Collaborative:Balanced Scalable Architectures for Data-Intensive Supercomputing

协作：数据密集型超级计算的平衡可扩展架构

• 批准号: 0937875
• 负责人: H. Howie Huang
• 金额: $ 37.54万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0937875&HistoricalAwards=false
• 关键词: Collaborative; Balanced; Scalable; Architectures; Data

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The nature of scientific computing is changing ? it is becoming increasingly data-centric. We use Amdahl?s Laws to quantify (i) what is a data-intensive computational problem, and (ii) what is a data-intensive computational architecture. Based on these objective metrics we propose several different architectural approaches, including some next-generation, low-power processors and storage devices, e.g., Solid-State Drives (SSDs), and consider how these architectures might provide substantial benefits. In this research, we plan to explore approaches where the first steps of the scientific data processing are performed on the backplane of the database servers ? the closest we can get to low level scientific data. We will also explore how we can use trees and arrays, representing very large scientific data sets, in both relational databases and on a MapReduce/Hadoop like environment. While SSDs can provide an excellent performance for both sequential and random I/Os, we will still need traditional hard disks for the bulk volume. This research will rethink and redesign our existing database clusters and data management systems, as the emergence of these new devices has undoubtedly introduced exciting opportunities for improving not only performance but also energy efficiency. The education and outreach plan in this proposal consists of four tasks: developing educational materials, mentoring underrepresented students, developing collaborations in the industry and public outreach activities. We plan to interleave them to maximize the broader impact on multiple fronts.

该奖项是根据2009年《美国复苏和再投资法案》(公法111-5)提供资金的。科学计算的本质正在改变？它正变得越来越以数据为中心。我们使用Amdahl？S定律来量化(I)什么是数据密集型计算问题，以及(Ii)什么是数据密集型计算体系结构。基于这些客观指标，我们提出了几种不同的架构方法，包括一些下一代低功耗处理器和存储设备，例如固态驱动器(SSD)，并考虑这些架构如何提供实质性优势。在这项研究中，我们计划探索在数据库服务器的底板上执行科学数据处理的第一步的方法。我们能得到的最接近低水平的科学数据。我们还将探索如何在关系数据库和类似MapReduce/Hadoop的环境中使用树和数组来表示非常大的科学数据集。虽然固态硬盘可以为顺序I/O和随机I/O提供出色的性能，但我们仍然需要传统硬盘来存储批量卷。这项研究将重新思考和重新设计我们现有的数据库集群和数据管理系统，因为这些新设备的出现无疑为提高性能和能源效率带来了令人兴奋的机会。该提案中的教育和外展计划包括四项任务：编写教材、指导代表人数不足的学生、发展行业合作和公共外展活动。我们计划将它们相互交错，以最大限度地扩大在多条战线上的更广泛影响。