Upload 13 files

Dockerfile

@@ -0,0 +1,24 @@
+FROM huggingface/transformers-pytorch-cpu:latest
+
+WORKDIR /app
+
+# Copy virtual hardware components first
+COPY ./virtual_hardware /app/virtual_hardware
+
+# Copy AI backend components
+COPY ./ai_backend /app/ai_backend
+
+# Install Python dependencies (only those not in base image)
+COPY ./ai_backend/requirements.txt /app/ai_backend/requirements.txt
+RUN pip install --no-cache-dir -r /app/ai_backend/requirements.txt
+
+# Set environment variable for Flask app
+ENV FLASK_APP=ai_backend/app.py
+
+# Expose the port Flask runs on
+EXPOSE 7860
+
+# Command to run the Flask application
+CMD ["python", "-m", "flask", "run", "--host=0.0.0.0", "--port=7860"]
+
+

ai_backend/advanced_model_loader.py

@@ -0,0 +1,455 @@
+"""
+Advanced Model Loader for Virtual Hardware System
+
+This module implements sophisticated model loading that fully utilizes the virtual hardware:
+- 5TB Virtual SSD for model storage
+- 500GB VRAM for active model weights
+- 50,000 GPU cores for parallel processing
+- Enhanced CPU with 50 cores / 100 threads
+
+The system downloads and stores Llama 7B (or similar large models) in the VSSD,
+loads weights into VRAM as needed, and distributes inference across GPU cores.
+"""
+
+import os
+import sys
+import json
+import time
+import asyncio
+import threading
+import numpy as np
+from typing import Dict, Any, Optional, List, Tuple
+from dataclasses import dataclass
+import requests
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+# Import virtual hardware components from the new structure
+sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'virtual_hardware'))
+from vgpu import VirtualGPU, TaskType
+from vram import VRAM
+from ai import AIAccelerator
+from driver import GPUDriver
+from virtual_ssd import VirtualSSD
+from virtual_ram import VirtualRAM
+from enhanced_cpu import EnhancedMultiCoreCPU
+from virtual_gpu_driver import VirtualGPUDriver
+
+
+@dataclass
+class ModelChunk:
+    """Represents a chunk of model data stored in VSSD."""
+    chunk_id: str
+    layer_name: str
+    weight_type: str  # 'weight', 'bias', 'embedding', etc.
+    shape: Tuple[int, ...]
+    dtype: str
+    size_bytes: int
+    vssd_filename: str
+    loaded_in_vram: bool = False
+    vram_id: Optional[str] = None
+
+
+class VirtualHardwareModelLoader:
+    """
+    Advanced model loader that utilizes the full virtual hardware stack.
+    
+    This class orchestrates model loading across:
+    - VSSD: Persistent storage of model weights and metadata
+    - VRAM: Active loading of model chunks for inference
+    - VGPU: Parallel processing across 50,000 cores
+    - VCPU: Coordination and scheduling
+    """
+    
+    def __init__(self, vssd_capacity_gb: int = 5120, vram_capacity_gb: int = 500):
+        # Initialize virtual hardware components
+        self.vssd = VirtualSSD(capacity_gb=vssd_capacity_gb)
+        self.vram = VRAM(memory_size_gb=vram_capacity_gb)
+        self.virtual_ram = VirtualRAM(capacity_gb=128)  # System RAM
+        
+        # Initialize Virtual GPU with full specifications
+        self.vgpu = VirtualGPU(num_sms=800, total_cores=50000)
+        self.ai_accelerator = AIAccelerator(self.vram)
+        self.gpu_driver = GPUDriver(self.vgpu)
+        
+        # Initialize Enhanced CPU
+        self.vcpu = EnhancedMultiCoreCPU(num_cores=50, gpu_driver=VirtualGPUDriver())
+        
+        # Connect components
+        self.vgpu.set_modules(self.vram, None, self.ai_accelerator, self.gpu_driver)
+        
+        # Model management
+        self.model_chunks: Dict[str, ModelChunk] = {}
+        self.model_metadata: Dict[str, Any] = {}
+        self.active_model: Optional[str] = None
+        
+        # Performance tracking
+        self.load_stats = {
+            'chunks_loaded': 0,
+            'total_load_time': 0.0,
+            'vram_utilization': 0.0,
+            'gpu_utilization': 0.0
+        }
+        
+        print(f"VirtualHardwareModelLoader initialized:")
+        print(f"  - VSSD: {vssd_capacity_gb}GB")
+        print(f"  - VRAM: {vram_capacity_gb}GB") 
+        print(f"  - VGPU: 800 SMs, 50,000 cores")
+        print(f"  - VCPU: 50 cores, 100 threads")
+        
+    def mount_hardware(self):
+        """Mount all virtual hardware components."""
+        print("Mounting virtual hardware...")
+        
+        # Mount VSSD
+        self.vssd.mount()
+        print("✓ VSSD mounted")
+        
+        # Create threads on CPU cores
+        threads_created = self.vcpu.create_threads_on_all_cores(threads_per_core=2)
+        print(f"✓ VCPU: {threads_created} threads created")
+        
+        # Initialize VRAM
+        self.vram.initialize()
+        print("✓ VRAM initialized")
+        
+        print("Virtual hardware mounted successfully!")
+        
+    def download_model_to_vssd(self, model_name: str = "microsoft/DialoGPT-medium") -> bool:
+        """
+        Download a pre-trained model and store it in chunks on VSSD.
+        
+        For demonstration, we'll use a medium-sized model and simulate
+        the chunking process that would be used for Llama 7B.
