12条实用技巧，教你设计AI驱动的高性能计算工作流，涵盖分布式计算关键要点。
arXiv:2606.07491v1 Announce Type: cross Abstract: High-performance computing (HPC) clusters remain the backbone of large-scale scientific computation,…
本地运行AI不仅省电，更是一场分布式的绿色计算革命，值得关注。
Data Centers: Dream Factories, Carbon Factories Behind every cloud query lies a colossal infrastructure. Millions of servers, spread across giant data…
揭秘千亿级AI工厂背后的高速网络架构，直击数据中心互联核心技术
arXiv:2605.21187v1 Announce Type: cross Abstract: As distributed model training scales to span hundreds of thousands of GPUs, scale-out networks face …
OpenAI官方分享训练大型神经网络的核心技术与工程挑战，GPU集群同步计算的关键方法。
Large neural networks are at the core of many recent advances in AI, but training them is a difficult engineering and research challenge which require…
新运行时系统TrainMover能弹性应对机器学习训练中断，比传统检查点恢复更高效可靠
arXiv:2412.12636v3 Announce Type: replace-cross Abstract: Large-scale ML training jobs are frequently interrupted by hardware and software anomalies, …
二阶优化方法加速LLM训练的瓶颈被Asteria运行时系统破解，大幅提升训练效率。
arXiv:2605.16184v1 Announce Type: cross Abstract: Second-order methods offer an attractive path toward more sample-efficient LLM training, but their p…