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

基本信息

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

项目摘要

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.

未来的计算节点很可能会依赖于异质处理和内存系统以及网络技术。为给定应用程序确定最合适的计算系统需要繁琐的任务来评估应用程序在尽可能多的替代方案上的性能。该项目开发了ESPRIT（新兴系统性能和能源评估工具以及测试台），这是一个计算系统，能够评估适用于特定应用程序类别的最合适系统。如果可以根据广泛的性能特征将应用程序分类为组，则可以确定最适合特定类别的应用程序的系统。这项工作将有助于将大规模计算系统配置为更有效的操作和较低的能源使用。ESPRIT项目由最新计算节点的状态组成。系统，记忆，功率和能量模拟器；来自不同应用程序的基准；一套测量工具；研究应用行为的模型；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，以评估行为的许多性能特征，并将其分类为簇，以通过不同的能力和技术量表来确定最合适的能源效率设计。 ESPRIT可用于调查新的设计选择，或调整特定设计的调整应用程序。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响标准，被认为值得通过评估来获得支持。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
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)
影响因子：
0
作者：
Haifa AlQuwaiee;Songlin He;C. Wu;Qiang Tang;Xuewen Shen
通讯作者：
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
影响因子：
0
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
影响因子：
0
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