@inproceedings{3a365339cdd44a259c14098e77e5e5ce,
title = "Intelligent Policy Selection for GPU Warp Scheduler",
abstract = "The graphics processing unit (GPU) is widely used in applications that require massive computing resources such as big data, machine learning, computer vision, etc. As the diversity of applications grows, the GPU's performance becomes difficult to maintain by its warp scheduler. Most of the prior studies of the warp scheduler are based on static analysis of GPU hardware behavior for certain types of benchmarks. We propose for the first time (to the best of our knowledge), a machine learning approach to intelligently select suitable policies for various applications in runtime. The simulation results indicate that the proposed approach can maintain performance comparable to the best policy across different applications.",
author = "Chiou, {Lih Yih} and Yang, {Tsung Han} and Syu, {Jian Tang} and Chang, {Che Pin} and Chang, {Yeong Jar}",
year = "2019",
month = mar,
doi = "10.1109/AICAS.2019.8771596",
language = "English",
series = "Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "302--303",
booktitle = "Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019",
address = "United States",
note = "1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 ; Conference date: 18-03-2019 Through 20-03-2019",
}