# simulation-orchestrator

> Orchestrate multi-simulation campaigns including parameter sweeps, batch jobs, and result aggregation. Use for running parameter studies, managing simulation batches, tracking job status, combining results from multiple runs, or automating simulation workflows.

- Author: Hesham Salama
- Repository: HeshamFS/materials-simulation-skills
- Version: 20260123035059
- Stars: 14
- Forks: 0
- Last Updated: 2026-02-06
- Source: https://github.com/HeshamFS/materials-simulation-skills
- Web: https://mule.run/skillshub/@@HeshamFS/materials-simulation-skills~simulation-orchestrator:20260123035059

---

---
name: simulation-orchestrator
description: Orchestrate multi-simulation campaigns including parameter sweeps, batch jobs, and result aggregation. Use for running parameter studies, managing simulation batches, tracking job status, combining results from multiple runs, or automating simulation workflows.
allowed-tools: Read, Bash, Write, Grep, Glob
---

# 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:

```bash
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:

```bash
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:

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

### Step 4: Aggregate Results

Combine results from completed runs:

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

## CLI Examples

```bash
# 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:
   ```bash
   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:
   ```bash
   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:
   ```bash
   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