3 papers
cs.CR2026
Trusted Weights, Treacherous Optimizations? Optimization-Triggered Backdoor Attacks on LLMs
Yifei Wang, Tianlin Li, Xiaohan Zhang +3
Inference optimization is a vital technique for deploying LLMs at scale. Compilation is the most widely adopted optimization technique for LLMs. While it assumes semantic equivalen…
cs.DC2026
TierCheck: Tiered Checkpointing for Fault Tolerance in Large Language Model Training
Shujie Han, Feng Jiang, Patrick P. C. Lee +5
Large Language Model (LLM) training is frequently interrupted by a heterogeneous spectrum of failures, from common GPU crashes to catastrophic cluster-wide outages. Existing checkp…
cs.DC2026
Comprehensive Deadlock Prevention for GPU Collective Communication
Lichen Pan, Juncheng Liu, Yongquan Fu +4
Distributed deep neural network training necessitates efficient GPU collective communications, which are inherently susceptible to deadlocks. GPU collective deadlocks arise easily…