Benefits of GPU-CPU Task Replacement for Edge Device and Platform: Poster Abstract

Abstract

Contemporary cyber-physical systems (CPS) applications are deployed on a networked platform with embedded devices and, like conventional workstations, each embedded device is now equipped with both CPU and GPU. In this paper, we present our on-going effort of synergizing CPU and GPU computing resources to improve application response time. We experimented on NVIDIA's Jetson Nano embedded device and RTX 2080 Ti graphics card and show that, in particular, with multiple GPU-intensive tasks running, it is possible to improve the application response time by replacing a GPU-intensive task by a corresponding CPU-intensive task. We studied several configurations of CPU-GPU task allocation and replacement, and accordingly we outlined a set of principles in leveraging such heterogeneous resources as a whole.