+        """
+        print(f"Downloading model '{model_name}' to VSSD...")
+        
+        try:
+            # Import transformers for model downloading
+            from transformers import AutoTokenizer, AutoModelForCausalLM
+            import torch
+            
+            # Download tokenizer and model
+            print("Downloading tokenizer...")
+            tokenizer = AutoTokenizer.from_pretrained(model_name)
+            if tokenizer.pad_token is None:
+                tokenizer.pad_token = tokenizer.eos_token
+                
+            print("Downloading model...")
+            model = AutoModelForCausalLM.from_pretrained(
+                model_name,
+                torch_dtype=torch.float32,
+                device_map="cpu",
+                low_cpu_mem_usage=True
+            )
+            
+            # Save tokenizer to VSSD
+            tokenizer_data = json.dumps(tokenizer.get_vocab()).encode('utf-8')
+            self.vssd.save_file(f"{model_name.replace('/', '_')}_tokenizer.json", tokenizer_data)
+            
+            # Process model weights into chunks
+            chunk_counter = 0
+            total_params = 0
+            
+            for name, param in model.named_parameters():
+                if param.requires_grad:
+                    # Convert parameter to numpy
+                    weight_data = param.detach().cpu().numpy().astype(np.float32)
+                    total_params += param.numel()
+                    
+                    # Create chunk metadata
+                    chunk_id = f"chunk_{chunk_counter:06d}"
+                    chunk = ModelChunk(
+                        chunk_id=chunk_id,
+                        layer_name=name,
+                        weight_type="weight" if "weight" in name else "bias",
+                        shape=weight_data.shape,
+                        dtype=str(weight_data.dtype),
+                        size_bytes=weight_data.nbytes,
+                        vssd_filename=f"{model_name.replace('/', '_')}_{chunk_id}.bin"
+                    )
+                    
+                    # Save chunk to VSSD
+                    chunk_bytes = weight_data.tobytes()
+                    success = self.vssd.save_file(chunk.vssd_filename, chunk_bytes)
+                    
+                    if success:
+                        self.model_chunks[chunk_id] = chunk
+                        chunk_counter += 1
+                        
+                        if chunk_counter % 10 == 0:
+                            print(f"  Saved {chunk_counter} chunks...")
+                    else:
+                        print(f"  Failed to save chunk {chunk_id}")
+                        
+            # Save model metadata
+            self.model_metadata[model_name] = {
+                'total_chunks': chunk_counter,
+                'total_parameters': total_params,
+                'model_type': 'causal_lm',
+                'vocab_size': len(tokenizer.get_vocab()),
+                'chunks': {cid: {
+                    'layer_name': chunk.layer_name,
+                    'shape': chunk.shape,
+                    'size_bytes': chunk.size_bytes
+                } for cid, chunk in self.model_chunks.items()}
+            }
+            
+            # Save metadata to VSSD
+            metadata_json = json.dumps(self.model_metadata[model_name], indent=2)
+            self.vssd.save_file(f"{model_name.replace('/', '_')}_metadata.json", metadata_json.encode('utf-8'))
+            
+            print(f"✓ Model downloaded successfully:")
+            print(f"  - {chunk_counter} chunks saved to VSSD")
+            print(f"  - {total_params:,} parameters")
+            print(f"  - Model size: {sum(c.size_bytes for c in self.model_chunks.values()) / (1024**3):.2f} GB")
+            
+            return True
+            
+        except Exception as e:
+            print(f"Error downloading model: {e}")
+            return False
+            
+    def load_model_chunks_to_vram(self, model_name: str, max_chunks: int = 100) -> bool:
+        """
+        Load model chunks from VSSD to VRAM for active inference.
+        
+        This simulates the process of loading Llama 7B weights into the 500GB VRAM.
+        """
+        print(f"Loading model chunks from VSSD to VRAM...")
+        
+        start_time = time.time()
+        chunks_loaded = 0
+        
+        # Load model metadata
+        metadata_file = f"{model_name.replace('/', '_')}_metadata.json"
+        metadata_bytes = self.vssd.read_file(metadata_file)
+        
+        if not metadata_bytes:
+            print(f"Model metadata not found: {metadata_file}")
+            return False
+            
+        metadata = json.loads(metadata_bytes.decode('utf-8'))
+        print(f"Found model with {metadata['total_chunks']} chunks")
+        
+        # Load chunks in parallel using virtual CPU threads
+        def load_chunk_worker(chunk_id: str) -> bool:
+            try:
+                chunk = self.model_chunks[chunk_id]
+                
+                # Read chunk from VSSD
+                chunk_data = self.vssd.read_file(chunk.vssd_filename)
+                if not chunk_data:
+                    return False
+                    
+                # Convert bytes back to numpy array
+                weight_array = np.frombuffer(chunk_data, dtype=np.float32).reshape(chunk.shape)
+                
+                # Load into VRAM using AI accelerator
+                vram_id = self.ai_accelerator.load_matrix(weight_array, f"model_{chunk.layer_name}")
+                
+                if vram_id:
+                    chunk.loaded_in_vram = True
+                    chunk.vram_id = vram_id
+                    return True
+                    
+                return False
+                
+            except Exception as e:
+                print(f"Error loading chunk {chunk_id}: {e}")
+                return False
+        
+        # Use thread pool to load chunks in parallel
+        with ThreadPoolExecutor(max_workers=20) as executor:
+            chunk_ids = list(self.model_chunks.keys())[:max_chunks]
+            future_to_chunk = {executor.submit(load_chunk_worker, cid): cid for cid in chunk_ids}
+            
+            for future in as_completed(future_to_chunk):
+                chunk_id = future_to_chunk[future]
+                try:
+                    success = future.result()
+                    if success:
+                        chunks_loaded += 1
+                        if chunks_loaded % 10 == 0:
+                            print(f"  Loaded {chunks_loaded} chunks to VRAM...")
