热限制的同时，在为当前和新兴软件应用方面实现高性能方面面临着越来越多的挑战。作为回应，行业转向使用专业图形和计算硬件和复杂内存组织的方法。 最终结果是，计算机系统已经变得更加异构和复杂，使程序员很难编写高效且高性能的软件。 在一个实施中运行的软件通常根本不会运行，或者在同一芯片家族中甚至具有不同的实现时，也会表现不佳或不可预测。这项研究工作的目的是设计和评估系统和硬件支持，以量身定制内存和数据访问/动作，以提高性能和功率效率，同时还可以简化可编程性和为单个芯片特征调整软件的问题。这项工作的两个关键主题是Shape Shape Shape Shaphing Shifting和PubSub数据摘要。 Shapeshifting是指允许在布局中转换数据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其他改善功率和性能的属性。 在某些初步实验中，即使是仅软件的形状转换实现，也可以提高15％的性能。 Pub子数据抽象为单个处理器提供方法，以指示有关其他程序变量的更新中的兴趣（或不感兴趣）。 这些抽象形成了针对应用程序的内存使用模式量身定制的内存优化的基础。通过减轻虚假共享，鼓励粗粒的提取物以及将连贯广播的广播减少到无趣的核心中，PubSub有可能提高多核实施的功率和性能效率的2倍或更多倍。研究计划针对多种类型的广泛影响。 首先，该项目开发的模拟器和工具将作为免费的开源软件发布。其次，结果可以提高未来并行硬件的性能和能源效率。 从国家经济和战略的角度来看，鉴于计算机系统的电力越来越多以及记忆层次结构在影响计算机功耗中的重要作用，从国家经济和战略的角度来看，能源效率尤其引起人们的关注。