CISE:CNS:EAGER: Exploring Managed Soft Computing for Data Intensive Applications

CISE:CNS:EAGER：探索数据密集型应用的托管软计算

• 批准号: 1152479
• 负责人: Chitaranjan Das
• 金额: $ 30万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1152479&HistoricalAwards=false
• 关键词: CISE; CNS; EAGER; Exploring; Managed

Inaccuracy in computation has usually been considered with a negative connotation and, therefore,conventional computing systems have been designed with a strict notion of correctness.However, inaccuracy or approximation is not always bad since several application domainsare intrinsically tolerant to varying degrees of relaxation in accuracy,and thus, such a property can be exploited for significant gain in application performance or fault-tolerance.The motivation of this EAGER project is to investigate the feasibility of utilizing such approximation,also known as "soft computing", for data-intensive applications for predicting the performance-power-accuracytrade-offs. The research consists of three intertwined tasks. The first task would examine a variety ofhigh performance computing (HPC) and MapReduce style data analytic applications, and determinewhich classes of applications are suitable for soft computing.The second component of the research is aimed at developing appropriate techniques for facilitatingsoft computing, while the last task focuses on examining the possibility of developing a control theoreticmodel for formalizing the various tradeoff analysis.This project aims at demonstrating that it is possible to achieve significant power and performancegain for a wide variety of data intensive applications through soft computing. The approach adopted in this research has the potential to influence the programming paradigm for manyclasses of scientific and business applications for optimizing the power-performance behavior.The cross-cutting nature of this research has potential to foster new research directions in several areas,spanning high performance computing, computer architecture, compilers, and system/application software.Undergraduate and graduate students involved in this research will get versatile training in several areas.The software tools developed in this research will be used in teachingexisting and new courses, and will be made publicly available.

计算中的不精确度通常被认为具有负面的含义，因此，传统计算系统的设计具有严格的正确性概念。然而，不精确度或近似值并不总是糟糕的，因为几个应用领域本质上容忍不同程度的精度松弛，因此，可以利用这种特性来显著提高应用程序的性能或容错性。这个迫切的项目的动机是研究在数据密集型应用程序中使用这种近似值(也称为“软计算”)来预测性能-功率-精确度权衡的可行性。这项研究由三个相互交织的任务组成。第一项任务将检查各种高性能计算(HPC)和MapReduce式数据分析应用程序，并确定哪类应用程序适合于软计算。第二项研究的目的是开发适当的技术来促进软计算，而最后一项任务重点是研究开发一种控制理论模型的可能性，以形式化各种权衡分析。该项目旨在证明，通过软计算可以为各种数据密集型应用程序实现显著的能力和性能。这项研究中采用的方法有可能影响许多科学和商业应用程序类的编程范例，以优化性能行为。这项研究的交叉性质有可能在几个领域培养新的研究方向，包括高性能计算、计算机体系结构、编译器和系统/应用软件。参与这项研究的本科生和研究生将在几个领域接受全方位的培训。本研究开发的软件工具将用于现有课程和新课程的教学，并将公开提供。