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About this paper
Chong Li, Zhengdao Yu, Nelson Lossing, Thibaut Tachon, Pierre Leca, Etienne Filhol, Yujie Yuan, Chong Bao and 1 more
HyperParallel-Mpipe presents a composable algebra system for optimizing multimodal large language model (MLLM) training on supernode clusters. The system achieves speedups up to 2.70x by using a "transpose" schedule to efficiently relocate variable modality encoder computations into pipeline warmup bubbles, improving Model FLOPs Utilization.
Key insights
- Relocating the computationally variable encoder into static pipeline warmup bubbles can significantly mitigate dynamic load imbalances without runtime scheduling overhead.
- A unified schedule algebra can systematically formalize existing pipeline parallelism techniques and enable the derivation of new, optimized schedules for heterogeneous model architectures.
- The backward footprint of a model region is invariant to its specific pipeline schedule, allowing a single 'transpose' schedule to be used for both frozen and trained encoders.
Summary by alphaXiv.
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