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.

计算中的不准确性通常被认为是负面的，因此，传统的计算系统被设计成具有严格的正确性概念。然而，不准确性或近似性并不总是坏的，因为几个应用领域本质上容忍不同程度的准确性放松，因此，这样的属性可以被开发用于在应用性能或容错方面的显著增益。这个EAGER项目的动机是调查利用这样的近似的可行性，也称为“软计算”，用于预测性能-功率-准确性权衡的数据密集型应用。这项研究包括三个相互交织的任务。第一个任务是研究各种高性能计算（HPC）和MapReduce风格的数据分析应用程序，并确定哪些应用程序适合软计算。研究的第二个组成部分旨在开发适当的技术来促进软计算，而最后一项任务则集中于研究开发一个控制理论模型的可能性，以形式化各种权衡分析。证明了通过软计算可以为各种数据密集型应用程序实现显著的功率和性能增益。本研究采用的方法有可能影响许多科学和商业应用程序的编程范式，以优化功耗-性能行为。本研究的交叉性质有可能在多个领域培育新的研究方向，包括高性能计算，计算机体系结构，编译器，和系统/应用软件。参与本研究的本科生和研究生将在几个领域得到多方面的培训。本研究中开发的软件工具将用于现有和新课程的教学，并将公开发布。