CSR: Small: Efficient Many-core Execution Models for Cognitive Computing

CSR：小型：用于认知计算的高效多核执行模型

• 批准号: 1718481
• 负责人: Omer Khan
• 金额: $ 30万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718481&HistoricalAwards=false
• 关键词: CSR; Small; Efficient; Many; core

Society is increasingly dependent on digital machines due to miniaturization and seamless integration of mobile, cloud and network computing. This project's objective is to develop an understanding of the computational algorithms, and devise novel architectural methods that improve the efficiency of the devices and platforms that will execute cognitive computing problems. Achieving good performance and scalability for concurrent hardware has been widely acknowledged to be a hard and important problem. This project singles out communication as a grand challenge and proposes novel methods for systems ranging from single chips with many "cores" or processors, through tightly integrated and increasingly heterogeneous futuristic many-core machines. The proposed research agenda provides for significant broader impacts related to (1) curriculum development and student training through industry collaborations and integration of cognitive computing in computer architecture curriculum, and (2) outreach through established Research Experiences for Undergraduates (REU) sites and supplement programs.A set of auxiliary communication models are proposed to give the programmer new mechanisms based on hardware-supported explicit messaging to exploit fine-grained parallelism in the algorithms that represent cognitive computing problems. The resulting architecture and software concurrency choices are expected to expose many problem-algorithm-input-machine configurations, and solving for a near-optimal configuration in real-time is a hard problem. A novel situationally adaptive execution model is proposed that analyzes and captures this massive search space to pick the right choices in the spatiotemporally changing runtime settings. The incorporation of auxiliary communication models in futuristic multicore and tightly-coupled many-core processors is timely as it will enable performance scaling trends to continue at ultra-energy efficiency: enterprises and US mission agencies will be able to deploy multicore technology in real-time embedded systems, such as self-driving cars. As a broader impact, better solutions for emerging cognitive computing problems will help improve our cyber infrastructure, healthcare, and manufacturing, all with significant benefits to the US economy and society.

由于移动、云计算和网络计算的小型化和无缝集成，社会越来越依赖数字机器。这个项目的目标是加深对计算算法的理解，并设计出新颖的架构方法，以提高执行认知计算问题的设备和平台的效率。实现并发硬件的良好性能和可扩展性一直被广泛认为是一个困难而重要的问题。该项目将通信列为一项重大挑战，并提出了新的方法，适用于从具有多个“核心”或处理器的单芯片到紧密集成且日益多样化的未来主义多核机器的各种系统。提出的研究议程提供了与以下相关的重要的更广泛的影响：(1)通过行业合作和将认知计算整合到计算机体系结构课程中的课程开发和学生培训，以及(2)通过已建立的本科生研究经验(REU)站点和补充程序的扩展。提出了一套辅助通信模型，以基于硬件支持的显式消息传递为程序员提供新的机制，以利用代表认知计算问题的算法中的细粒度并行性。由此产生的体系结构和软件并发选择预计会暴露许多问题-算法-输入机配置，而实时解决接近最佳的配置是一个困难的问题。提出了一种新的情境自适应执行模型，该模型能够分析和捕捉这种巨大的搜索空间，以便在时空变化的运行环境中选择正确的选择。在未来的多核和紧耦合的多核处理器中纳入辅助通信模型是及时的，因为它将使性能扩展趋势在超高能效下继续：企业和美国任务机构将能够在实时嵌入式系统(如自动驾驶汽车)中部署多核技术。作为更广泛的影响，针对新出现的认知计算问题的更好解决方案将有助于改善我们的网络基础设施、医疗保健和制造业，所有这些都将给美国经济和社会带来重大好处。

项目成果

Exploring accelerator and parallel graph algorithmic choices for temporal graphs

• DOI: 10.1145/3380536.3380540
• 发表时间: 2020-02
• 期刊: Proceedings of the Eleventh International Workshop on Programming Models and Applications for Multicores and Manycores
• 作者: Akif Rehman;Masab Ahmad;O. Khan

In-Hardware Moving Compute to Data Model to Accelerate Thread Synchronization on Large Multicores

硬件内将计算转移到数据模型以加速大型多核上的线程同步

• DOI: 10.1109/mm.2019.2955079
• 发表时间: 2020
• 期刊: IEEE Micro
• 影响因子: 3.6
• 作者: Ahmad, Masab;Dogan, Halit;Joao, Jose A.;Khan, Omer
• 通讯作者: Khan, Omer

An Efficient Algorithm for the Construction of Dynamically Updating Trajectory Networks

一种构建动态更新轨迹网络的高效算法

• 发表时间: 2021
• 期刊: IEEE Conference on High Performance Extreme Computing
• 作者: Gurevin, Deniz;Michael, Chris J.;Khan, Omer
• 通讯作者: Khan, Omer

HeteroMap: A Runtime Performance Predictor for Efficient Processing of Graph Analytics on Heterogeneous Multi-Accelerators

• DOI: 10.1109/ispass.2019.00039
• 发表时间: 2019-03
• 期刊: 2019 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)
• 作者: Masab Ahmad;H. Dogan;Christopher J. Michael;O. Khan

Accelerating Synchronization in Graph Analytics Using Moving Compute to Data Model on Tilera TILE-Gx72

在 Tilera TILE-Gx72 上使用将计算移动到数据模型来加速图形分析中的同步

• DOI: 10.1109/iccd.2018.00080
• 发表时间: 2018
• 期刊: 2018 IEEE 36th International Conference on Computer Design (ICCD
• 作者: Dogan, Halit;Ahmad, Masab;Joao, Jose;Khan, Omer
• 通讯作者: Khan, Omer