CRII: SHF: Investigation of Effective On-chip Network Designs for GPUs

CRII：SHF：有效的 GPU 片上网络设计研究

• 批准号: 1566637
• 负责人: Lizhong Chen
• 金额: $ 17.5万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1566637&HistoricalAwards=false
• 关键词: CRII; SHF; Investigation; Effective; chip

Graphics Processing Units (GPUs) have been proliferating at an extraordinary speed in the past decade. Continuing innovations in related technologies allow today?s GPUs to play critical roles in numerous disciplines and sectors as well as many emerging fields that might not otherwise be possible. Examples include processing ambient video inputs in automobiles for enhanced safety and intelligent driving; powering graphics-based medical processing applications in mobile devices for ubiquitous biometric monitoring and personalized healthcare; supporting virtual reality headsets for transformative and immersive new experiences in education, training, and entertainment; and providing energy-efficient parallel computing in HPC systems and data-centers to facilitate a myriad of scientific, economic, and social computing applications. Such promising developments are enabled by the massively parallel computing capacity of GPU architectures, which can integrate thousands of processing cores on a single chip. To continue meeting growing performance expectations, on-chip interconnect architectures must be developed to provide fast and efficient communications among the vast number of processing cores in GPUs.This research investigates cross-cutting approaches and techniques to improve the effectiveness of on-chip networks (or NoCs) in GPU systems. The objective is to fully explore the challenges and develop framework useful for GPU NoC designs that will meet the performance, energy, and resource efficiency targets of current and future GPU systems. Among some of the specific aspects investigated are the bottlenecks of NoCs in the GPU context, alternative methods of enabling scale-up, sensitivity of NoCs to various types of GPU applications, and the impact of NoCs on GPU system-level trade-offs. This research also investigates opportunities in coordinated design among NoC components as well as co-optimizations between NoCs and other GPU subsystems. The objective is to enable on-chip networks to operate more consistently and efficiently for the overall benefit of GPU systems by factoring in multiple components and key application characteristics. Beyond its specific technical contributions to fundamental advancements in computing, this research has broader potential impact to society through its activities on research education and outreach that aim to broaden participation for people from diverse background, including groups underrepresented in engineering at various education levels.

在过去的十年中，图形处理单元（GPU）以非凡的速度增殖。相关技术的持续创新允许当今的GPU在众多学科和部门以及许多可能是不可能的新兴领域中扮演关键角色。示例包括在汽车中处理环境视频输入，以增强安全性和智能驾驶；在移动设备中为基于图形的医疗处理应用提供动力，以用于无处不在的生物识别监测和个性化医疗保健；支持虚拟现实耳机，以在教育，培训和娱乐方面进行变革和沉浸式的新体验；并在HPC系统和数据中心提供节能的并行计算，以促进无数的科学，经济和社会计算应用程序。 GPU体系结构的大量平行计算能力可以实现此类有希望的发展，这些计算能力可以集成一个芯片上的数千个处理核心。为了继续满足不断增长的绩效期望，必须开发芯片互连体系结构，以在GPU中的大量加工核心之间提供快速有效的通信。这项研究研究了跨切割方法和技术，以提高GPU系统中芯片网络（或NOC）的有效性。目的是充分探索挑战并开发框架对GPU NOC设计有用，这些设计将满足当前和未来GPU系统的性能，能源和资源效率目标。在研究的一些具体方面包括在GPU环境中NOC的瓶颈，促进扩大规模的替代方法，NOC对各种GPU应用的敏感性以及NOC对GPU系统级别权衡的影响。这项研究还调查了NOC组件之间协调设计的机会，以及NOCS和其他GPU子系统之间的合作量。目的是通过考虑多个组件和关键应用程序特征来使片上网络更加一致，有效地为GPU系统的整体收益。除了其对计算方面的基本进步的具体技术贡献外，这项研究还通过其在研究教育和推广方面的活动对社会产生了更大的潜在影响，旨在扩大各种背景的人的参与，包括在各种教育水平上代表性不足的团体。