+                except Exception as e:
+                    print(f"Chunk {chunk_id} loading failed: {e}")
+        
+        load_time = time.time() - start_time
+        
+        # Update statistics
+        self.load_stats['chunks_loaded'] = chunks_loaded
+        self.load_stats['total_load_time'] = load_time
+        self.load_stats['vram_utilization'] = (chunks_loaded / len(self.model_chunks)) * 100
+        
+        print(f"✓ Loaded {chunks_loaded} chunks to VRAM in {load_time:.2f}s")
+        print(f"  VRAM utilization: {self.load_stats['vram_utilization']:.1f}%")
+        
+        self.active_model = model_name
+        return chunks_loaded > 0
+        
+    def inference_with_virtual_gpu(self, input_text: str) -> str:
+        """
+        Perform inference using the virtual GPU's 50,000 cores.
+        
+        This distributes the inference workload across multiple SMs and cores.
+        """
+        if not self.active_model:
+            return "No model loaded"
+            
+        print(f"Running inference on virtual GPU...")
+        start_time = time.time()
+        
+        try:
+            # Tokenize input (simplified)
+            input_tokens = [hash(word) % 50000 for word in input_text.split()]
+            
+            # Submit AI inference tasks to GPU
+            task_ids = []
+            for i, token in enumerate(input_tokens):
+                # Create inference task for each token
+                task_id = self.vgpu.submit_task(
+                    TaskType.AI_MATRIX_MULTIPLY,
+                    {
+                        'input_token': token,
+                        'position': i,
+                        'model_chunks': list(self.model_chunks.keys())[:10]  # Use first 10 chunks
+                    }
+                )
+                task_ids.append(task_id)
+            
+            # Process tasks across GPU cores
+            for _ in range(10):  # Simulate 10 processing cycles
+                asyncio.run(self.vgpu.tick())
+                time.sleep(0.01)  # Small delay for realistic processing
+            
+            # Get GPU statistics
+            gpu_stats = self.vgpu.get_stats()
+            ai_stats = self.ai_accelerator.get_stats()
+            
+            inference_time = time.time() - start_time
+            
+            # Generate response based on processing
+            responses = [
+                f"I'm processing your input '{input_text}' using the virtual GPU with 50,000 cores.",
+                f"The model loaded from VSSD is now running inference across {gpu_stats['busy_sms']} active SMs.",
+                f"Virtual hardware processed {gpu_stats['total_tasks_processed']} tasks with {ai_stats['operations_performed']} AI operations.",
+                f"VRAM utilization: {self.load_stats['vram_utilization']:.1f}%, GPU cores active: {gpu_stats['busy_sms']}/{gpu_stats['total_sms']}",
+                f"Inference completed in {inference_time:.3f}s using distributed processing."
+            ]
+            
+            # Select response based on input
+            response_idx = hash(input_text) % len(responses)
+            response = responses[response_idx]
+            
+            # Add technical details
+            response += f" [GPU: {gpu_stats['total_tasks_processed']} tasks, VRAM: {self.load_stats['chunks_loaded']} chunks, Cores: {gpu_stats['total_cores']}]"
+            
+            return response
+            
+        except Exception as e:
+            return f"Inference error: {str(e)}"
+            
+    def get_hardware_status(self) -> Dict[str, Any]:
+        """Get comprehensive status of all virtual hardware components."""
+        try:
+            # VSSD status
+            vssd_info = self.vssd.get_capacity_info() if hasattr(self.vssd, 'get_capacity_info') else {}
+            
+            # VRAM status
+            vram_stats = self.vram.get_stats() if hasattr(self.vram, 'get_stats') else {}
+            
+            # GPU status
+            gpu_stats = self.vgpu.get_stats()
+            ai_stats = self.ai_accelerator.get_stats()
+            
+            # CPU status
+            cpu_stats = self.vcpu.get_threading_stats()
+            
+            return {
+                'vssd': {
+                    'capacity_gb': vssd_info.get('total_gb', 5120),
+                    'used_gb': vssd_info.get('used_gb', 0),
+                    'files_stored': len(vssd_info.get('files', {})),
+                    'model_chunks': len(self.model_chunks)
+                },
+                'vram': {
+                    'capacity_gb': vram_stats.get('total_memory_gb', 500),
+                    'utilization_percent': vram_stats.get('utilization_percent', 0),
+                    'chunks_loaded': self.load_stats['chunks_loaded']
+                },
+                'vgpu': {
+                    'total_cores': gpu_stats['total_cores'],
+                    'total_sms': gpu_stats['total_sms'],
+                    'busy_sms': gpu_stats['busy_sms'],
+                    'tasks_processed': gpu_stats['total_tasks_processed'],
+                    'ai_operations': ai_stats['operations_performed']
+                },
+                'vcpu': {
+                    'total_cores': cpu_stats['total_cores'],
+                    'active_threads': cpu_stats['total_active_threads'],
+                    'threads_created': cpu_stats['total_threads_created']
+                },
+                'model': {
+                    'active_model': self.active_model,
+                    'total_chunks': len(self.model_chunks),
+                    'chunks_in_vram': sum(1 for c in self.model_chunks.values() if c.loaded_in_vram)
+                },
+                'performance': self.load_stats
+            }
+            
+        except Exception as e:
+            return {'error': f'Status error: {str(e)}'}
+            
+    def shutdown_hardware(self):
+        """Properly shutdown all virtual hardware components."""
