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% | >50% | | Parallel efficiency | >0.80 | 0.70-0.80 | <0.70 | | Memory usage | <60% | 60-80% | >80% |

Script Outputs (JSON Fields)

| Script | Key Outputs | |--------|-------------| | timing_analyzer.py | timing_data.phases, timing_data.slowest_phase, timing_data.total_time | | scaling_analyzer.py | scaling_analysis.results, scaling_analysis.efficiency_threshold_processors | | memory_profiler.py | memory_profile.total_memory_gb, memory_profile.per_process_gb, memory_profile.warnings | | bottleneck_detector.py | bottlenecks, recommendations |

Workflow

Complete Profiling Workflow

1. Analyze timing from simulation logs
2. Analyze scaling from multi-run data (if available)
3. Profile memory from simulation parameters
4. Detect bottlenecks and get recommendations
5. Implement optimizations based on recommendations
6. Re-profile to verify improvements

Quick Profiling (Timing Only)

1. Run timing analyzer on simulation log
2. Identify dominant phases (>50% of runtime)
3. Apply targeted optimizations to dominant phases

CLI Examples

Timing Analysis

bash
# Basic timing analysis
python3 scripts/timing_analyzer.py \
    --log simulation.log \
    --json

# Custom timing pattern
python3 scripts/timing_analyzer.py \
    --log simulation.log \
    --pattern 'Step\s+(\w+)\s+took\s+([\d.]+)s' \
    --json

Scaling Analysis

bash
# Strong scaling (fixed problem size)
python3 scripts/scaling_analyzer.py \
    --data scaling_data.json \
    --type strong \
    --json

# Weak scaling (constant work per processor)
python3 scripts/scaling_analyzer.py \
    --data scaling_data.json \
    --type weak \
    --json

Memory Profiling

bash
# Estimate memory requirements
python3 scripts/memory_profiler.py \
    --params simulation_params.json \
    --available-gb 16.0 \
    --json

Bottleneck Detection

bash
# Detect bottlenecks from timing only
python3 scripts/bottleneck_detector.py \
    --timing timing_results.json \
    --json

# Comprehensive analysis with all inputs
python3 scripts/bottleneck_detector.py \
    --timing timing_results.json \
    --scaling scaling_results.json \
    --memory memory_results.json \
    --json

Conversational Workflow Example

User: My simulation is taking too long. Can you help me identify what's slow?

Agent workflow:

1. Ask for simulation log file
2. Run timing analyzer:

bash python3 scripts/timing_analyzer.py --log simulation.log --json

1. Interpret results:

• If solver dominates (>50%): Recommend preconditioner tuning
• If assembly dominates: Recommend caching or vectorization
• If I/O dominates: Recommend reducing output frequency

1. If user has multi-run data, analyze scaling:

bash python3 scripts/scaling_analyzer.py --data scaling.json --type strong --json

1. Generate comprehensive recommendations:

bash python3 scripts/bottleneck_detector.py --timing timing.json --scaling scaling.json --json

Interpretation Guidance

Timing Analysis

| Scenario | Meaning | Action | |----------|---------|--------| | Solver >70% | Solver-dominated | Tune preconditioner, check tolerance | | Assembly >50% | Assembly-dominated | Cache matrices, vectorize, parallelize | | I/O >30% | I/O-dominated | Reduce frequency, use parallel I/O | | Balanced (<30% each) | Well-balanced | Look for algorithmic improvements |

Scaling Analysis

| Efficiency | Meaning | Action | |------------|---------|--------| | >0.80 | Excellent scaling | Continue scaling up | | 0.70-0.80 | Good scaling | Monitor at larger scales | | 0.50-0.70 | Poor scaling | Investigate communication/load balance | | <0.50 | Very poor scaling | Reduce processor count or redesign |

Memory Profile

| Usage | Meaning | Action | |-------|---------|--------| | <60% available | Safe | No action needed | | 60-80% available | Moderate | Monitor, consider optimization | | >80% available | High | Reduce resolution or increase processors | | >100% available | Exceeds capacity | Must reduce problem size |

Error Handling

| Error | Cause | Resolution | |-------|-------|------------| | Log file not found | Invalid path | Verify log file path | | No timing data found | Pattern mismatch | Provide custom pattern with --pattern | | At least 2 runs required | Insufficient data | Provide more scaling runs | | Missing required parameters | Incomplete params | Add mesh and fields to params file |

Optimization Strategies by Bottleneck Type

Solver Bottlenecks

• Use algebraic multigrid (AMG) preconditioner
• Tighten solver tolerance if over-solving
• Consider direct solver for small problems
• Profile matrix assembly vs solve time

Assembly Bottlenecks

• Cache element matrices if geometry is static
• Use vectorized assembly routines
• Consider matrix-free methods
• Parallelize assembly with coloring

I/O Bottlenecks

• Reduce output frequency
• Use parallel I/O (HDF5, MPI-IO)
• Write to fast scratch storage
• Compress output data

Scaling Bottlenecks

• Investigate communication overhead
• Check for load imbalance
• Reduce synchronization points
• Use asynchronous communication
• Consider hybrid MPI+OpenMP

Memory Bottlenecks

• Reduce mesh resolution
• Use iterative solver (lower memory than direct)
• Enable out-of-core computation
• Increase number of processors
• Use single precision where appropriate

Limitations

• Log parsing: Depends on pattern matching; may miss unusual formats
• Scaling analysis: Requires at least 2 runs for meaningful results
• Memory estimation: Approximate; actual usage may vary
• Recommendations: General guidance; may need domain-specific tuning

References

• references/profiling_guide.md - Profiling concepts and interpretation
• references/optimization_strategies.md - Detailed optimization approaches

Version History

• v1.0.0 (2025-01-22): Initial release with 4 profiling scripts