parallel-execution-optimizer

Executing independent tasks in parallel requires batching non-dependent operations into layers using a dependency graph. This approach dispatches all eligible operations simultaneously within a single message to achieve 3-5x speedups while preserving result integrity.

Yes, you can automate parallel execution for phases like implement and optimize. The optimizer analyzes these common project phases to identify independent operations, batching them into parallel layers to maximize speedups while preserving determinism.

Dependency graph batching works by mapping task relationships, grouping non-dependent tasks into execution layers, and dispatching all tasks in a single message. This ensures operations run simultaneously when safe, achieving 3-5x speedups without compromising correctness.

When dependency constraints or resource conflicts are detected during parallel batching, the system provides safe guidance and rollback strategies. It ensures operations are only dispatched in parallel when safe, preserving determinism and result integrity.

No, you do not need to manually identify independent operations. The optimizer automatically constructs a dependency graph from your current plan, analyzes dependencies, and safely batches eligible independent operations for parallel execution.

Yes, parallel execution optimizer is safe for ship-preflight and prototype phases. It provides safe failure handling and clear aggregation, ensuring result integrity and determinism are preserved throughout the parallel dispatch process.