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

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 domains

are 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-accuracy

trade-offs. The research consists of three intertwined tasks. The first task would examine a variety of

high performance computing (HPC) and MapReduce style data analytic applications, and determine

which classes of applications are suitable for soft computing.

The second component of the research is aimed at developing appropriate techniques for facilitating

soft computing, while the last task focuses on examining the possibility of developing a control theoretic

model for formalizing the various tradeoff analysis.

This project aims at demonstrating that it is possible to achieve significant power and performance

gain 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 many

classes 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 teaching

existing and new courses, and will be made publicly available.