Year: 2014
Type: article
Abstract: Nowadays, the same piece of code should run on different architectures, providing performance guarantees in a variety of environments and situations. To this end, designers often integrate existing systems with ad-hoc adaptive strategies able to tune specific parameters that impact performance or energy—for example, frequency scaling. However, these strategies interfere with one another and unpredictable performance degradation may occur due to the interaction between different entities. In this article, we propose a software approach to reconfiguration when different strategies, called loops , are encapsulated in the system and are available to be activated. Our solution to loop coordination is based on machine learning and it selects a policy for the activation of loops inside of a system without prior knowledge. We implemented our solution on top of GNU/Linux and evaluated it with a significant subset of the PARSEC benchmark suite.
DOI: 10.1145/2611563
Institutions: Polytechnique Montréal, Lund University, Politecnico di Milano
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2159176014