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arXiv · 2512.20345

A Comprehensive Study of Bugs in Modern Distributed Deep Learning Systems

Xiaoxue Ma·Wanwei Zhan·Jiale Chen·Yishu Li·Jacky Keung·Federica Sarro
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Abstract

In today's data-driven era, deep learning is vital for processing massive datasets, yet single-device training is constrained by computational and memory limits. Distributed deep learning overcomes these challenges by leveraging multiple GPUs or machines in parallel. While general-purpose frameworks (e.g., TensorFlow and PyTorch) provide distributed capabilities, these are often add-on features that demand significant manual effort for advanced parallelism, underscoring the need for specialized frameworks. This study conducts the first large-scale empirical analysis of practitioner challenges in dedicated distributed frameworks. We examine 849 real-world issues from DeepSpeed, Megatron-LM, and Colossal-AI and construct a taxonomy of 34 bug symptoms, 28 root causes, and 6 fix patterns. Crucially, we establish explicit mappings between symptoms, causes, and fixes across distributed training stages, enabling a systematic understanding of how issues emerge and are resolved. Our results show that 45.1\% of bug symptoms are unique to distributed frameworks, with setup failures, memory issues, and performance anomalies being the most prevalent. Moreover, 95\% of issues in the communication setup stage occur exclusively in distributed contexts. We also find over 60\% of cases can be resolved through version and dependency management, and distributed feature, API, and communication tuning. Based on these findings, we provide actionable implications.

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Xiaoxue Ma, Wanwei Zhan, Jiale Chen, Yishu Li, Jacky Keung, Federica Sarro. 2025-12-23. A Comprehensive Study of Bugs in Modern Distributed Deep Learning Systems. https://arxiv.org/abs/2512.20345

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