Changbo Wu

Xinchi Han, Weihao Jiang, Yingming Mao et al.

Recent years have witnessed the growing deployment of optical circuit switches (OCS) in commercial GPU clusters (e.g., Google A3 GPU cluster) optimized for machine learning (ML) workloads. Such clusters adopt a three-tier leaf-spine-OCS topology, servers attach to leaf-layer electronic packet switches (EPSes); these leaf switches aggregate into spine-layer EPSes to form a Pod; and multiple Pods are interconnected via core-layer OCSes. Unlike EPSes, OCSes only support circuit-based paths between directly connected spine switches, potentially inducing a phenomenon termed routing polarization, which refers to the scenario where the bandwidth requirements between specific pairs of Pods are unevenly fulfilled through links among different spine switches. The resulting imbalance induces traffic contention and bottlenecks on specific leaf-to-spine links, ultimately reducing ML training throughput. To mitigate this issue, we introduce a leaf-centric paradigm to ensure traffic originating from the same leaf switch is evenly distributed across multiple spine switches with balanced loads. Through rigorous theoretical analysis, we establish a sufficient condition for avoiding routing polarization and propose a corresponding logical topology design algorithm with polynomial-time complexity. Large-scale simulations validate up to 19.27% throughput improvement and a 99.16% reduction in logical topology computation overhead compared to Mixed Integer Programming (MIP)-based methods.

Changbo Wu, Zhuolong Yu, Gongming Zhao et al.

Collective communication (CC) is widely adopted for large-scale distributed machine learning (DML) training workloads. DML's predictable traffic pattern provides a great oppotunity for applying optical network technology. Existing optical interconnects-based CC schemes adopt ``one-shot network reconfiguration'', which provisions static high-capacity topologies for an entire collective operation -- sometimes for a full training iteration. However, this approach faces significant scalability limitations when supporting more complex and efficient CC algorithms required for modern workloads: the ``one-shot'' strategies either demand excessive resource overprovisioning or suffer performance degradation due to rigid resource allocation. To address these challenges, we propose SWOT, a demand-aware optical network framework. SWOT employs ``intra-collective reconfiguration'' and can dynamically align network resources with CC traffic patterns. SWOT incorporates a novel scheduling technique that overlaps optical switch reconfigurations with ongoing transmissions, and improves communication efficiency. SWOT introduce a lightweight collective communication shim that enables coordinated optical network configuration and transmission scheduling while supporting seamless integration with existing CC libraries. Our simulation results demonstrate SWOT's significant performance improvements.