In response to increasing demands for largescale machine learning training jobs, many organizations have deployed GPU clusters across geographically distributed regions.However, existing Integer Linear Programming (ILP)-or genetic-based cross-cluster training approaches largely overlook the topology of decentralized clusters, lacking both topology-aware task scheduling mechanisms and automated model parallelization strategies.As a result, naively applying these optimization-based methods in cross-cluster settings leads to prohibitive scheduling overhead, due to the drastically enlarged search space induced by complex inter-cluster topologies.To address these challenges, we propose SpiderFlow, a topologyaware scheduling system specifically designed for decentralized GPU clusters.We formulate cross-cluster task scheduling as a graph optimization problem and introduce SpinSearch, a low-overhead topology-aware scheduling algorithm.In addition, for automated model parallelization, we propose Topology-aware Parallelism Automation (TPA), a two-level scheduling framework that combines heuristic methods at the inter-cluster level with ILP-based optimization within clusters, effectively reducing the search space while maintaining high training throughput with substantially lower scheduling overhead.We evaluate SpiderFlow on a physical platform comprising 8 decentralized clusters, as well as on a simulation platform with up to 64 decentralized clusters.Experimental results demonstrate that SpiderFlow reduces job completion time (JCT) by 1.2-1.3×,improves throughput by 1.12-1.25×,and reduces scheduling overhead by 20-90× on average compared to state-of-the-art scheduling systems.