Search for: All records

The increasing complexity of AI workloads, especially distributed Large Language Model (LLM) training, places significant strain on the networking infrastructure of parallel data centers and supercomputing systems. While Equal-Cost Multi-Path (ECMP) routing distributes traffic over parallel paths, hash collisions often lead to imbalanced network resource utilization and performance bottlenecks. This paper presents FlowTracer, a tool designed to analyze network path utilization and evaluate different routing strategies. Unlike tools that introduce additional traffic, FlowTracer aids in debugging network inefficiencies by passively monitoring and correlating user workload flows. As a result, FlowTracer does not interfere with ongoing data transfers, enabling analysis with minimal overhead, which is an important factor when debugging and fine-tuning routing schemes in production systems. FlowTracer can provide detailed insights into traffic distribution and can help identify the root causes of performance degradation, such as hash collisions. With FlowTracer’s flow-level insights, system operators can optimize routing, reduce congestion, and improve the performance of distributed AI workloads. We use a RoCEv2-enabled cluster with a leaf-spine network and 16 400-Gbps nodes to demonstrate how FlowTracer can be used to compare the flow imbalances of ECMP routing against a statically configured network. The example showcases a 30% reduction in imbalance, as measured by a new metric we introduce.