CTA-Aware dynamic scheduling scheme for streaming multiprocessors in high-performance GPUs

Abstract

GPGPUs can provide powerful computational capability and are employed to execute both graphics and general-purpose applications. Hardware resource utilization is one of the most important factors in determining the GPGPU performance. For GPGPUs, multiple-application execution can increase the data parallelism, resulting in high resource utilization. However, applications have different execution time depending on their workload sizes. Therefore, if one application is completed earlier than the other ones, resource underutilization problem may happen because the hardware resource allocated for the early completed application become idle. In this work, a CTA-aware dynamic streaming multiprocessors scheduling scheme is proposed for multiple-application execution in the GPGPU to efficiently manage hardware resources. Compared to the baseline architecture, the proposed CTA-aware dynamic SM scheduling scheme can increase GPU performance by up to 25.6% on average.