simulation-orchestrator

Simulation Orchestrator

Goal

Provide tools to manage multi-simulation campaigns: generate parameter sweeps, track job execution status, and aggregate results from completed runs.

Requirements

• Python 3.10+
• No external dependencies (uses Python standard library only)
• Works on Linux, macOS, and Windows

Inputs to Gather

Before running orchestration scripts, collect from the user:

Input	Description	Example
Base config	Template simulation configuration	base_config.json
Parameter ranges	Parameters to sweep with bounds	dt:[1e-4,1e-2],kappa:[0.1,1.0]
Sweep method	How to sample parameter space	grid, lhs, linspace
Output directory	Where to store campaign files	./campaign_001
Simulation command	Command to run each simulation	python sim.py --config {config}

Decision Guidance

Choosing a Sweep Method

Need every combination (full factorial)?
├── YES → Use grid (warning: exponential growth with parameters)
└── NO → Is space-filling coverage needed?
    ├── YES → Use lhs (Latin Hypercube Sampling)
    └── NO → Use linspace for uniform sampling per parameter

Method	Best For	Sample Count
grid	Low dimensions (1-3), need exact corners	n^d (exponential)
linspace	1D sweeps, uniform spacing	n per parameter
lhs	High dimensions, space-filling	user-specified budget

Campaign Size Guidelines

Parameters	Grid Points Each	Total Runs	Recommendation
1	10	10	Grid is fine
2	10	100	Grid acceptable
3	10	1,000	Consider LHS
4+	10	10,000+	Use LHS or DOE

Script Outputs (JSON Fields)

Script	Output Fields
scripts/sweep_generator.py	configs, parameter_space, sweep_method, total_runs
scripts/campaign_manager.py	campaign_id, status, jobs, progress
scripts/job_tracker.py	job_id, status, start_time, end_time, exit_code
scripts/result_aggregator.py	summary, statistics, best_run, failed_runs

Workflow

Step 1: Generate Parameter Sweep

Create configurations for all parameter combinations:

python3 scripts/sweep_generator.py \
    --base-config base_config.json \
    --params "dt:1e-4:1e-2:5,kappa:0.1:1.0:3" \
    --method linspace \
    --output-dir ./campaign_001 \
    --json

Step 2: Initialize Campaign

Create campaign tracking structure:

python3 scripts/campaign_manager.py \
    --action init \
    --config-dir ./campaign_001 \
    --command "python sim.py --config {config}" \
    --json

Step 3: Track Job Status

Monitor running jobs:

python3 scripts/job_tracker.py \
    --campaign-dir ./campaign_001 \
    --update \
    --json

Step 4: Aggregate Results

Combine results from completed runs:

python3 scripts/result_aggregator.py \
    --campaign-dir ./campaign_001 \
    --metric objective_value \
    --json

CLI Examples

# Generate 5x3=15 runs varying dt (5 values) and kappa (3 values)
python3 scripts/sweep_generator.py \
    --base-config sim.json \
    --params "dt:1e-4:1e-2:5,kappa:0.1:1.0:3" \
    --method linspace \
    --output-dir ./sweep_001 \
    --json

# Generate LHS samples for 4 parameters with budget of 20 runs
python3 scripts/sweep_generator.py \
    --base-config sim.json \
    --params "dt:1e-4:1e-2,kappa:0.1:1.0,M:1e-6:1e-4,W:0.5:2.0" \
    --method lhs \
    --samples 20 \
    --output-dir ./lhs_001 \
    --json

# Check campaign status
python3 scripts/campaign_manager.py \
    --action status \
    --config-dir ./sweep_001 \
    --json

# Get summary statistics from completed runs
python3 scripts/result_aggregator.py \
    --campaign-dir ./sweep_001 \
    --metric final_energy \
    --json

Conversational Workflow Example

User: I want to run a parameter sweep on dt and kappa for my phase-field simulation. I want to try 5 values of dt between 1e-4 and 1e-2, and 4 values of kappa between 0.1 and 1.0.

Agent workflow:

1. Calculate total runs: 5 x 4 = 20 runs
2. Generate sweep configurations:

   python3 scripts/sweep_generator.py \
       --base-config simulation.json \
       --params "dt:1e-4:1e-2:5,kappa:0.1:1.0:4" \
       --method linspace \
       --output-dir ./dt_kappa_sweep \
       --json
   

3. Initialize campaign:

   python3 scripts/campaign_manager.py \
       --action init \
       --config-dir ./dt_kappa_sweep \
       --command "python phase_field.py --config {config}" \
       --json
   

4. After user runs simulations, aggregate results:

   python3 scripts/result_aggregator.py \
       --campaign-dir ./dt_kappa_sweep \
       --metric interface_width \
       --json
   

Error Handling

Error	Cause	Resolution
Base config not found	Invalid file path	Verify base config file exists
Invalid parameter format	Malformed param string	Use format name:min:max:count or name:min:max
Output directory exists	Would overwrite	Use --force or choose new directory
No completed jobs	No results to aggregate	Wait for jobs to complete or check for failures
Metric not found	Result files missing field	Verify metric name in result JSON

Integration with Other Skills

The simulation-orchestrator works with other simulation-workflow skills:

parameter-optimization          simulation-orchestrator
        │                              │
        │ DOE samples ────────────────>│ Generate configs
        │                              │
        │                              │ Run simulations
        │                              │
        │<──────────────────────────── │ Aggregate results
        │                              │
        │ Sensitivity analysis         │
        │ Optimizer selection          │

Typical Combined Workflow

1. Use parameter-optimization/doe_generator.py to get sample points
2. Use simulation-orchestrator/sweep_generator.py to create configs
3. Run simulations (user's responsibility)
4. Use simulation-orchestrator/result_aggregator.py to collect results
5. Use parameter-optimization/sensitivity_summary.py to analyze

Limitations

• Not a job scheduler: Does not submit jobs to SLURM/PBS; generates configs and tracks status
• No parallel execution: User must run simulations externally (can use GNU parallel, SLURM, etc.)
• File-based tracking: Status tracked via files; no database or real-time monitoring
• Local filesystem: Assumes all files accessible from local machine

References

• references/campaign_patterns.md - Common campaign structures
• references/sweep_strategies.md - Parameter sweep design guidance
• references/aggregation_methods.md - Result aggregation techniques

Version History

• v1.0.0 (2024-12-24): Initial release with sweep, campaign, tracking, and aggregation