trace analysis

**Trace analysis** is the **timeline-based examination of runtime events to understand execution ordering, stalls, and overlap** - it reveals the real microsecond-level behavior of compute, memory transfer, and communication pipelines. **What Is Trace analysis?** - **Definition**: Inspection of chronological event traces from CPU threads, GPU streams, and communication backends. - **Primary Artifacts**: Kernel launch intervals, memcpy spans, synchronization points, and queue wait periods. - **Signal Types**: Idle gaps, serialization patterns, overlap quality, and long-tail straggler events. - **Tool Sources**: Nsight timelines, framework tracers, and scheduler trace exports. **Why Trace analysis Matters** - **Reality Check**: Trace data shows actual execution flow rather than inferred high-level assumptions. - **Idle Detection**: Exposes bubbles where accelerators or host threads are underutilized. - **Overlap Validation**: Confirms whether communication and compute are truly concurrent. - **Root Cause Speed**: Shortens debugging by directly locating serialization and synchronization bottlenecks. - **Optimization Prioritization**: Helps rank performance issues by measured timeline impact. **How It Is Used in Practice** - **Targeted Windows**: Collect traces around slow steps, startup phases, and periodic throughput drops. - **Layered Interpretation**: Combine timeline analysis with operator and kernel statistics for confidence. - **Action Verification**: Re-trace after each fix to ensure expected overlap and stall reduction occurred. Trace analysis is **the most direct way to see performance truth in ML systems** - timeline evidence turns vague slowdown symptoms into concrete, fixable execution problems.