36 citations · 56 across the 7 of their papers we have counts for
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cs.DC2026
Addressing Variable Heterogeneity in Distributed Multimodal Training with Entrain
Insu Jang, Mosharaf Chowdhury
Multimodal LLM datasets are inherently heterogeneous, with significant data variability. Although each modality exhibits independent variability, sample-level entanglement makes it…
cs.DC2025★ 1 cited
Efficient Distributed MLLM Training with Cornstarch
Insu Jang, Runyu Lu, Nikhil Bansal +2
Multimodal large language models (MLLMs) extend the capabilities of large language models (LLMs) by combining heterogeneous model architectures to handle diverse modalities like im…
cs.DC2023★ 36 cited
Oobleck: Resilient Distributed Training of Large Models Using Pipeline Templates
Insu Jang, Zhenning Yang, Zhen Zhang +2
Oobleck enables resilient distributed training of large DNN models with guaranteed fault tolerance. It takes a planning-execution co-design approach, where it first generates a set…