TY - GEN
T1 - Task allocation for minimum system power in a homogenous multi-core processor
AU - Ge, Yang
AU - Qiu, Qinru
PY - 2010
Y1 - 2010
N2 - In this paper we address the impact of task allocation to the system power consumption of a homogenous multi-core processor with a main focus on its impact on the leakage power and fan power. Although the leakage power is determined by the average die temperature and the fan power is determined by the peak temperature, our analysis shows that the overall power can be minimized if a task allocation with minimum peak temperature is adopted together with an intelligent fan speed adjustment technique that finds the optimal tradeoff between fan power and leakage power. We further propose a multi-agent distributed task migration technique that searches for the best task allocation during runtime. By choosing only those migration requests that will result chip maximum temperature reduction, the proposed framework achieves large fan power savings as well as overall power reduction. Experimental results show that, our agent-based distributed task migration policy can save up to 37.2% fan power and 17.9% system overall power compared to the random mapping policy when the temperature constraint is tight. When the temperature constraint is loose, the overall system power is insensitive to the task allocation.
AB - In this paper we address the impact of task allocation to the system power consumption of a homogenous multi-core processor with a main focus on its impact on the leakage power and fan power. Although the leakage power is determined by the average die temperature and the fan power is determined by the peak temperature, our analysis shows that the overall power can be minimized if a task allocation with minimum peak temperature is adopted together with an intelligent fan speed adjustment technique that finds the optimal tradeoff between fan power and leakage power. We further propose a multi-agent distributed task migration technique that searches for the best task allocation during runtime. By choosing only those migration requests that will result chip maximum temperature reduction, the proposed framework achieves large fan power savings as well as overall power reduction. Experimental results show that, our agent-based distributed task migration policy can save up to 37.2% fan power and 17.9% system overall power compared to the random mapping policy when the temperature constraint is tight. When the temperature constraint is loose, the overall system power is insensitive to the task allocation.
KW - Low power
KW - Multiagent distributed framework
KW - Task allocation
KW - Thermal aware
UR - http://www.scopus.com/inward/record.url?scp=78449298709&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78449298709&partnerID=8YFLogxK
U2 - 10.1109/GREENCOMP.2010.5598299
DO - 10.1109/GREENCOMP.2010.5598299
M3 - Conference contribution
AN - SCOPUS:78449298709
SN - 9781424476138
T3 - 2010 International Conference on Green Computing, Green Comp 2010
SP - 299
EP - 306
BT - 2010 International Conference on Green Computing, Green Comp 2010
T2 - 2010 International Conference on Green Computing, Green Comp 2010
Y2 - 15 August 2010 through 18 August 2010
ER -