DocumentCode
1310711
Title
Dynamic Multicore Resource Management: A Machine Learning Approach
Author
Martínez, José F. ; Ipek, Engin
Author_Institution
Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA
Volume
29
Issue
5
fYear
2009
Firstpage
8
Lastpage
17
Abstract
A machine learning approach to multicore resource management produces self-optimizing on-chip hardware agents capable of learning, planning, and continuously adapting to changing workload demands. Machine learning is the study of computer programs and algorithms that learn about their environment and improve automatically with experience.This approach thus contrasts with today´s predominant approach of directly specifying at design time how the hardware should accomplish the desired goal. This results in more efficient and flexible management of critical hardware resources at runtime.
Keywords
learning (artificial intelligence); microprocessor chips; multiprocessing systems; planning (artificial intelligence); computer algorithm; computer program; critical hardware resource; dynamic multicore resource management; flexible management; machine learning approach; planning approach; self-optimizing on-chip hardware agent; Algorithm design and analysis; Hardware; Machine learning; Machine learning algorithms; Multicore processing; Resource management; Runtime; dynamic resource management; machine learning.; multicore;
fLanguage
English
Journal_Title
Micro, IEEE
Publisher
ieee
ISSN
0272-1732
Type
jour
DOI
10.1109/MM.2009.77
Filename
5325152
Link To Document