ACOS: 低成本光学开关阵列
ACOS: Arrays of Cheap Optical Switches
AI总结：
ACOS通过低成本光学开关阵列实现训练集群的可扩展性，突破现有专用ML网络的瓶颈，同时在性能和成本上具有优势。
摘要：
机器学习训练对集群网络提出了巨大需求，推动了专用架构和并行化策略的专门设计。最近的方案结合了光学电路开关（OCSes）具有改进的成本、功率效率和长期带宽扩展优势，优于包交换开关。然而，大多数现有方法依赖于昂贵的高基数OCSes以及/或将其与包交换开关结合以实现可扩展性下的竞争力。不幸的是，高基数OCSes既昂贵又重新配置慢，限制了可扩展性和性能。我们提出了低成本光学开关阵列（ACOS），将应用共设计直接带到可重构织物的结构中。使用低基数OCSes作为基本构件，ACOS支持训练集群所需的重新配置形式，包括拓扑选择、工作负载适应和故障恢复。ACOS的成本随着支持的拓扑和适应能力而变化，而不是端口数量，突破了当前专用ML网络的可扩展性障碍。我们通过模拟表明，基于ACOS的部署在大规模训练最先进的LLMs时与完全配置的包交换网络性能相当，同时使用现有现成的OCSes实现了显著的成本节约，未来具有更强的带宽扩展和更高的成本节约。
英文摘要：
Machine learning training places immense demands on cluster networks, motivating specialized architectures and co-design with parallelization strategies. Recent designs incorporating optical circuit switches (OCSes) are promising, offering improved cost, power efficiency, and long-term bandwidth scaling than packet switches. However, most existing approaches rely on costly high-radix OCSes and/or combine them with packet switches to achieve competitive performance at scale. Unfortunately, high-radix OCSes are both expensive and slow to reconfigure, limiting both scalability and performance. We propose Arrays of Cheap Optical Switches (ACOS), which bring application co-design directly to the structure of the reconfigurable fabric. Using low-radix OCSes as building blocks, ACOS supports the forms of reconfiguration needed in training clusters including topology selection, workload adaptation, and failure resilience. The cost of ACOS scales with supported topologies and adaptations rather than with port count, breaking past the scalability barriers of current specialized ML networks. We show through simulation that ACOS-based deployments match the performance of fully provisioned packet-switched networks when training state-of-the-art LLMs at scale, while delivering significant cost savings using existing off-the-shelf OCSes, with strong bandwidth scaling and higher cost savings in the future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
