Brief Industry Paper: Workload-Aware GPU Performance Estimation in the Airborne Embedded System
Yuan Yao, Sikai Wu, Shuangyang Liu, Qingshuang Sun, Gang Yang, Yujiao Hu, Yu Zhang, Liu Huixia, Xinyu Tian · 2021
New generation airborne embedded system has deployed Graphical Processing Units (GPUs) to raise processing capability to meet growing computational demands. Applications in the airborne embedded system have strict real-time constraints. Therefore, it is necessary to accurately predict timing behaviors of those applications. Many previous work propose GPU performance models to estimate the execution time of applications. However, most of those models do not consider the impact of co-execution on the GPU performance. In this paper, we propose a workload-aware GPU performance model to predict the execution time of applications executed concurrently on a single GPU. Experimental results illustrate that the proposed model can achieve a 5.1%-11.6% prediction error in a real airborne embedded hardware platform.