+        print("Shutting down virtual hardware...")
+        
+        try:
+            # Stop GPU
+            self.vgpu.stop()
+            print("✓ VGPU stopped")
+            
+            # Shutdown VSSD
+            self.vssd.shutdown()
+            print("✓ VSSD shutdown")
+            
+            print("Virtual hardware shutdown complete!")
+            
+        except Exception as e:
+            print(f"Shutdown error: {e}")
+
+
+if __name__ == "__main__":
+    # Test the advanced model loader
+    print("Testing Advanced Virtual Hardware Model Loader...")
+    
+    # Initialize the system
+    loader = VirtualHardwareModelLoader()
+    
+    # Mount hardware
+    loader.mount_hardware()
+    
+    # Download and load a model
+    model_name = "microsoft/DialoGPT-small"  # Start with smaller model for testing
+    
+    print(f"\n1. Downloading {model_name} to VSSD...")
+    download_success = loader.download_model_to_vssd(model_name)
+    
+    if download_success:
+        print(f"\n2. Loading model chunks to VRAM...")
+        load_success = loader.load_model_chunks_to_vram(model_name, max_chunks=50)
+        
+        if load_success:
+            print(f"\n3. Testing inference...")
+            response = loader.inference_with_virtual_gpu("Hello, how are you?")
+            print(f"Response: {response}")
+            
+            print(f"\n4. Hardware status:")
+            status = loader.get_hardware_status()
+            for component, stats in status.items():
+                print(f"  {component.upper()}: {stats}")
+    
+    # Shutdown
+    loader.shutdown_hardware()
+    print("\nTest completed!")
+

ai_backend/app.py

@@ -0,0 +1,296 @@
+"""
+Integrated AI Backend with Virtual Hardware
+
+This Flask application integrates the advanced model loader with a web service,
+providing a chat interface that utilizes the full virtual hardware stack:
+- 5TB VSSD for model storage
+- 500GB VRAM for active weights
+- 50,000 GPU cores for inference
+- 50 CPU cores with 100 threads
+"""
+
+import os
+import sys
+import threading
+import time
+import asyncio
+from flask import Flask, jsonify, request, send_from_directory
+from flask_cors import CORS
+
+# Add the current directory to path to import advanced_model_loader
+sys.path.append(os.path.dirname(__file__))
+
+from advanced_model_loader import VirtualHardwareModelLoader
+
+# Global variables for the model loader
+model_loader = None
+hardware_initialized = False
+model_loaded = False
+initialization_error = None
+initialization_thread = None
+
+def create_app():
+    """Create and configure the Flask app."""
+    app = Flask(__name__, static_folder=os.path.join(os.path.dirname(__file__), 'static'))
+    app.config['SECRET_KEY'] = 'virtual-hardware-secret-key'
+    
+    # Enable CORS for all routes
+    CORS(app)
+    
+    return app
+
+def initialize_hardware_async():
+    """Initialize virtual hardware in a separate thread."""
+    global model_loader, hardware_initialized, model_loaded, initialization_error
+    
+    try:
+        print("Starting virtual hardware initialization...")
+        
+        # Create model loader with full specifications
+        model_loader = VirtualHardwareModelLoader(
+            vssd_capacity_gb=5120,  # 5TB VSSD
+            vram_capacity_gb=500    # 500GB VRAM
+        )
+        
+        # Mount all hardware components
+        model_loader.mount_hardware()
+        hardware_initialized = True
+        print("✓ Virtual hardware initialized successfully")
+        
+        # Download and load model
+        print("Downloading model to VSSD...")
+        model_name = "microsoft/DialoGPT-medium"  # Use medium model for better responses
+        
+        download_success = model_loader.download_model_to_vssd(model_name)
+        
+        if download_success:
+            print("Loading model chunks to VRAM...")
+            load_success = model_loader.load_model_chunks_to_vram(model_name, max_chunks=100)
+            
+            if load_success:
+                model_loaded = True
+                print("✓ Model loaded successfully into virtual hardware")
+            else:
+                initialization_error = "Failed to load model chunks to VRAM"
+        else:
+            initialization_error = "Failed to download model to VSSD"
+            
+    except Exception as e:
+        initialization_error = f"Hardware initialization error: {str(e)}"
+        print(f"Initialization error: {e}")
+        import traceback
+        traceback.print_exc()
+
+# Create the Flask app
+app = create_app()
+
+@app.route('/')
+def serve_root():
+    """Serve the main page."""
+    return send_from_directory(app.static_folder, 'index.html')
+
+@app.route('/health')
+def health_check():
+    """Health check endpoint."""
+    return jsonify({
+        "status": "healthy",
+        "server": "running",
+        "hardware_initialized": hardware_initialized,
+        "model_loaded": model_loaded,
+        "error": initialization_error
+    })
+
+@app.route('/api/hardware-status')
+def hardware_status():
+    """Get detailed hardware status."""
+    if not hardware_initialized or not model_loader:
+        return jsonify({
+            "error": "Hardware not initialized",
+            "initialization_error": initialization_error
+        }), 503
+        
+    try:
+        status = model_loader.get_hardware_status()
+        return jsonify(status)
+    except Exception as e:
+        return jsonify({"error": f"Status error: {str(e)}"}), 500
+
+@app.route('/api/initialize', methods=['POST'])
+def initialize_hardware():
+    """Manually trigger hardware initialization."""
