%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/

%% Created for Florina M. Ciorba at 2016-01-14 09:54:08 +0100 


%% Saved with string encoding Unicode (UTF-8) 



@inbook{banicescu:2013:a,
	Abstract = {In this chapter an effective approach for improving the performance of scientific applications via autonomic computing is described. A short overview of the chapter is given in Section 1.1, while scientific applications and their need for performance optimization are discussed in Section 1.2. Achieving load balancing via dynamic loop scheduling, a central topic for the performance of scientific applications, is defined in Section 1.3. The basic concepts of machine learning and motivating factors for using reinforcement learning to optimize the performance of scientific applications are introduced in Section 1.4. This also includes an overview of relevant existing work. The use of an autonomic computing approach for optimizing the performance of scientific applications, based on reinforcement learning, is described in Section 1.5. It is followed by an example of its effective use for optimizing the performance of wavepacket simulations, a computationally intensive scientific application. The experimental setup and the obtained results are discussed in Section 1.6. Insights into future work, open problems, and conclusions are summarized in Section 1.7.},
	Author = {Banicescu, Ioana and Ciorba, Florina M. and Srivastava, Srishti},
	Chapter = {Performance Optimization of Scientific Applications using an Autonomic Computing Approach},
	Date-Added = {2016-01-14 08:53:06 +0000},
	Date-Modified = {2016-01-14 08:54:05 +0000},
	Number = {Chapter 22},
	Pages = {437-466},
	Publisher = {John Wiley&Sons, Inc.},
	Title = {Scalable Computing: Theory and Practice},
	Year = {2013}}