Dynamic Workload Division in GPU-CPU Heterogeneous Systems

GPU provides powerful computational capabilities and huge potential optimization possibility of efficient. As a result, the CPU-GPU heterogeneous architecture is still the hot zone of the high performance computation. However, the energy consuming is still the bottle neck of the entire the system, when the system and its corresponding framework need massive scale calculation. Most of the existing study is focus on how to lower the GPU power requirement. However, they did not considered CPU and GPU as an entire architecture. This thesis is based on the GreenGPU heterogeneous architecture. Because of the new generation of platform, one of the assumptions is that the operation system and its correspondence driver will adjust the DVFS to its best optimizing point. I implement a workload division algorithm using Tesla CUDA GPUs and AMD CPUs to balance the time difference caused by the workload. The real physical testbed results show the new workload division algorithm can provide at least 5X accuracy than previous algorithm without extra energy cost in the workload division procedure. This more accurate workload division algorithm could benefits the overall system energy consumption especially when the workload is huge.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor dc:publisher

The Ohio State University

Year dc:date

2013

Author dc:creator

• Chen, Wei

Contributors dc:contributor

• Xiaorui, Wang

• Electrical Engineering

Statement dc:rights

• unrestricted
• This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.

Language dc:language

English

Repository record dc:identifier

http://rave.ohiolink.edu/etdc/view?acc_num=osu1364250106

OAI identifier oai:identifier

oai:etd.ohiolink.edu:osu1364250106