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面对定制设计的高性能组网架构，业界开源的GPU集合通信库（比如NCCL）并不能将网络的通信性能发挥到极致，从而影响大模型训练的集群效率。 DeepNCCL是为阿里云神龙异构产品开发的一种用于多GPU互联的AI通信加速库，在AI分布式训练或多卡推理任务中用于提升通信效率。 本文主要介绍DeepNCCL的架构、优化原理和性能说明。 今天，我们正式开源 VCCL（Venus Collective Communication Library）- 一款由创智、基流、智谱、联通、北航、清华、东南联合研发的 GPU 集合通信库增强方案，以三大核心能力，重新定义 GPU 集群通信体验。 十年前，Intel主导的PCIe标准限制了包括GPU在内的PCIe设备间的直接数据传输，这些设备通常需要通过CPU进行通信，这限制了它们的通信带宽。 为了突破这一限制，NVIDIA的CEO黄仁勋推出了NVLink技术，允许GPU之间直接互联，绕过CPU，显著提高了数据传输速度。
具体通信流程：当模型参数需要从源GPU迁移到目标节点的异构GPU时，首先会经由PCIe通道，将数据从GPU复制到源节点的CPU，随后通过IPoIB或以太网跨越节点边界，抵达目标节点的CPU。 为了解决用户需求和技术挑战，壁仞科技团队攻坚异构GPU训练难点，自主研发出原创异构GPU协同训练方案HGCT，通过异构调度、异构通信、异构拆分三层架构，实现了一套统一方案支持多种不同型号、不同厂商的GPU，业界首次支持3种及以上异构GPU混合训练. 为应对这些挑战，创智、基流、智谱、联通等多家机构联合推出了开源GPU集合通信库VCCL。 VCCL在兼容异构硬件和支撑主流训练框架的基础上，创新性地引入了三大核心技术： 通过不断优化通信协议和设计创新的异构通信方案，企业和研究机构能够有效地解决异构卡间通信难、算力不均衡等问题，并推动大规模、高效的集群训练系统的落地。
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