Performance Characterization of Containerized DNN Training and Inference on Edge Accelerators

Authors: Prashanthi S.K., Vinayaka Hegde, Keerthana Patchava, Ankita Das and Yogesh Simmhan

Edge devices have typically been used for DNN in-ferencing. The increase in the compute power of accelerated edges is leading to their use in DNN training also. As privacy becomes a concern on multi-tenant edge devices, Docker containers provide a lightweight virtualization mechanism to sandbox models. But their overheads for edge devices are not yet explored. In this work, we study the impact of containerized DNN inference and training workloads on an NVIDIA AGX Orin edge device and contrast it against bare metal execution on running time, CPU, GPU and memory utilization, and energy consumption. Our analysis provides several interesting insights on these overheads.

Journal/Conference

EEE International Conference on High Performance Computing, Data, and Analytics (HiPC), 2023