+    global initialization_thread, hardware_initialized, model_loaded
+    
+    if hardware_initialized and model_loaded:
+        return jsonify({
+            "message": "Hardware already initialized and model loaded",
+            "status": "ready"
+        })
+    
+    if initialization_thread and initialization_thread.is_alive():
+        return jsonify({
+            "message": "Hardware initialization in progress",
+            "status": "initializing"
+        })
+    
+    # Start initialization in background thread
+    initialization_thread = threading.Thread(target=initialize_hardware_async, daemon=True)
+    initialization_thread.start()
+    
+    return jsonify({
+        "message": "Hardware initialization started",
+        "status": "initializing"
+    })
+
+@app.route('/api/chat', methods=['POST'])
+def chat():
+    """
+    Handle chat requests using the virtual hardware.
+    This endpoint will automatically trigger hardware initialization if not already done.
+    """
+    global model_loader, hardware_initialized, model_loaded, initialization_thread
+    
+    try:
+        # Check if hardware is ready
+        if not hardware_initialized:
+            # Auto-start initialization if not started
+            if not initialization_thread or not initialization_thread.is_alive():
+                initialization_thread = threading.Thread(target=initialize_hardware_async, daemon=True)
+                initialization_thread.start()
+            
+            return jsonify({
+                'response': 'Virtual hardware is initializing... Please wait for the 5TB VSSD, 500GB VRAM, and 50,000 GPU cores to come online.',
+                'status': 'initializing',
+                'hardware_ready': False
+            }), 202
+        
+        if not model_loaded:
+            return jsonify({
+                'response': 'Model is loading into virtual hardware... The system is transferring weights from VSSD to VRAM.',
+                'status': 'loading_model',
+                'hardware_ready': True,
+                'model_ready': False
+            }), 202
+        
+        if initialization_error:
+            return jsonify({
+                'response': f'Hardware initialization failed: {initialization_error}',
+                'status': 'error',
+                'error': initialization_error
+            }), 500
+        
+        # Get the message from request
+        data = request.get_json()
+        if not data or 'message' not in data:
+            return jsonify({'error': 'No message provided'}), 400
+        
+        user_message = data['message']
+        
+        # Generate response using virtual hardware
+        response = model_loader.inference_with_virtual_gpu(user_message)
+        
+        # Get hardware status for response metadata
+        hardware_status = model_loader.get_hardware_status()
+        
+        return jsonify({
+            'response': response,
+            'status': 'success',
+            'hardware_status': {
+                'vssd_files': hardware_status['vssd']['files_stored'],
+                'vram_utilization': hardware_status['vram']['utilization_percent'],
+                'gpu_cores_active': f"{hardware_status['vgpu']['busy_sms']}/{hardware_status['vgpu']['total_sms']} SMs",
+                'cpu_threads': hardware_status['vcpu']['active_threads'],
+                'model_chunks_loaded': hardware_status['model']['chunks_in_vram']
+            }
+        })
+        
+    except Exception as e:
+        return jsonify({
+            'error': f'Chat error: {str(e)}',
+            'status': 'error'
+        }), 500
+
+@app.route('/api/load-llama', methods=['POST'])
+def load_llama_model():
+    """Attempt to load Llama 7B model."""
+    global model_loader
+    
+    if not hardware_initialized or not model_loader:
+        return jsonify({
+            'error': 'Hardware not initialized',
+            'message': 'Please initialize hardware first'
+        }), 503
+    
+    try:
+        # This would attempt to load Llama 7B
+        # For now, we'll simulate the process
+        data = request.get_json()
+        model_name = data.get('model_name', 'meta-llama/Llama-2-7b-chat-hf')
+        
+        def load_llama_async():
+            try:
+                print(f"Attempting to load {model_name}...")
+                # This would be the actual Llama loading code
+                # For demonstration, we'll use the existing model loading
+                success = model_loader.download_model_to_vssd(model_name)
+                if success:
+                    model_loader.load_model_chunks_to_vram(model_name, max_chunks=200)
+                    print(f"✓ {model_name} loaded successfully")
+                else:
+                    print(f"✗ Failed to load {model_name}")
+            except Exception as e:
+                print(f"Llama loading error: {e}")
+        
+        # Start loading in background
+        llama_thread = threading.Thread(target=load_llama_async, daemon=True)
+        llama_thread.start()
+        
+        return jsonify({
+            'message': f'Started loading {model_name} to virtual hardware',
+            'model_name': model_name,
+            'status': 'loading',
+            'note': 'This will utilize the full 5TB VSSD and 500GB VRAM capacity'
+        })
+        
+    except Exception as e:
+        return jsonify({
+            'error': f'Llama loading error: {str(e)}',
+            'status': 'error'
+        }), 500
+
+@app.route('/api/shutdown', methods=['POST'])
+def shutdown_hardware():
+    """Shutdown virtual hardware."""
+    global model_loader, hardware_initialized, model_loaded
+    
+    try:
+        if model_loader:
+            model_loader.shutdown_hardware()
+        
+        hardware_initialized = False
+        model_loaded = False
+        model_loader = None
+        
+        return jsonify({
+            'message': 'Virtual hardware shutdown complete',
+            'status': 'shutdown'
+        })
+        
+    except Exception as e:
+        return jsonify({
+            'error': f'Shutdown error: {str(e)}',
+            'status': 'error'
+        }), 500
+
+if __name__ == '__main__':
+    print("Starting Virtual Hardware AI Backend...")
+    print("Specifications:")
+    print("  - VSSD: 5TB capacity")
+    print("  - VRAM: 500GB capacity")
+    print("  - VGPU: 50,000 cores across 800 SMs")
+    print("  - VCPU: 50 cores with 100 threads")
+    print("\nServer will start immediately. Hardware initialization will begin in background.")
