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: 1828123
• 负责人: Chase Wu
• 金额: $ 10万
• 依托单位: New Jersey Institute of Technology
• 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 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1828123&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不同的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评估该应用程序在尽可能多的备选方案上的性能这一繁琐的任务。该项目开发了ESPRIT(新兴系统性能和能量评估仪器和测试台)，这是一个能够评估最适合特定应用类别的系统的计算系统。如果可以根据应用程序沿着广泛的性能特征的相似性将其分类为组，则有可能确定最适合特定应用程序类别的系统。这项工作将帮助大规模计算系统配置为更高效的运行和更低的能源使用。ESPRIT项目包括最先进的计算节点；系统、内存和功率和能源模拟器；来自不同应用程序的基准测试；一套测量仪器；用于调查应用程序行为的模型；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，根据行为的许多性能特征对应用程序进行评估，并将它们分类为集群，以便通过不同的能力和技术规模确定最适合的能效设计。ESPRIT可用于调查新的设计选择，或调整特定设计的应用程序。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Throughput optimization for Storm-based processing of stream data on clouds

基于Storm的云上流数据处理吞吐量优化

• DOI: 10.1016/j.future.2020.06.009
• 发表时间: 2020
• 期刊: Future Generation Computer Systems
• 作者: Cao, Huiyan;Wu, Chase Q.;Bao, Liang;Hou, Aiqin;Shen, Wei
• 通讯作者: Shen, Wei

On Distributed Information Composition in Big Data Systems

• DOI: 10.1109/escience.2019.00025
• 发表时间: 2019-09
• 期刊: 2019 15th International Conference on eScience (eScience)
• 作者: Haifa AlQuwaiee;Songlin He;C. Wu;Qiang Tang;Xuewen Shen

Performance Modeling and Prediction of Big Data Workflows: An Exploratory Analysis

大数据工作流的性能建模和预测：探索性分析

• DOI: 10.1109/icccn49398.2020.9209715
• 发表时间: 2020
• 期刊: 2020 29th International Conference on Computer Communications and Networks (ICCCN
• 作者: Liu, Wuji;Wu, Chase Q.;Ye, Qianwen;Hou, Aiqin;Shen, Wei
• 通讯作者: Shen, Wei

Machine Learning-assisted Computational Steering of Large-scale Scientific Simulations

大规模科学模拟的机器学习辅助计算指导

• DOI: 10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00138
• 发表时间: 2021
• 期刊: Proceedings of the 19th IEEE International Symposium on Parallel and Distributed Processing with Applications
• 作者: Liu, W.;Ye, Q.;Wu, C.Q.;Liu, Y.;Zhou, X.;Shan, Y.
• 通讯作者: Shan, Y.

Exploratory analysis and performance prediction of big data transfer in High-performance Networks

• DOI: 10.1016/j.engappai.2021.104285
• 发表时间: 2021-06
• 期刊: Eng. Appl. Artif. Intell.
• 作者: Daqing Yun;Wuji Liu;C. Wu;N. Rao;R. Kettimuthu