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
50 lines
1.2 KiB
Markdown
50 lines
1.2 KiB
Markdown
# Parallel Task Execution
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## Purpose
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Execute independent subtasks in parallel for maximum efficiency.
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## Coordination Strategy
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### 1. Task Decomposition
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```
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Tool: mcp__claude-flow__task_orchestrate
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Parameters: {
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"task": "Build complete REST API with auth, CRUD operations, and tests",
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"strategy": "parallel",
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"maxAgents": 8
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}
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```
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### 2. Parallel Workflows
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The system automatically:
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- Identifies independent components
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- Assigns specialized agents
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- Executes in parallel where possible
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- Synchronizes at dependency points
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### 3. Example Breakdown
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For the REST API task:
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- **Agent 1 (Architect)**: Design API structure
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- **Agent 2-3 (Coders)**: Implement auth & CRUD in parallel
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- **Agent 4 (Tester)**: Write tests as features complete
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- **Agent 5 (Documenter)**: Update docs continuously
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## CLI Usage
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```bash
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# Execute parallel tasks via CLI
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npx claude-flow parallel "Build REST API" --max-agents 8
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```
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## Performance Gains
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- 🚀 2.8-4.4x faster execution
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- 💪 Optimal CPU utilization
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- 🔄 Automatic load balancing
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- 📈 Linear scalability with agents
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## Monitoring
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```
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Tool: mcp__claude-flow__swarm_monitor
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Parameters: {"interval": 1000, "swarmId": "current"}
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```
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Watch real-time parallel execution progress! |