Masonmason
a099c56bb5
- Rebrand README from InferencePipeline to Cluster4NPU UI Visual Pipeline Designer - Focus documentation on PyQt5-based GUI and drag-and-drop workflow - Update PROJECT_SUMMARY with current capabilities and focused development priorities - Streamline DEVELOPMENT_ROADMAP with 4-phase implementation plan - Remove redundant Chinese technical summary files (STAGE_IMPROVEMENTS_SUMMARY.md, UI_FIXES_SUMMARY.md, STATUS_BAR_FIXES_SUMMARY.md) - Align all documentation with actual three-panel UI architecture and NodeGraphQt integration 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
131 lines
4.9 KiB
Markdown
131 lines
4.9 KiB
Markdown
# 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 |