simulation-orchestrator（模拟协调器）

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:

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

Campaign Size Guidelines

Script Outputs (JSON Fields)

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

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

Security

Input Validation

• Metric names are validated against [a-zA-Z_][a-zA-Z0-9_.]* to prevent traversal or injection via crafted keys
• campaign_manager.py validates command templates to reject shell chaining operators (;, |, &, backticks, $)
• --params format strings are parsed and validated (name:min:max:count with finite numeric bounds and positive integer counts)
• --method is validated against a fixed allowlist (grid, linspace, lhs)
• --samples is validated as a positive integer with an upper bound
• --action is validated against a fixed allowlist (init, status)

File Access

• sweep_generator.py reads a single base config file (JSON) specified by --base-config and writes generated configs to --output-dir
• result_aggregator.py enforces a 10 MB file-size limit per result file, maximum JSON nesting depth, and strict numeric type checking (rejects bool, NaN, Inf)
• All string values from result files are sanitized (truncated, control characters stripped) before surfacing them
• Config paths interpolated into shell commands are validated against a safe-character allowlist and escaped with shlex.quote()

Tool Restrictions

• Read: Used to inspect script source, references, base configs, and campaign status files
• Write: Used to save generated sweep configs, campaign manifests, and aggregated results; writes are scoped to the user's working directory
• Grep/Glob: Used to locate campaign files, result files, and search references
• The skill's allowed-tools excludes Bash to prevent the agent from executing arbitrary commands when processing untrusted simulation outputs

Safety Measures

• No eval(), exec(), or dynamic code generation
• All subprocess calls use explicit argument lists (no shell=True)
• Reduced tool surface (no Bash) limits the agent to read/write operations only
• Command templates are validated but never executed by the skill itself; execution is the user's responsibility

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