Achieving Near-Linear Training Scalability for Pinterest’s Foundation Models

Achieving Near-Linear Training Scalability for Pinterest’ s Foundation Models

摘要

Pinterest工程团队通过量化通信、均衡分片、带宽感知优化和2D并行拓扑重构，将多节点训练扩展效率从0.2倍提升至8节点7.5倍（理想值的93.75%）。核心经验：通信瓶颈必须通过减少传输字节、重塑负载和本地化昂贵操作直接解决。