Energy Efficient Block-Partitioned Multicore Processors for Parallel Applications

Date

2010-07

Authors

Qi, Xuan
Zhu, Dakai

Journal Title

Journal ISSN

Volume Title

Publisher

UTSA Department of Computer Science

Abstract

Energy management has become an important research area in the last decade. As an energy efficient architecture, multicore has been widely adopted. However, with the number of cores on a single chip continuing to increase, it has been a grand challenge to effectively manage the energy efficiency of multicore-based systems. In this paper, based on voltage island and dynamic voltage and frequency scaling (DVFS) techniques, we investigate the energy efficiency of block-partitioned multicore processors, where cores are grouped into blocks and each block has a DVFS-enabled power supply. Depending on the number of cores on each block, we study both symmetric and asymmetric block configurations. We develop a system-level power model (which can support various power management techniques) and derive both block- and system-level energy-efficient frequencies. Based on the power model, we prove that, for embarrassingly parallel applications, having all cores on a single block can achieve the same energy savings as that of the individual block configuration (where each core forms a single block and has its own power supply). However, for applications with limited degrees of parallelism, we show the superiority of the buddy-asymmetric block configuration, where the number of required blocks (i.e., power supplies) is logarithmically related to the number of cores on the chip, in that it can achieve the same amount of energy savings as that of the individual block configuration. The energy efficiency of block-partitioned multicore systems is further evaluated through extensive simulations with both synthetic as well as a real life application.

Description

Keywords

multicore processors, energy management, dynamic voltage and frequency scaling (DVFS), voltage islands, parallel applications

Citation

Department

Computer Science