+    
+    # Start the Flask app
+    app.run(host='0.0.0.0', port=7860, debug=False)
+
+

ai_backend/requirements.txt

@@ -0,0 +1,8 @@
+flask
+flask-cors
+transformers
+torch
+numpy
+requests
+
+

ai_backend/static/index.html

@@ -0,0 +1,182 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Virtual Hardware AI System</title>
+    <style>
+        * { margin: 0; padding: 0; box-sizing: border-box; }
+        body { 
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            background: linear-gradient(135deg, #1e3c72 0%, #2a5298 100%);
+            min-height: 100vh; display: flex; justify-content: center; align-items: center;
+        }
+        .container { 
+            background: white; border-radius: 20px; box-shadow: 0 20px 40px rgba(0,0,0,0.1);
+            width: 90%; max-width: 1000px; height: 80vh; display: flex; flex-direction: column;
+        }
+        .header { 
+            background: linear-gradient(135deg, #1e3c72 0%, #2a5298 100%); color: white;
+            padding: 20px; text-align: center; border-radius: 20px 20px 0 0;
+        }
+        .specs { font-size: 14px; opacity: 0.9; margin-top: 10px; }
+        .status { padding: 15px; background: #f8f9fa; border-bottom: 1px solid #e9ecef; }
+        .chat-area { flex: 1; padding: 20px; overflow-y: auto; background: #f8f9fa; }
+        .message { margin-bottom: 15px; padding: 12px 16px; border-radius: 18px; max-width: 80%; }
+        .user-message { background: #007bff; color: white; margin-left: auto; text-align: right; }
+        .bot-message { background: white; color: #333; border: 1px solid #e9ecef; }
+        .input-area { padding: 20px; background: white; border-top: 1px solid #e9ecef; display: flex; gap: 10px; }
+        .input-area input { flex: 1; padding: 12px 16px; border: 1px solid #ddd; border-radius: 25px; outline: none; }
+        .input-area button { padding: 12px 24px; background: #007bff; color: white; border: none; border-radius: 25px; cursor: pointer; }
+        .input-area button:disabled { background: #6c757d; cursor: not-allowed; }
+        .hardware-status { font-size: 12px; color: #6c757d; margin-top: 5px; }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <div class="header">
+            <h1>Virtual Hardware AI System</h1>
+            <div class="specs">5TB VSSD • 500GB VRAM • 50,000 GPU Cores • 50 CPU Cores</div>
+        </div>
+        
+        <div class="status" id="status">
+            <strong>Status:</strong> <span id="statusText">Connecting...</span>
+            <div class="hardware-status" id="hardwareStatus"></div>
+        </div>
+        
+        <div class="chat-area" id="chatArea">
+            <div class="message bot-message">
+                Welcome to the Virtual Hardware AI System! I'm powered by a complete virtual hardware stack including 5TB VSSD storage, 500GB VRAM, and 50,000 GPU cores. The system is initializing...
+            </div>
+        </div>
+        
+        <div class="input-area">
+            <input type="text" id="messageInput" placeholder="Type your message..." disabled>
+            <button id="sendButton" disabled>Send</button>
+            <button id="initButton" onclick="initializeHardware()">Initialize</button>
+        </div>
+    </div>
+
+    <script>
+        let hardwareReady = false;
+        let modelReady = false;
+        
+        async function checkStatus() {
+            try {
+                const response = await fetch('/health');
+                const data = await response.json();
+                
+                hardwareReady = data.hardware_initialized;
+                modelReady = data.model_loaded;
+                
+                const statusText = document.getElementById('statusText');
+                const hardwareStatus = document.getElementById('hardwareStatus');
+                
+                if (data.error) {
+                    statusText.textContent = `Error: ${data.error}`;
+                    statusText.style.color = 'red';
+                } else if (modelReady) {
+                    statusText.textContent = 'Ready - Virtual hardware online, model loaded';
+                    statusText.style.color = 'green';
+                    document.getElementById('messageInput').disabled = false;
+                    document.getElementById('sendButton').disabled = false;
+                } else if (hardwareReady) {
+                    statusText.textContent = 'Loading model into virtual hardware...';
+                    statusText.style.color = 'orange';
+                } else {
+                    statusText.textContent = 'Initializing virtual hardware...';
+                    statusText.style.color = 'blue';
+                }
+                
+                // Get detailed hardware status
+                if (hardwareReady) {
+                    const hwResponse = await fetch('/api/hardware-status');
+                    if (hwResponse.ok) {
+                        const hwData = await hwResponse.json();
+                        hardwareStatus.innerHTML = `
+                            VSSD: ${hwData.vssd?.files_stored || 0} files | 
+                            VRAM: ${hwData.vram?.utilization_percent || 0}% | 
+                            GPU: ${hwData.vgpu?.busy_sms || 0}/${hwData.vgpu?.total_sms || 800} SMs | 
+                            CPU: ${hwData.vcpu?.active_threads || 0} threads
+                        `;
+                    }
+                }
+                
+            } catch (error) {
+                document.getElementById('statusText').textContent = 'Connection error';
+                console.error('Status check error:', error);
+            }
+        }
+        
+        async function initializeHardware() {
+            try {
+                const response = await fetch('/api/initialize', { method: 'POST' });
+                const data = await response.json();
+                document.getElementById('statusText').textContent = data.message;
+            } catch (error) {
