Efficient Workload Balancing on Heterogeneous GPUs using MixedInteger Non-Linear Programming
Chih-Sheng Lin, Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan ; Chih-Wei Hsieh, Hsi-Ya Chang, National Center for High-Performance Computing, Taiwan ; Pao-Ann Hsiung, Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan
Journal of Applied Research and Technology Volume 12, Number 6, ISSN 1665-6423 Publisher: Elsevier Ltd
Recently, heterogeneous system architectures are becoming mainstream for achieving high performance and power efficiency. In particular, many-core graphics processing units (GPUs) now play an important role for computing in heterogeneous architectures. However, for application designers, computational workload still needs to be distributed to heterogeneous GPUs manually and remains inefficient. In this paper, we propose a mixed integer non-linear programming (MINLP) based method for efficient workload distribution on heterogeneous GPUs by considering asymmetric capabilities of GPUs for various applications. Compared to the previous methods, the experimental results show that our proposed method improves performance and balance up to 33% and 116%, respectively. Moreover, our method only requires a few overhead while achieving high performance and load balancing.
Lin, C.S., Hsieh, C.W., Chang, H.Y. & Hsiung, P.A. (2014). Efficient Workload Balancing on Heterogeneous GPUs using MixedInteger Non-Linear Programming. Journal of Applied Research and Technology, 12(6), 1176-1186. Elsevier Ltd.