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arXiv · 2607.03229

HyperParallel-Mpipe: A Composable Algebra System for Optimizing MLLM Training over Supernode Clusters

Chong Li·Zhengdao Yu·Nelson Lossing·Thibaut Tachon·Pierre Leca·Etienne Filhol·Yujie Yuan·Chong Bao·Teng Su
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Abstract

Modern AI applications have expanded beyond text-only interaction into a wide range of multimodal scenarios, making multimodal large language models (MLLMs) crucial for both research and industry. However, compared with traditional decoder-only LLM training, large-scale MLLM training often shows much lower MFU. We analyze the key pain points in MLLM training and introduce Mpipe, which uses a schedule algebra to derive concrete runtime behavior from a compact schedule specification. From this algebra, Mpipe derives transpose, a multimodal-aware heterogeneous parallel schedule that remaps modality-encoder computation into otherwise idle pipeline regions. On Ascend 910C NPU clusters, Mpipe achieves 2.70x speedup in a small-scale setting and 1.21x speedup in a 512-card large-scale setting.

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Chong Li, Zhengdao Yu, Nelson Lossing, Thibaut Tachon, Pierre Leca, Etienne Filhol, Yujie Yuan, Chong Bao, Teng Su. 2026-07-03. HyperParallel-Mpipe: A Composable Algebra System for Optimizing MLLM Training over Supernode Clusters. https://arxiv.org/abs/2607.03229

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