FoMR: IPC Improvement through Hardware Memorization

FoMR：通过硬件记忆改进 IPC

• 批准号: 1912517
• 负责人: Michael Ferdman
• 金额: $ 20万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1912517&HistoricalAwards=false
• 关键词: FoMR; IPC; Improvement; through; Hardware

Although chip manufacturers continue to increase the number of transistors that can fit into a single computer chip, making use of these additional transistors to improve performance of new computers is increasingly more challenging. In the past several years, the pace of performance improvements of each generation of computer processors have come down, with the traditional techniques for improving performance reaching diminishing returns. This work develops Speculative Memoization, a new technique with potential to more effectively use additional transistors for improving processor performance. Speculative Memoization can enable significant increases in the performance of future computers, increasing user productivity and providing more immersive user experiences.Traditional ways of improving processor performance targeted increasing the sizes of on-chip buffers, such as caches, re-order buffers, and branch predictors. Increasing the buffer sizes offers some performance improvement, but does not reduce the total work done by the processor. Instead, Speculative Memoization offers a radical alternative, identifying repeating work that the processor has already done and avoiding redundant execution, therefore, improving performance by reducing the total amount of work done by the processors. This work explores the limits of performing memoization in hardware, proposes designs for the hardware memoization mechanisms, and works to mitigate the compiler and system software effects on such memorization.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.

尽管芯片制造商不断增加可以装入单个计算机芯片的晶体管的数量，但利用这些额外的晶体管来提高新计算机的性能越来越具有挑战性。在过去的几年中，每一代计算机处理器的性能改进的速度已经下降，用于提高性能的传统技术达到收益递减。这项工作开发了推测记忆，一种新技术，有可能更有效地使用额外的晶体管来提高处理器的性能。推测记忆可以显著提高未来计算机的性能，提高用户的工作效率，并提供更身临其境的用户体验。传统的提高处理器性能的方法旨在增加片上缓冲区的大小，例如高速缓存，重新排序缓冲区和分支预测器。增加缓冲区大小可以提供一些性能改进，但不会减少处理器完成的总工作。相反，推测记忆提供了一个激进的替代方案，识别处理器已经完成的重复工作，避免冗余执行，因此，通过减少处理器完成的工作总量来提高性能。这项工作探讨了执行memoization在硬件上的限制，提出了硬件memoization机制的设计，并致力于减轻编译器和系统软件对这种memotion.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。