6ed69a3d48
Major changes: - Organized Python v1 implementation into v1/ subdirectory - Created Rust workspace with 9 modular crates: - wifi-densepose-core: Core types, traits, errors - wifi-densepose-signal: CSI processing, phase sanitization, FFT - wifi-densepose-nn: Neural network inference (ONNX/Candle/tch) - wifi-densepose-api: Axum-based REST/WebSocket API - wifi-densepose-db: SQLx database layer - wifi-densepose-config: Configuration management - wifi-densepose-hardware: Hardware abstraction - wifi-densepose-wasm: WebAssembly bindings - wifi-densepose-cli: Command-line interface Documentation: - ADR-001: Workspace structure - ADR-002: Signal processing library selection - ADR-003: Neural network inference strategy - DDD domain model with bounded contexts Testing: - 69 tests passing across all crates - Signal processing: 45 tests - Neural networks: 21 tests - Core: 3 doc tests Performance targets: - 10x faster CSI processing (~0.5ms vs ~5ms) - 5x lower memory usage (~100MB vs ~500MB) - WASM support for browser deployment
1.2 KiB
1.2 KiB
Parallel Task Execution
Purpose
Execute independent subtasks in parallel for maximum efficiency.
Coordination Strategy
1. Task Decomposition
Tool: mcp__claude-flow__task_orchestrate
Parameters: {
"task": "Build complete REST API with auth, CRUD operations, and tests",
"strategy": "parallel",
"maxAgents": 8
}
2. Parallel Workflows
The system automatically:
- Identifies independent components
- Assigns specialized agents
- Executes in parallel where possible
- Synchronizes at dependency points
3. Example Breakdown
For the REST API task:
- Agent 1 (Architect): Design API structure
- Agent 2-3 (Coders): Implement auth & CRUD in parallel
- Agent 4 (Tester): Write tests as features complete
- Agent 5 (Documenter): Update docs continuously
CLI Usage
# Execute parallel tasks via CLI
npx claude-flow parallel "Build REST API" --max-agents 8
Performance Gains
- 🚀 2.8-4.4x faster execution
- 💪 Optimal CPU utilization
- 🔄 Automatic load balancing
- 📈 Linear scalability with agents
Monitoring
Tool: mcp__claude-flow__swarm_monitor
Parameters: {"interval": 1000, "swarmId": "current"}
Watch real-time parallel execution progress!