MRI Collaborative: Development of ESPRIT - Emerging systems' performance and energy evaluation instruments and testbench

MRI Collaborative：开发 ESPRIT - 新兴系统的性能和能源评估仪器和测试台

• 批准号: 1828105
• 负责人: Krishna Kavi
• 金额: $ 30万
• 依托单位: University of North Texas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1828105&HistoricalAwards=false
• 关键词: MRI; Collaborative; Development; ESPRIT; Emerging

Future computing nodes will most likely rely on heterogeneous processing and memory systems as well as networking technologies. Identifying the most suitable computing system for a given application requires the cumbersome task of evaluating the application's performance on as many alternatives as possible. This project develops ESPRIT (Emerging Systems PeRformance and Energy Evaluation Instrument and Testbench), a computing system capable of evaluating the most suitable system for specific classes of applications. If applications can be classified into groups based on their similarities along a wide range of performance characteristics, it may be possible to determine the system best suited for a specific class of applications. This work will help large-scale computing systems be configured for more efficient operation and lower energy use.The ESPRIT project consist of state of the art computing nodes; system, memory, and power and energy simulators; benchmarks from different applications; a suite of measuring instruments; models for investigating application behaviors; statistical clustering and other machine learning techniques.The merit of this project resides in the development of instruments to evaluate applications along a number of performance characteristics of behaviors and classifying them into clusters in order to identify the most suitable design for energy efficiencies by varying capacities as well as technology scales. ESPRIT could be used to investigate new design choices, or tune applications for specific designs.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.

未来的计算节点将很可能依赖于异构处理和存储系统以及网络技术。为给定的应用程序确定最合适的计算系统需要尽可能多地评估应用程序的性能。该项目开发ESPRIT（新兴系统PeRégence和能源评估仪器和测试台），这是一种能够评估最适合特定应用类别的系统的计算系统。如果应用程序可以根据它们的相似性沿着广泛的性能特征进行分组，则可以确定最适合特定类别的应用程序的系统。ESPRIT项目包括最先进的计算节点、系统、存储器、电源和能源模拟器、不同应用程序的基准测试、一套测量仪器、调查应用程序行为的模型、一个用于计算系统性能的测试系统、一个用于计算系统性能的测试系统、一个用于计算系统性能的测试系统和一个用于计算系统性能的测试系统。统计聚类和其他机器学习技术。该项目的优点在于开发工具来评估应用程序的沿着一些行为和分类的性能特征将它们分成组群，以便通过不同的能力和技术规模确定最合适的能源效率设计。ESPRIT可用于研究新的设计选择，或调整特定设计的应用程序。该奖项反映了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。

项目成果

MLCNN: Cross-Layer Cooperative Optimization and Accelerator Architecture for Speeding Up Deep Learning Applications

MLCNN：用于加速深度学习应用的跨层协作优化和加速器架构

• 发表时间: 2022
• 期刊: 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS
• 作者: Beilei Jiang, Xianwei Cheng

Hardware accelerator thread for unstructured sparse data processing

用于非结构化稀疏数据处理的硬件加速器线程

• 发表时间: 2022
• 期刊: International Conference on Computer Aided Design
• 作者: Pranathi Vasireddy, Krishna Kavi

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.

Exploration on Task Scheduling Strategy of CPU-GPU Heterogeneous Computing System

CPU-GPU异构计算系统任务调度策略探索

• 发表时间: 2022
• 期刊: 2020 IEEE Computer Society Annual Symposium on VLSI (ISVLSI
• 作者: Juan Fang, Jiaxing Zhang

Towards Application-Specific Address Mapping for Emerging Memory Devices

• DOI: 10.1145/3422575.3422785
• 发表时间: 2020-09
• 期刊: Proceedings of the International Symposium on Memory Systems
• 作者: Shashank Adavally