CAREER: Reinventing Network-on-Chips of GPU-Accelerated Systems

职业：重塑 GPU 加速系统的片上网络

• 批准号: 2046186
• 负责人: Hui Zhao
• 金额: $ 51.9万
• 依托单位: University of North Texas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046186&HistoricalAwards=false
• 关键词: CAREER; Reinventing; Network; Chips; GPU

Graphics processing units (GPUs) have rapidly evolved to become high-performance accelerators for data-parallel computing. To fully take advantage of the computing power of GPUs, on-chip networks need to provide timely data movement to satisfy the requests of data by the processing cores. Currently, there exists a big gap between the fast-growing processing power of the GPU processing cores and the slow-increasing on-chip network bandwidth. Because of this, GPU-accelerated systems are interconnect-dominated and the on-chip network becomes their performance bottleneck. The emergence of data-intensive applications, such as artificial intelligence, graph analysis, and big data is putting more pressure on the interconnection fabrics. Furthermore, the future of computing beyond Moore’s law and Dennard scaling is moving toward advanced integration of emerging hardware architectures, such as Tensor cores and high bandwidth memories through 2.5D or 3D stacking. Such new hardware architectures also call for effective support from the on-chip networks. The educational contributions of this research include: (1) develop effective strategies for teaching computer systems, create an active learning environment for students and integrate the proposed research results into the curriculum development; (2) integrate the modules and simulators into course projects and mentor students to conduct computer system related research.This research seeks to reinvent on-chip networks for GPU-accelerated systems to remove the communication bottleneck. A major outcome of the project is a set of techniques that enable the development of effective and efficient network-on-chip architectures. The research activities leverage a combination of system modeling, state-of-the-art design methods, and cutting-edge VLSI technologies. The specific research objectives are: (1) develop computational models, benchmarks, and simulator for GPU on-chip networks, with the goal of understanding the communication behavior of GPU applications, especially the recently emerged ones; (2) develop innovative network architectures tailored for GPU-accelerated systems for improved performance and power efficiency; (3) explore the emerging research paradigm of in-network computing to improve the computation/communication efficiency.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.

图形处理单元（gpu）已经迅速发展成为数据并行计算的高性能加速器。为了充分发挥gpu的计算能力，片上网络需要提供及时的数据移动，以满足处理内核对数据的请求。目前，快速增长的GPU处理核心的处理能力与缓慢增长的片上网络带宽之间存在着很大的差距。因此，gpu加速系统以互连为主，片上网络成为其性能瓶颈。人工智能、图形分析、大数据等数据密集型应用的出现，给互联结构带来了更大的压力。此外，超越摩尔定律和登纳德缩放的未来计算正在向新兴硬件架构的高级集成发展，例如通过2.5D或3D堆叠的张量内核和高带宽存储器。这种新的硬件架构也需要片上网络的有效支持。本研究的教育贡献包括：(1)制定有效的计算机系统教学策略，为学生创造积极的学习环境，并将提出的研究成果融入课程开发；(2)将模块和模拟器整合到课程项目中，指导学生进行计算机系统相关研究。这项研究旨在重塑gpu加速系统的片上网络，以消除通信瓶颈。该项目的一个主要成果是一组技术，这些技术能够开发出有效和高效的片上网络架构。研究活动结合了系统建模、最先进的设计方法和尖端的VLSI技术。具体的研究目标是：(1)开发GPU片上网络的计算模型、基准测试和模拟器，目的是了解GPU应用程序的通信行为，特别是最近出现的GPU应用程序；(2)开发为gpu加速系统量身定制的创新网络架构，以提高性能和能效；(3)探索网络内计算的新兴研究范式，提高计算/通信效率。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Improving GPU Throughput through Parallel Execution Using Tensor Cores and CUDA Cores

使用 Tensor 核心和 CUDA 核心通过并行执行提高 GPU 吞吐量

• DOI: 10.1109/isvlsi54635.2022.00051
• 发表时间: 2022
• 期刊: 2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI
• 作者: Ho, Khoa;Zhao, Hui;Jog, Adwait;Mohanty, Saraju
• 通讯作者: Mohanty, Saraju

Task Scheduling Strategy for Heterogeneous Multicore Systems

异构多核系统的任务调度策略

• DOI: 10.1109/mce.2021.3073654
• 发表时间: 2022
• 期刊: IEEE Consumer Electronics Magazine
• 影响因子: 4.5
• 作者: Fang, Juan;Zhang, Jiaxing;Lu, Shuaibing;Zhao, Hui;Zhang, Di;Cui, Yuwen
• 通讯作者: Cui, Yuwen

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

• DOI: 10.1109/ispass57527.2023.00026
• 发表时间: 2023-04
• 期刊: 2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)
• 作者: Zhuren Liu;Shouzhe Zhang;Justin Garrigus;Hui Zhao

Predicting GPU Performance and System Parameter Configuration Using Machine Learning

使用机器学习预测 GPU 性能和系统参数配置

• DOI: 10.1109/isvlsi54635.2022.00056
• 发表时间: 2022
• 期刊: 2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI
• 作者: Liu, Zhuren;Exley, Trevor;Meek, Austin;Yang, Rachel;Zhao, Hui;Albert, Mark V.
• 通讯作者: Albert, Mark V.