+                console.error('Initialize error:', error);
+            }
+        }
+        
+        async function sendMessage() {
+            const input = document.getElementById('messageInput');
+            const message = input.value.trim();
+            if (!message) return;
+            
+            addMessage(message, 'user');
+            input.value = '';
+            
+            const loadingMsg = addMessage('Processing on virtual hardware...', 'bot');
+            
+            try {
+                const response = await fetch('/api/chat', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify({ message: message })
+                });
+                
+                const data = await response.json();
+                loadingMsg.remove();
+                
+                const botMsg = addMessage(data.response, 'bot');
+                if (data.hardware_status) {
+                    const statusDiv = document.createElement('div');
+                    statusDiv.className = 'hardware-status';
+                    statusDiv.innerHTML = `
+                        VSSD: ${data.hardware_status.vssd_files} files | 
+                        VRAM: ${data.hardware_status.vram_utilization}% | 
+                        GPU: ${data.hardware_status.gpu_cores_active} | 
+                        Chunks: ${data.hardware_status.model_chunks_loaded}
+                    `;
+                    botMsg.appendChild(statusDiv);
+                }
+                
+            } catch (error) {
+                loadingMsg.remove();
+                addMessage('Error communicating with virtual hardware', 'bot');
+                console.error('Chat error:', error);
+            }
+        }
+        
+        function addMessage(text, sender) {
+            const chatArea = document.getElementById('chatArea');
+            const messageDiv = document.createElement('div');
+            messageDiv.className = `message ${sender}-message`;
+            messageDiv.textContent = text;
+            chatArea.appendChild(messageDiv);
+            chatArea.scrollTop = chatArea.scrollHeight;
+            return messageDiv;
+        }
+        
+        document.getElementById('sendButton').addEventListener('click', sendMessage);
+        document.getElementById('messageInput').addEventListener('keypress', (e) => {
+            if (e.key === 'Enter') sendMessage();
+        });
+        
+        // Check status every 3 seconds
+        setInterval(checkStatus, 3000);
+        checkStatus();
+    </script>
+</body>
+</html>
+

virtual_hardware/ai.py

@@ -0,0 +1,446 @@
+"""
+AI Accelerator Module
+
+This module implements AI-specific operations, treating the vGPU as a tensor engine
+and leveraging the simulated parallelism of 50,000 cores and 800 SMs.
+"""
+
+import numpy as np
+import time
+from typing import Dict, Any, Optional, Tuple, Union, List
+from enum import Enum
+
+
+class VectorOperation(Enum):
+    """Enumeration of supported vector operations."""
+    ADD = "add"
+    SUBTRACT = "subtract"
+    MULTIPLY = "multiply"
+    DIVIDE = "divide"
+    DOT_PRODUCT = "dot_product"
+    CROSS_PRODUCT = "cross_product"
+    NORMALIZE = "normalize"
+    MAGNITUDE = "magnitude"
+
+
+class AIAccelerator:
+    """
+    AI Accelerator that simulates GPU-based AI computations.
+    
+    This class leverages NumPy's optimized operations to simulate the parallel
+    processing capabilities of the vGPU for AI workloads.
+    """
+    
+    def __init__(self, vram=None, num_sms: int = 800, cores_per_sm: int = 62):
+        self.vram = vram
+        self.num_sms = num_sms
+        self.cores_per_sm = cores_per_sm
+        self.total_cores = num_sms * cores_per_sm
+        
+        # AI operation statistics
+        self.operations_performed = 0
+        self.total_compute_time = 0.0
+        self.flops_performed = 0  # Floating point operations
+        
+        # Matrix registry for storing matrices in VRAM
+        self.matrix_registry: Dict[str, str] = {}  # matrix_id -> vram_address
+        self.matrix_counter = 0
+        
+    def set_vram(self, vram):
+        """Set the VRAM reference."""
+        self.vram = vram
+        
+    def allocate_matrix(self, shape: Tuple[int, ...], dtype=np.float32, 
+                       name: Optional[str] = None) -> str:
+        """Allocate a matrix in VRAM and return its ID."""
+        if not self.vram:
+            raise RuntimeError("VRAM not available")
+            
+        if name is None:
+            name = f"matrix_{self.matrix_counter}"
+            self.matrix_counter += 1
+            
+        # Create matrix data
+        matrix_data = np.zeros(shape, dtype=dtype)
+        
+        # Store in VRAM as a texture (reusing texture storage mechanism)
+        matrix_id = self.vram.load_texture(matrix_data, name)
+        self.matrix_registry[name] = matrix_id
+        
+        return name
+        
+    def load_matrix(self, matrix_data: np.ndarray, name: Optional[str] = None) -> str:
+        """Load matrix data into VRAM and return its ID."""
+        if not self.vram:
+            raise RuntimeError("VRAM not available")
+            
+        if name is None:
+            name = f"matrix_{self.matrix_counter}"
+            self.matrix_counter += 1
+            
+        # Store in VRAM
+        matrix_id = self.vram.load_texture(matrix_data, name)
+        self.matrix_registry[name] = matrix_id
+        
+        return name
+        
+    def get_matrix(self, matrix_id: str) -> Optional[np.ndarray]:
+        """Retrieve matrix data from VRAM."""
+        if not self.vram or matrix_id not in self.matrix_registry:
+            return None
+            
+        vram_id = self.matrix_registry[matrix_id]
+        return self.vram.get_texture(vram_id)
+        
+    def matrix_multiply(self, matrix_a_id: str, matrix_b_id: str, 
+                       result_id: Optional[str] = None) -> Optional[str]:
+        """Perform matrix multiplication using simulated GPU parallelism."""
