cluster4npu/DEVELOPMENT_ROADMAP.md

Development Roadmap

Mission

Create an intuitive visual pipeline designer that demonstrates clear speedup benefits of parallel NPU processing through real-time performance visualization and automated optimization.

🎯 Core Development Goals

1. Performance Visualization (Critical)

• Speedup Metrics: Clear display of 2x, 3x, 4x performance improvements
• Before/After Comparison: Visual proof of parallel processing benefits
• Device Utilization: Real-time visualization of NPU usage
• Execution Flow: Visual representation of parallel processing paths

2. Benchmarking System (Critical)

• Automated Testing: One-click performance measurement
• Comparison Charts: Single vs multi-device performance analysis
• Regression Testing: Track performance over time
• Optimization Suggestions: Automated recommendations

3. Device Management (High Priority)

• Visual Dashboard: Device status and health monitoring
• Manual Allocation: Drag-and-drop device assignment
• Load Balancing: Optimal distribution across available NPUs
• Performance Profiling: Individual device performance tracking

4. Real-time Monitoring (High Priority)

• Live Charts: FPS, latency, and throughput graphs
• Resource Monitoring: CPU, memory, and NPU utilization
• Bottleneck Detection: Automated identification of performance issues
• Alert System: Warnings for performance degradation

📋 Implementation Plan

Phase 1: Performance Visualization (Weeks 1-2)

Core Components:

• PerformanceBenchmarker class for automated testing
• PerformanceDashboard widget with live charts
• Speedup calculation and display widgets
• Integration with existing pipeline editor

Deliverables:

• Single vs multi-device benchmark comparison
• Real-time FPS and latency monitoring
• Visual speedup indicators (e.g., "3.2x FASTER")
• Performance history tracking

Phase 2: Device Management (Weeks 3-4)

Core Components:

• DeviceManager with enhanced NPU control
• DeviceManagementPanel for visual allocation
• Device health monitoring and profiling
• Load balancing optimization algorithms

Deliverables:

• Visual device status dashboard
• Drag-and-drop device assignment interface
• Device performance profiling and history
• Automatic load balancing recommendations

Phase 3: Advanced Features (Weeks 5-6)

Core Components:

• OptimizationEngine for automated suggestions
• Pipeline analysis and bottleneck detection
• Configuration templates and presets
• Performance prediction algorithms

Deliverables:

• Automated pipeline optimization suggestions
• Configuration templates for common use cases
• Performance prediction before execution
• Bottleneck identification and resolution

Phase 4: Professional Polish (Weeks 7-8)

Core Components:

• Advanced visualization and reporting
• Export and documentation features
• Performance analytics and insights
• User experience refinements

Deliverables:

• Professional performance reports
• Advanced analytics and trending
• Export capabilities for results
• Comprehensive user documentation

🎨 Target User Experience

Ideal Workflow

1. Design (< 5 minutes): Drag-and-drop pipeline creation
2. Configure: Automatic device detection and optimal allocation
3. Benchmark: One-click performance measurement
4. Monitor: Real-time speedup visualization during execution
5. Optimize: Automated suggestions for performance improvements

Success Metrics

• Speedup Visibility: Clear before/after performance comparison
• Ease of Use: Intuitive interface requiring minimal training
• Performance Gains: Measurable improvements from optimization
• Professional Quality: Enterprise-ready monitoring and reporting

🛠 Technical Approach

Extend Current Architecture

• Build on existing InferencePipeline and Multidongle classes
• Enhance UI with new performance panels and dashboards
• Integrate visualization libraries (matplotlib/pyqtgraph)
• Add benchmarking automation and result storage

Key Technical Components

• Performance Engine: Automated benchmarking and comparison
• Visualization Layer: Real-time charts and progress indicators
• Device Abstraction: Enhanced NPU management and allocation
• Optimization Logic: Automated analysis and suggestions

📈 Expected Impact

For Users

• Simplified Setup: No coding required for parallel processing
• Clear Benefits: Visual proof of performance improvements
• Optimal Performance: Automated hardware utilization
• Professional Tools: Enterprise-grade monitoring and analytics

For Platform

• Competitive Advantage: Unique visual approach to parallel AI inference
• Market Expansion: Lower barrier to entry for non-technical users
• Performance Leadership: Systematic optimization of NPU utilization
• Enterprise Ready: Foundation for advanced features and scaling