Intelligent Policy Selection for GPU Warp Scheduler
Lih‐Yih Chiou, Tsung-Han Yang, Jian-Tang Syu, Che-Pin Chang, Yeong-Jar Chang · 2019
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.