Python-level logs and GPU hardware metrics operate on divergent timebases causing misalignment

Software application logs and hardware GPU metrics use different timebases and granularities (event-driven vs periodic sampling), preventing correlation of request-level latency spikes with specific hardware transients

Implement timestamp calibration between CPU/Python application layer and GPU driver layer. Use nanosecond-precision unified timeline with causal correlation algorithms to map Python function calls to GPU kernel execution. Synchronize collection of micro-events with macro-metrics.