XPUTimer is a comprehensive diagnostic framework designed to identify and resolve anomalies in large-scale GPU clusters for LLM training, addressing a critical gap in existing tools.

• Targets the complete training stack rather than specific issues, enabling holistic diagnostics
• Provides real-time anomaly detection capabilities for thousand-plus GPU clusters
• Implements specialized components including tracing daemons and diagnostic engines for efficient performance monitoring
• Helps maintain high performance in increasingly complex LLM training environments

For engineering teams, this research offers practical solutions to a growing challenge: maintaining consistent performance as LLM training scales. By detecting divergent processing patterns early, organizations can significantly reduce costly training failures and infrastructure inefficiencies.

XPUTimer: Anomaly Diagnostics for Divergent LLM Training in GPU Clusters of Thousand-Plus Scale