3 papers
cs.DC2026
OctoPipe: Reducing Pipeline Bubbles for Heterogeneous Models via Co-Optimizing Partitioning, Placement, and Scheduling
Jihu Guo, Tenghui Ma, Wei Gao +6
Pipeline parallelism is widely used to train large language models (LLMs). However, increasing heterogeneity in model architectures exacerbates pipeline bubbles, thereby reducing t…
cs.AR2026
SLOTH: Lightweight Detection and Localization of On-Chip Fail-Slow Failures for DNN Accelerators
Junchi Wu, Xinfei Wan, Zhuoran Li +5
Spatial DNN accelerators are essential for high-performance inference, but their performance is undermined by widespread fail-slow failures. Detecting such failures on-chip is chal…
cs.AI2025
RL in the Wild: Characterizing RLVR Training in LLM Deployment
Jiecheng Zhou, Qinghao Hu, Yuyang Jin +7
Large Language Models (LLMs) are now widely used across many domains. With their rapid development, Reinforcement Learning with Verifiable Rewards (RLVR) has surged in recent month…