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Open nowPosted 44 days ago

智算互联方向实习生

sii-czxy/campus170 open roles

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Share of postings closed within
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  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 44 days ago

The posting

1. 集群网络拓扑与通信性能优化：参与超大规模智算集群（IB/RoCE 等）网络架构分析，利用 NCCL、MPI 等集合通信工具开展带宽、延迟测试与性能建模，定位分布式训练过程中的通信瓶颈。 2. 优化大规模模型训练通信效率：分析 All-Reduce、All-Gather、All-to-All 等通信操作在大模型训练中的性能影响，探索路由策略、拥塞控制、拓扑亲和部署等优化方案，提高集群通信效率。 3. 通信库底层调优与适配：深入研究 NVIDIA NCCL 或国产 AI 芯片通信栈（如华为 CANN、寒武纪等），针对不同模型架构（如 MoE）优化通信参数配置，提升训练性能。 4. 设计通信故障检测与自愈机制：参与智算集群通信稳定性优化，研究健康检查、故障定位、自动重路由等机制，提高大规模训练任务可靠性和有效运行时间。 5. 实验分析与工具开发：完成通信性能测试、瓶颈分析和优化验证，开发通信调优脚本、自动化测试工具及故障诊断原型，并整理技术报告。

1. 招募对象：本科生、硕士研究生。 2. 学历与专业背景：计算机、通信工程、电子工程、人工智能等相关专业优先。 3. 网络与通信基础：深入理解 TCP/IP 协议栈，熟悉 RoCEv2 或 InfiniBand 架构原理，了解网络拥塞控制算法。 4. 并行通信能力：了解 MPI 编程或 NCCL 集合通信库，理解 All-Reduce、All-Gather、All-to-All 等通信原语在大模型训练中的通信量与性能模型。 5. 编程与工程能力：熟练使用 Python 或 C/C++，能够编写自动化性能测试、压测和数据采集脚本。 6. 加分经历：具备 RDMA 编程经验、DPU（数据处理器）使用经验、大规模分布式训练或 AI Infra 相关项目经验者优先。 7. 具备较强的问题分析与定位能力，能够从系统、网络、软件等多个层面分析性能瓶颈，并具有良好的团队协作能力。 8. 对大规模智算系统、AI 基础设施、分布式训练优化等方向具有持续研究兴趣，愿意深入探索模型—软件—硬件协同优化问题。 9. 实习安排：线上线下结合；周期3–6个月；每周投入10–20小时。

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