By Topic

Improving power efficiency of dense linear algebra algorithms on multi-core processors via slack control

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Alonso, P. ; Dept. de Sist. Informaticos y Comput., Univ. Politec. de Valencia, Valencia, Spain ; Dolz, M.F. ; Mayo, R. ; Quintana-Orti, E.S.

This paper addresses the efficient exploitation of task-level parallelism, present in many dense linear algebra operations, from the point of view of both computational performance and energy consumption. In particular, we consider a procedure, the Slack Reduction Algorithm (SRA), to optimize the execution frequency of a collection of tasks (in which many dense linear algebra algorithms can be decomposed) on multi-core architectures. The results from this procedure are modulated by an energy-aware simulator, which is in charge of scheduling/mapping the execution of these tasks to the cores, leveraging dynamic frequency voltage scaling featured by current technology. Simultaneously, the simulator evaluates the performance benefits of the solution. Experiments with these tools show significant energy gains for two key dense linear algebra operations: the Cholesky and QR factorizations.

Published in:

High Performance Computing and Simulation (HPCS), 2011 International Conference on

Date of Conference:

4-8 July 2011