This work analyzes the main isolation mechanisms available in modern NVIDIA GPUs: MPS, MIG, and the recent Green Contexts, to ensure predictable inference time in safety-critical applications using deep learning models. The experimental methodology includes performance tests, evaluation of partitioning impact, and analysis of temporal isolation between processes, considering both the NVIDIA A100 and Jetson Orin platforms. It is observed that MIG provides a high level of isolation. At the same time, Green Contexts represent a promising alternative for edge devices by enabling fine-grained SM allocation with low overhead, albeit without memory isolation. The study also identifies current limitations and outlines potential research directions to improve temporal predictability in shared GPUs.

翻译：本研究分析了现代NVIDIA GPU中可用的主要隔离机制：MPS、MIG以及最新的Green Contexts，旨在确保使用深度学习模型的安全关键应用获得可预测的推理时间。实验方法包括性能测试、分区影响评估以及进程间时序隔离分析，并同时考虑了NVIDIA A100和Jetson Orin平台。研究发现MIG能提供高水平的隔离性，而Green Contexts则通过实现细粒度SM分配且保持较低开销，为边缘设备提供了有前景的替代方案，尽管其缺乏内存隔离。本研究还指出了当前机制的局限性，并提出了改善共享GPU时序可预测性的潜在研究方向。