This paper provides a comprehensive review of parallel strategies for distributed LLM training and inference, offering methodological guidance for optimal system design through mathematical models and case studies. 该论文对LLM分布式训练与推理中的并行策略进行了全面综述，通过数学模型和案例分析，为设计最优分布式系统提供了方法论指导。
Notes
Reviews collective operations and parallel strategies in distributed LLM computing with mathematical formulations.
Analyzes hybrid parallelization with focus on communication-computation overlap.
Discusses automated search for optimal hybrid strategies using cost models.
Presents case studies with mainstream architectures for strategy selection insights.
Highlights open challenges of current LLM training paradigms.
Outlines directions for next-generation large-scale model development.
系统回顾LLM分布式计算中的集体操作与并行策略，提供数学公式深化理论理解。
分析混合并行化设计，强调训练与推理阶段的通信计算重叠。
讨论基于成本模型自动搜索最优混合并行策略的进展。
通过主流架构案例研究，揭示并行策略选择的经验见解。
指出当前LLM训练范式的开放挑战与局限。
为下一代大规模模型开发指明研究方向。
DeML OS Q & A 问答
Deep Dive 💬深度解析协议 💬
02.11
2026
Wed星期三
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What does "hybrid parallelization" refer to in the paper?论文中提到的“混合并行化”是什么意思？
"Hybrid parallelization" combines data, model, and pipeline parallelism to distribute LLM workloads efficiently, with emphasis on overlapping communication and computation.“混合并行化”是指结合使用多种并行技术（如数据并行、模型并行、流水线并行）来更有效地分配LLM的计算和内存负载。论文强调通过重叠通信与计算来优化性能。
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What major challenges of current LLM training paradigms does the paper highlight?论文指出了当前LLM训练范式的哪些主要挑战？
Challenges include communication-computation coordination at scale, memory bottlenecks, automation complexity, and designing scalable architectures for larger models.论文指出了几个开放挑战，包括在超大规模模型下通信与计算的协调效率、内存瓶颈的突破、混合策略自动化的复杂性，以及为下一代更大模型设计可扩展且成本效益高的系统架构。
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What are the theoretical contributions and practical limitations of automated search based on cost models for finding optimal hybrid parallelization strategies?基于成本模型的自动化搜索对于寻找最优混合并行策略有何理论贡献和实践限制？
Theoretical contribution: formalizing strategy search as constrained optimization over exponential space. Practical limits: cost model accuracy, search overhead, and poor adaptability to dynamic/heterogeneous environments.理论贡献在于将策略搜索问题形式化为一个在约束（如内存、设备数量）下的优化问题，允许系统探索指数级大的策略空间。实践限制包括成本模型本身的准确性（依赖于对硬件和框架行为的简化假设）、搜索过程的计算开销，以及对于动态负载或异构集群环境的适应性不足。