Application Characterization for Adaptive Computing Platform Determination for Computational and Data-Enabled Science and Engineering

计算和数据支持的科学与工程的自适应计算平台确定的应用表征

• 批准号: 1404981
• 负责人: Haiying Shen
• 金额: $ 69.98万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-05-01 至 2016-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1404981&HistoricalAwards=false
• 关键词: Application; Characterization; Adaptive; Computing; Platform

Traditional high performance computing clusters (HPCC) and Hadoop clusters are both important platforms in computational and data-enabled science and engineering (CDS&E). Hadoop MapReduce is typically effective in large-scale data analysis while traditional HPC is more commonly employed in computational problems (petabytes vs. petaflops). HPCC can include a shim layer that allows Hadoop MapReduce to access HPC storage (denoted by Hadoop+HPCC), local storage (denoted by Hadoop), and a combination of both (denoted by Hadoop/HPCC). This project aims to identify the characteristics of various CDS&E MapReduce computational tasks, and adaptively determine the best platform (Hadoop, Hadoop+HPCC and Hadoop/HPCC) for individual applications based on their characteristics, and also optimally arrange data placement between local storage and dedicated remote storage, given performance objectives and system cost metrics. Broader impacts include critical insights into the suitability of different computing platforms to different CDS&E applications, and a more advanced HPC system. Research results will be disseminated through technology transfer to industry partners, via publication in peer review journals, and in software releases. Results will also serve as catalyst for research in cyberinfrastructure, which serves the CDS&E fields. This project will provide thorough training of students and collaborative research opportunities for participating Clemson graduates, undergraduates, faculty, and K-12 students. Results will be integrated into courses taught by the PIs. The PIs will recruit new students, particularly those from underrepresented groups, to undertake the study of a STEM discipline.

传统的高性能计算簇（HPCC）和Hadoop群集既是计算和数据支持科学与工程（CDS＆E）的重要平台。 Hadoop MapReduce通常在大规模数据分析中有效，而传统的HPC则更常用于计算问题（Petabytes vs. Petaflops）。 HPCC可以包括一个垫片，该层允许Hadoop MapReduce访问HPC存储（由Hadoop+HPCC表示），本地存储（由Hadoop表示）和两者的组合（由Hadoop/HPCC表示）。该项目旨在确定各种CDS＆E MAPREDUCE计算任务的特征，并根据其特征自适应地确定最佳平台（Hadoop，Hadoop+HPCC和Hadoop/HPCC），以根据其特征来确定各个应用程序，并最佳地在本地存储和专用的远程存储之间安排数据放置，鉴于性能性能目标和系统成本量表。更广泛的影响包括对不同计算平台对不同CDS＆E应用程序的适用性以及更先进的HPC系统的关键见解。研究结果将通过技术转移到行业合作伙伴，通过同行评审期刊和软件版本的出版物来传播。结果还将作为网络基础结构研究的催化剂，该结构为CDS＆E领域提供服务。该项目将为参与Clemson的毕业生，本科生，教职员工和K-12学生提供彻底培训和协作研究机会。结果将集成到PIS教授的课程中。 PI将招募新学生，尤其是来自代表性不足的学生，以进行STEM学科的研究。