TY - GEN
T1 - Empirical characterization of power efficiency for large scale data processing
AU - Lee, Yongbin
AU - Kim, Sungchan
N1 - Publisher Copyright:
© 2015 Global IT Research Institute (GiRI).
PY - 2015/8/25
Y1 - 2015/8/25
N2 - It becomes popular to equip CPU and GPU on a single computer system because of its performance and energy benefits, constituting a heterogeneous system for processing big data workloads. However, the optimal exploitation of such a heterogeneous system requires us to know the power consumption characteristics of the applications for difference processing units. To this end, this paper aims at characterizing the power efficiency of CPUs and GPUs for big data processing through empirical measurements. We take three recent computing units, high-end CPU, and GPU, and mobile embedded GPU as target platforms. We first show the performance and power consumption measurements on each computing platform using the Rodinia benchmarks as representative big data workloads. Then, we discuss how performance-per-watt of each computing platform is associated with different characteristics of the workloads.
AB - It becomes popular to equip CPU and GPU on a single computer system because of its performance and energy benefits, constituting a heterogeneous system for processing big data workloads. However, the optimal exploitation of such a heterogeneous system requires us to know the power consumption characteristics of the applications for difference processing units. To this end, this paper aims at characterizing the power efficiency of CPUs and GPUs for big data processing through empirical measurements. We take three recent computing units, high-end CPU, and GPU, and mobile embedded GPU as target platforms. We first show the performance and power consumption measurements on each computing platform using the Rodinia benchmarks as representative big data workloads. Then, we discuss how performance-per-watt of each computing platform is associated with different characteristics of the workloads.
KW - big data workload
KW - measurement
KW - Performance-per-watt
KW - Rodinia benchmark
UR - https://www.scopus.com/pages/publications/84949948893
U2 - 10.1109/ICACT.2015.7224902
DO - 10.1109/ICACT.2015.7224902
M3 - Conference paper
AN - SCOPUS:84949948893
T3 - International Conference on Advanced Communication Technology, ICACT
SP - 787
EP - 790
BT - IEEE 17th International Conference on Advanced Communications Technology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Advanced Communications Technology, ICACT 2015
Y2 - 1 July 2015 through 3 July 2015
ER -