CAREER: Addressing Scalability Challenges in Designing Next-generation GPU-Based Heterogeneous Architectures

图形处理单元（GPU）正在成为高性能计算（HPC）、深度学习和虚拟/增强现实等许多领域的默认加速器。它们与高性能多核CPU架构的紧密集成也允许非常高效的异构计算。展望未来，这种基于GPU的系统必须在性能和能效方面进行扩展，以满足未来的兆级（及更高）计算需求。然而，这些系统的持续缩放是具有挑战性的，这主要是因为a）制造单个大管芯提供非常低的产量，使得其过于昂贵，B）存储器层级仍然是关键的性能和能量效率瓶颈，以及c）可编程性和应用可伸缩性受到共享虚拟存储器中的低效率和多该项目旨在通过重新思考未来基于GPU的大规模系统的设计来解决这些可扩展性挑战。特别是，该研究项目围绕三个主要组成部分：a）设计核心空间探索（包括它们的组织）和整个存储器层次结构，B）通过识别然后经由新颖的协同高速缓存和调度技术利用高速缓存局部性来开发数据移动优化技术，以及c）通过增强共享虚拟存储器和多应用执行支持来提高大规模系统资源的资源利用率。所有三个研究组成部分将在新开发的综合评价基础设施上进行评价。这项研究的结果将纳入新的和现有的本科生和研究生课程。预计这项研究所产生的见解将对基于GPU的计算产生长期的积极影响，从而使我们的日常生活更加富有成效。

Graphics processing units (GPUs) are becoming default accelerators in many domains such as high-performance computing (HPC), deep learning, and virtual/augmented reality. Their close integration with high-performance multi-core CPU architectures is also allowing very efficient heterogeneous computing. Going forward, it is imperative that such GPU-based systems scale both in terms of performance and energy efficiency to meet the exascale (and beyond) computing demands of the future. However, sustained scaling of these systems is challenging primarily because a) fabricating a single large die provides very low yield, making it prohibitively expensive, b) memory hierarchy remains a critical performance and energy efficiency bottleneck, and c) programmability and application scalability is hindered by inefficiencies in the shared virtual memory and multi-application support.This project seeks to address these scalability challenges by rethinking the design of future large-scale GPU-based systems. In particular, this research project revolves around three major components: a) design space exploration of cores (including their organization) and the entire memory hierarchy, b) development of data movement optimization techniques by identifying and then exploiting cache locality via novel synergistic caching and scheduling techniques, and c) improving resource utilization of large-scale system resources by enhancing shared virtual memory and multi-application execution support. All three research components will be evaluated on a newly-developed comprehensive evaluation infrastructure. The findings of this research will be incorporated into new and existing undergraduate and graduate courses. It is expected that the insights resulting from this research would have a long-term positive impact on GPU-based computing, thereby making our daily lives more productive.