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cs.DC2025
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.DC2025★ 1 cited
SPPO:Efficient Long-sequence LLM Training via Adaptive Sequence Pipeline Parallel Offloading
Qiaoling Chen, Shenggui Li, Wei Gao +3
In recent years, Large Language Models (LLMs) have exhibited remarkable capabilities, driving advancements in real-world applications. However, training LLMs on increasingly long i…