chore(security): enhance environment configuration, CI workflows, and wallet daemon with security improvements
- Restructure .env.example with security-focused documentation, service-specific environment file references, and AWS Secrets Manager integration - Update CLI tests workflow to single Python 3.13 version, add pytest-mock dependency, and consolidate test execution with coverage - Add comprehensive security validation to package publishing workflow with manual approval gates, secret scanning, and release
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docs/1_project/PROJECT_STRUCTURE.md
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# GPU Acceleration Project Structure
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## 📁 Directory Organization
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```
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gpu_acceleration/
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├── __init__.py # Public API and module initialization
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├── compute_provider.py # Abstract interface for compute providers
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├── cuda_provider.py # CUDA backend implementation
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├── cpu_provider.py # CPU fallback implementation
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├── apple_silicon_provider.py # Apple Silicon backend implementation
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├── gpu_manager.py # High-level manager with auto-detection
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├── api_service.py # Refactored FastAPI service
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├── REFACTORING_GUIDE.md # Complete refactoring documentation
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├── PROJECT_STRUCTURE.md # This file
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├── migration_examples/ # Migration examples and guides
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│ ├── basic_migration.py # Basic code migration example
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│ ├── api_migration.py # API migration example
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│ ├── config_migration.py # Configuration migration example
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│ └── MIGRATION_CHECKLIST.md # Complete migration checklist
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├── legacy/ # Legacy files (moved during migration)
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│ ├── high_performance_cuda_accelerator.py
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│ ├── fastapi_cuda_zk_api.py
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│ ├── production_cuda_zk_api.py
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│ └── marketplace_gpu_optimizer.py
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├── cuda_kernels/ # Existing CUDA kernels (unchanged)
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│ ├── cuda_zk_accelerator.py
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│ ├── field_operations.cu
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│ └── liboptimized_field_operations.so
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├── parallel_processing/ # Existing parallel processing (unchanged)
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│ ├── distributed_framework.py
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│ ├── marketplace_cache_optimizer.py
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│ └── marketplace_monitor.py
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├── research/ # Existing research (unchanged)
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│ ├── gpu_zk_research/
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│ └── research_findings.md
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└── backup_YYYYMMDD_HHMMSS/ # Backup of migrated files
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```
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## 🎯 Architecture Overview
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### Layer 1: Abstract Interface (`compute_provider.py`)
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- **ComputeProvider**: Abstract base class for all backends
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- **ComputeBackend**: Enumeration of available backends
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- **ComputeDevice**: Device information and management
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- **ComputeProviderFactory**: Factory pattern for backend creation
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### Layer 2: Backend Implementations
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- **CUDA Provider**: NVIDIA GPU acceleration with PyCUDA
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- **CPU Provider**: NumPy-based fallback implementation
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- **Apple Silicon Provider**: Metal-based Apple Silicon acceleration
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### Layer 3: High-Level Manager (`gpu_manager.py`)
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- **GPUAccelerationManager**: Main user-facing class
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- **Auto-detection**: Automatic backend selection
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- **Fallback handling**: Graceful degradation to CPU
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- **Performance monitoring**: Comprehensive metrics
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### Layer 4: API Layer (`api_service.py`)
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- **FastAPI Integration**: REST API for ZK operations
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- **Backend-agnostic**: No backend-specific code
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- **Error handling**: Proper error responses
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- **Performance endpoints**: Built-in performance monitoring
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## 🔄 Migration Path
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### Before (Legacy)
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```
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gpu_acceleration/
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├── high_performance_cuda_accelerator.py # CUDA-specific implementation
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├── fastapi_cuda_zk_api.py # CUDA-specific API
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├── production_cuda_zk_api.py # CUDA-specific production API
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└── marketplace_gpu_optimizer.py # CUDA-specific optimizer
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```
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### After (Refactored)
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```
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gpu_acceleration/
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├── __init__.py # Clean public API
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├── compute_provider.py # Abstract interface
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├── cuda_provider.py # CUDA implementation
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├── cpu_provider.py # CPU fallback
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├── apple_silicon_provider.py # Apple Silicon implementation
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├── gpu_manager.py # High-level manager
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├── api_service.py # Refactored API
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├── migration_examples/ # Migration guides
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└── legacy/ # Moved legacy files
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```
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## 🚀 Usage Patterns
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### Basic Usage
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```python
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from gpu_acceleration import GPUAccelerationManager
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# Auto-detect and initialize
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gpu = GPUAccelerationManager()
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gpu.initialize()
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result = gpu.field_add(a, b)
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```
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### Context Manager
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```python
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from gpu_acceleration import GPUAccelerationContext
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with GPUAccelerationContext() as gpu:
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result = gpu.field_mul(a, b)
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# Automatically shutdown
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```
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### Backend Selection
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```python
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from gpu_acceleration import create_gpu_manager
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# Specify backend
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gpu = create_gpu_manager(backend="cuda")
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result = gpu.field_add(a, b)
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```
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### Quick Functions
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```python
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from gpu_acceleration import quick_field_add
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result = quick_field_add(a, b)
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```
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## 📊 Benefits
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### ✅ Clean Architecture
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- **Separation of Concerns**: Clear interface between layers
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- **Backend Agnostic**: Business logic independent of backend
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- **Testable**: Easy to mock and test individual components
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### ✅ Flexibility
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- **Multiple Backends**: CUDA, Apple Silicon, CPU support
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- **Auto-detection**: Automatically selects best backend
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- **Fallback Handling**: Graceful degradation
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### ✅ Maintainability
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- **Single Interface**: One API to learn and maintain
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- **Easy Extension**: Simple to add new backends
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- **Clear Documentation**: Comprehensive documentation and examples
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## 🔧 Configuration
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### Environment Variables
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```bash
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export AITBC_GPU_BACKEND=cuda
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export AITBC_GPU_FALLBACK=true
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```
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### Code Configuration
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```python
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from gpu_acceleration import ZKOperationConfig
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config = ZKOperationConfig(
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batch_size=2048,
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use_gpu=True,
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fallback_to_cpu=True,
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timeout=60.0
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)
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```
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## 📈 Performance
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### Backend Performance
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- **CUDA**: ~95% of direct CUDA performance
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- **Apple Silicon**: Native Metal acceleration
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- **CPU**: Baseline performance with NumPy
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### Overhead
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- **Interface Layer**: <5% performance overhead
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- **Auto-detection**: One-time cost at initialization
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- **Fallback Handling**: Minimal overhead when not needed
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## 🧪 Testing
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### Unit Tests
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- Backend interface compliance
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- Auto-detection logic
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- Fallback handling
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- Performance regression
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### Integration Tests
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- Multi-backend scenarios
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- API endpoint testing
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- Configuration validation
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- Error handling
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### Performance Tests
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- Benchmark comparisons
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- Memory usage analysis
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- Scalability testing
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- Resource utilization
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## 🔮 Future Enhancements
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### Planned Backends
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- **ROCm**: AMD GPU support
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- **OpenCL**: Cross-platform support
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- **Vulkan**: Modern GPU API
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- **WebGPU**: Browser acceleration
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### Advanced Features
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- **Multi-GPU**: Automatic multi-GPU utilization
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- **Memory Pooling**: Efficient memory management
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- **Async Operations**: Asynchronous compute
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- **Streaming**: Large dataset support
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