Towards Predicting GPGPU Performance for Concurrent Workloads

Abstract

General-Purpose Graphics Processing Units (GPGPUs) have been widely adapted to the industry due to the high parallelism of Graphics Processing Units (GPUs) compared with Central Processing Units (CPUs). To handle the ever-increasing demand, multiple applications often run concurrently in the GPGPU device. However, the GPGPU device can be under-utilized when various types of GPGPU applications are running concurrently. In this paper, we analyze various types of scientific applications and identify factors that impact the performance during the concurrent execution of the applications in the GPGPU device. Our analysis results show that each application has a distinct characteristic and a certain combination of applications has better performance compared with the others when executed concurrently. Based on the finding of our analysis, we propose a simulator which predicts the performance of GPGPU. Our simulator collects performance metrics during the execution of applications and predicts the performance benefits. The experimental result shows that the best combination of applications can increase the performance by 39.44% and 65.98% compared with the average of combinations and the worst case, respectively.