To mitigate the increasingly common underutilization of computational resources in modern GPUs, spatial sharing methods enable multiple applications to use them simultaneously. This work presents a comprehensive evaluation of NVIDIA's primary technologies to achieve that goal: Multi-Process Service (MPS) and Multi-Instance GPU (MIG). Our findings reveal a crucial trade-off between MPS's flexibility and MIG's isolation, and provide many key insights for improving the co-execution strategy according to job profiles. In the most favorable scenarios, MPS improves performance by up to 30% and reduces energy by about 20%, using its provisioning option to avoid resource monopolization. However, under memory contention, it suffers severe degradation, worsening performance by around 30%. Conversely, MIG's full hardware isolation resolves memory contention, leading to more consistent improvements, but these gains are tempered by higher overhead, and its rigid scheme can degrade performance in certain cases.
@article{arxiv.2604.22430,
title = {A comprehensive evaluation of spatial co-execution on GPUs using MPS and MIG technologies},
author = {Jorge Villarrubia and Luis Costero and Francisco D. Igual and Katzalin Olcoz},
journal= {arXiv preprint arXiv:2604.22430},
year = {2026}
}