A dynamic CTA scheduling scheme for massive parallel computing

Recent computing devices execute massive parallel data requiring huge computing hardware. To satisfy increasing computing need, GPUs providing powerful computational capability are employed to execute both graphics and general-purpose applications (GPGPUs). In the GPGPU, executing multiple applications together can increase the data parallelism, resulting in high resource utilization. Improving the resource utilization of the GPGPU can increase the GPGPU performance. However, various kinds of 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 becomes idle. In this work, a CTA-aware dynamic streaming multiprocessors scheduling scheme is proposed for multiple applications execution in the GPGPU to efficiently manage hardware resources. Simulation results show that the proposed CTA-aware dynamic SM scheduling scheme can increase the GPU performance by up to 25.6% on average.