Work

A Mathematical Method for Online Autotuning of Power and Energy Consumption with Corrected Temperature Effects

Year: 2013

Type: conference-paper

Abstract: This paper discusses a method of online autotuning of power or energy consumption. A problem of online power modeling is the strong positive influence of the temperature on the power consumption. Without proper treatment of the temperature, an autotuning mechanism will misunderstand a variant measured at a low temperature to be power-efficient. This paper proposes a model of the relation between power and temperature, a Bayesian inference formula to estimate the power consumption with estimates of estimation errors, and an experimental design (i.e. algorithm of choosing variants) for online autotuning. The proposed methods are evaluated by simulation, and applied to online power and energy optimizations.

Source: Procedia Computer Science

DOI: 10.1016/j.procs.2013.05.297

Authors: Reiji Suda, Luo Cheng, Takahiro Katagiri

Institution: The University of Tokyo

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: diamond

Funders: Japan Society for the Promotion of Science, Core Research for Evolutional Science and Technology