Demo: Real-Time Collective Communication Log Analyzer for Distributed AI/ML Workloads

We present a log analytics framework that collects collective communication logs from real-time distributed AI/ML workloads such as Large Language Models training, finetuning and inferencing jobs. It is designed to handle massive log volumes from frameworks such as NVIDIA’s Collective Communication Library (NCCL) during distributed GPU operations, the framework ingests heterogeneous log streams as these logs are often the first indicators of faults in the end-to-end path. Using NLP-based parsing, log template clustering, temporal correlation for related logs, and anomaly pattern detection, it links critical log events to performance metrics across layers which enable fault localization in GPU-to-GPU communication. Beyond fault detection, it provides comprehensive performance analytics from byte exchange patterns to identify network bottlenecks like low-latency links, bandwidth-constrained node pairs, and network pressure points that increasingly constrain modern AI workloads. In case of distributed inferencing workloads (ex. DeepSeek R1 Distilled 8B), it captures phase-specific (prefill/decode) and collective operation-specific metrics, revealing communication bottlenecks affecting end-to-end response time.