Performance Profiling

Goal

Provide tools to analyze simulation performance, identify bottlenecks, and recommend optimization strategies for computational materials science simulations.

Requirements

• Python 3.8+
• No external dependencies (uses Python standard library only)
• Works on Linux, macOS, and Windows

Inputs to Gather

Before running profiling scripts, collect from the user:

Input	Description	Example
Simulation log	Log file with timing information	simulation.log
Scaling data	JSON with multi-run performance data	scaling_data.json
Simulation parameters	JSON with mesh, fields, solver config	params.json
Available memory	System memory in GB (optional)	16.0

Decision Guidance

When to Use Each Script

Need to identify slow phases?
├── YES → Use timing_analyzer.py
│         └── Parse simulation logs for timing data
│
Need to understand parallel performance?
├── YES → Use scaling_analyzer.py
│         └── Analyze strong or weak scaling efficiency
│
Need to estimate memory requirements?
├── YES → Use memory_profiler.py
│         └── Estimate memory from problem parameters
│
Need optimization recommendations?
└── YES → Use bottleneck_detector.py
          └── Combine analyses and get actionable advice

Choosing Analysis Thresholds

Metric	Good	Acceptable	Poor
Phase dominance	<30%	30-50%