3 ms·
Very real. https://www.usenix.org/system/files/usenixsecurity24-guo-yanan.pdf https://www.usenix.org/system/files/usenixsecurity24-guo-yan... https://www.scie
by woadwarrior01 1y ago
Very real.
https://www.usenix.org/system/files/usenixsecurity24-guo-yanan.pdf https://www.usenix.org/system/files/usenixsecurity24-guo-yan...
https://www.sciencedirect.com/science/article/pii/S0167404820303886 https://www.sciencedirect.com/science/article/pii/S016740482...
- throw0101d 1y agoI do not see MIG mentioned in either paper. I do not think the papers are examining isolation security between instances, which the GP was asking about.
- woadwarrior01 1y agoYeah, I only posted two links from my notes, from when I was looking at this a few months ago. Here's one on MIG. https://arxiv.org/abs/2207.11428 https://arxiv.org/abs/2207.11428
- stygiansonic 1y agoThat paper doesn’t seem to be about security vulnerabilities in MiG but rather using it to improve workload efficiency
- throw0101d 1y agoAs per sibling comment, this is about utilization efficiency and not breaking isolation (between MIG instances). The conclusion: > In this paper, we presented MISO, a technique to leverage the MIG functionality on NVIDIA A100 GPUs to dynamically partition GPU resources among co-located jobs. MISO deploys a learning-based method to quickly find the optimal MIG partition for a given job mix running in MPS. MISO is evaluated using a variety of deep learning workloads and achieves an average job completion time that is lower than the unpartitioned GPU scheme by 49% and is within 10% of the Oracle technique.