人工智能时代高性能网络发展现状与趋势OA

The rapid development of artificial intelligence(AI)large models has posed new challenges to high-performance network architectures.This paper systematically analyzes three core issues:the conflict between high-bandwidth demands and costs,bottlenecks in low-latency stability,and complexities in topology adaptation.It reveals the critical roles of enhancements in remote direct memory access(RDMA)technology,improvements in edge-side capabilities,and end-to-end network collaborative optimization.The study finds that there is an urgent need for industry-academia-research collaboration to address the challenges faced by high-performance network,and provides suggestions for development directions.Future trends point to collaborative innovation in protocol openness,hardware optoelectronic integration,and paradigm autonomy,which breaks the limitations of single-dimensional optimization through cross-layer design,thereby providing high-performance,low-energy-consumption interconnection support for ultra-large-scale AI clusters.