MegatronApp: Efficient and Comprehensive Management on Distributed LLM Training
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911078460424192 |
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| author | Zhao, Bohan Yang, Guang Chen, Shuo Liu, Ruitao Zhang, Tingrui He, Yongchao Xu, Wei |
| author_facet | Zhao, Bohan Yang, Guang Chen, Shuo Liu, Ruitao Zhang, Tingrui He, Yongchao Xu, Wei |
| contents | The rapid escalation in the parameter count of large language models (LLMs) has transformed model training from a single-node endeavor into a highly intricate, cross-node activity. While frameworks such as Megatron-LM successfully integrate tensor (TP), pipeline (PP), and data (DP) parallelism to enable trillion-parameter training, they simultaneously expose practitioners to unprecedented systems-level challenges in performance optimization, diagnosis, and interpretability. MegatronApp is an open-source toolchain expressly designed to meet these challenges. It introduces four orthogonal, yet seamlessly composable modules--MegaScan, MegaFBD, MegaDPP, and MegaScope--that collectively elevate the reliability, efficiency, and transparency of production-scale training. This paper presents the motivation, architecture, and distinctive contributions of each module, and elucidates how their synergistic integration augments the Megatron-LM ecosystem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19845 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MegatronApp: Efficient and Comprehensive Management on Distributed LLM Training Zhao, Bohan Yang, Guang Chen, Shuo Liu, Ruitao Zhang, Tingrui He, Yongchao Xu, Wei Distributed, Parallel, and Cluster Computing The rapid escalation in the parameter count of large language models (LLMs) has transformed model training from a single-node endeavor into a highly intricate, cross-node activity. While frameworks such as Megatron-LM successfully integrate tensor (TP), pipeline (PP), and data (DP) parallelism to enable trillion-parameter training, they simultaneously expose practitioners to unprecedented systems-level challenges in performance optimization, diagnosis, and interpretability. MegatronApp is an open-source toolchain expressly designed to meet these challenges. It introduces four orthogonal, yet seamlessly composable modules--MegaScan, MegaFBD, MegaDPP, and MegaScope--that collectively elevate the reliability, efficiency, and transparency of production-scale training. This paper presents the motivation, architecture, and distinctive contributions of each module, and elucidates how their synergistic integration augments the Megatron-LM ecosystem. |
| title | MegatronApp: Efficient and Comprehensive Management on Distributed LLM Training |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2507.19845 |