The widespread adoption of distributed, GPU-accelerated systems for artificial intelligence (AI) has created a pressing need for advanced profiling and optimization methodologies. Understanding and resolving performance issues in these complex environments is essential. This paper discusses an approach centered on leveraging high-precision time synchronization to improve profiling and debugging in AI clusters.  Standard profiling techniques in distributed settings often struggle with temporal inconsistencies across different nodes. This lack of a common time reference can make correlating events and reliably diagnosing issues difficult. By employing time synchronization mechanisms such as the Precision Time Protocol (PTP), we can achieve precise, unified timestamp alignment across the entire cluster.  We discuss how this synchronized, high-fidelity data enables more effective tracing of AI workload execution. By resolving fundamental temporal ambiguities, this approach enables deterministic, cross-node event correlation. This, in turn, provides a clear foundation for causal analysis and the identification of performance bottlenecks, resource contention, and other operational anomalies with greater clarity.  This methodology underscores the value of precise time data in improving profiling tools for distributed AI infrastructure. Enabling more accurate analysis of system events ultimately supports the development of more reliable and optimized AI applications.