Performance and Energy Modeling for Cooperative Hybrid Computing

Authors

Rong Ge, Marquette UniversityFollow
Xizhou Feng, Marquette UniversityFollow
Martin Burtscher, Texas State University - San Marcos
Ziliang Zong, Texas State University - San Marcos

Document Type

Conference Proceeding

Language

eng

Format of Original

10 p.

Publication Date

2014

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Source Publication

2014 9th IEEE International Conference on Networking, Architecture, and Storage

Source ISSN

978-1-4799-4087-5

Original Item ID

doi: 10.1109/NAS.2014.42

Abstract

Accelerator-based heterogeneous systems can provide high performance and energy efficiency, both of which are key design goals in high performance computing. To fully realize the potential of heterogeneous architectures, software must optimally exploit the hosts' and accelerators' processing and power-saving capabilities. Yet, previous studies mainly focus on using hosts and accelerators to boost application performance. Power-saving features to improve the energy efficiency of parallel programs, such as Dynamic Voltage and Frequency Scaling (DVFS), remain largely unexplored. Recognizing that energy efficiency is a different objective than performance and should therefore be independently pursued, we study how to judiciously distribute computation between hosts and accelerators for energy optimization. We further explore energy-saving scheduling in combination with computation distribution for even larger gains. Moreover, we present PEACH, an analytical model for Performance and Energy Aware Cooperative Hybrid computing. With just a few system- and application-dependent parameters, PEACH accurately captures the performance and energy impact of computation distribution and energy-saving scheduling to quickly identify the optimal coupled strategy for achieving the best performance or the lowest energy consumption. PEACH thus eliminates the need for extensive profiling and measurement. Experimental results from two GPU-accelerated heterogeneous systems show that PEACH predicts the performance and energy of the studied codes with less than 3% error and successfully identifies the optimal strategy for a given objective.

Comments

Accepted Version. Published as part of the proceedings of the conference, 2014 9th IEEE International Conference on Networking, 2014: 232-241. DOI. ©2014 IEEE. Used with permission

Recommended Citation

Ge, Rong; Feng, Xizhou; Burtscher, Martin; and Zong, Ziliang, "Performance and Energy Modeling for Cooperative Hybrid Computing" (2014). Mathematics, Statistics and Computer Science Faculty Research and Publications. 278.
https://epublications.marquette.edu/mscs_fac/278