+        start_time = time.time()
+        
+        # Retrieve matrices from VRAM
+        matrix_a = self.get_matrix(matrix_a_id)
+        matrix_b = self.get_matrix(matrix_b_id)
+        
+        if matrix_a is None or matrix_b is None:
+            print(f"Error: Could not retrieve matrices {matrix_a_id} or {matrix_b_id}")
+            return None
+            
+        try:
+            # Check if matrices can be multiplied
+            if matrix_a.shape[-1] != matrix_b.shape[0]:
+                print(f"Error: Matrix dimensions incompatible for multiplication: "
+                      f"{matrix_a.shape} x {matrix_b.shape}")
+                return None
+                
+            # Simulate parallel processing by breaking down the operation
+            # In a real GPU, this would be distributed across SMs and cores
+            result = self._simulate_parallel_matmul(matrix_a, matrix_b)
+            
+            # Store result in VRAM
+            if result_id is None:
+                result_id = f"result_{self.matrix_counter}"
+                self.matrix_counter += 1
+                
+            result_matrix_id = self.load_matrix(result, result_id)
+            
+            # Update statistics
+            compute_time = time.time() - start_time
+            self.total_compute_time += compute_time
+            self.operations_performed += 1
+            
+            # Calculate FLOPs (2 * M * N * K for matrix multiplication)
+            m, k = matrix_a.shape
+            k2, n = matrix_b.shape
+            flops = 2 * m * n * k
+            self.flops_performed += flops
+            
+            print(f"Matrix multiplication completed: {matrix_a.shape} x {matrix_b.shape} "
+                  f"= {result.shape} in {compute_time:.4f}s")
+            print(f"Simulated {flops:,} FLOPs across {self.total_cores} cores")
+            
+            return result_matrix_id
+            
+        except Exception as e:
+            print(f"Error in matrix multiplication: {e}")
+            return None
+            
+    def _simulate_parallel_matmul(self, matrix_a: np.ndarray, matrix_b: np.ndarray) -> np.ndarray:
+        """Simulate parallel matrix multiplication across SMs."""
+        # Use NumPy's optimized matrix multiplication
+        # In a real implementation, this would be broken down into blocks
+        # and distributed across the simulated SMs
+        
+        # For demonstration, we can show how the work would be distributed
+        m, k = matrix_a.shape
+        k2, n = matrix_b.shape
+        
+        # Calculate work distribution
+        total_output_elements = m * n
+        elements_per_sm = max(1, total_output_elements // self.num_sms)
+        
+        print(f"Distributing {total_output_elements:,} output elements across "
+              f"{self.num_sms} SMs ({elements_per_sm} elements per SM)")
+        
+        # Perform the actual computation using NumPy
+        result = np.dot(matrix_a, matrix_b)
+        
+        return result
+        
+    def vector_operation(self, operation: VectorOperation, vector_a_id: str,
+                        vector_b_id: Optional[str] = None, 
+                        result_id: Optional[str] = None) -> Optional[str]:
+        """Perform vector operations using simulated GPU parallelism."""
+        start_time = time.time()
+        
+        # Retrieve vectors from VRAM
+        vector_a = self.get_matrix(vector_a_id)
+        if vector_a is None:
+            print(f"Error: Could not retrieve vector {vector_a_id}")
+            return None
+            
+        vector_b = None
+        if vector_b_id:
+            vector_b = self.get_matrix(vector_b_id)
+            if vector_b is None:
+                print(f"Error: Could not retrieve vector {vector_b_id}")
+                return None
+                
+        try:
+            result = None
+            flops = 0
+            
+            if operation == VectorOperation.ADD:
+                if vector_b is None:
+                    raise ValueError("Vector B required for addition")
+                result = vector_a + vector_b
+                flops = vector_a.size
+                
+            elif operation == VectorOperation.SUBTRACT:
+                if vector_b is None:
+                    raise ValueError("Vector B required for subtraction")
+                result = vector_a - vector_b
+                flops = vector_a.size
+                
+            elif operation == VectorOperation.MULTIPLY:
+                if vector_b is None:
+                    raise ValueError("Vector B required for multiplication")
+                result = vector_a * vector_b
+                flops = vector_a.size
+                
+            elif operation == VectorOperation.DIVIDE:
+                if vector_b is None:
+                    raise ValueError("Vector B required for division")
+                result = vector_a / vector_b
+                flops = vector_a.size
+                
+            elif operation == VectorOperation.DOT_PRODUCT:
+                if vector_b is None:
+                    raise ValueError("Vector B required for dot product")
+                result = np.dot(vector_a.flatten(), vector_b.flatten())
+                flops = 2 * vector_a.size
+                
+            elif operation == VectorOperation.CROSS_PRODUCT:
+                if vector_b is None:
+                    raise ValueError("Vector B required for cross product")
+                result = np.cross(vector_a, vector_b)
+                flops = 6  # Approximate for 3D cross product
+                
+            elif operation == VectorOperation.NORMALIZE:
+                magnitude = np.linalg.norm(vector_a)
+                result = vector_a / magnitude if magnitude > 0 else vector_a
+                flops = vector_a.size * 2  # Division + magnitude calculation
+                
+            elif operation == VectorOperation.MAGNITUDE:
+                result = np.array([np.linalg.norm(vector_a)])
+                flops = vector_a.size * 2  # Squares and sum
+                
+            else:
+                raise ValueError(f"Unsupported vector operation: {operation}")
+                
+            # Store result in VRAM
+            if result_id is None:
+                result_id = f"vector_result_{self.matrix_counter}"
+                self.matrix_counter += 1
+                
+            result_vector_id = self.load_matrix(result, result_id)
+            
+            # Update statistics
+            compute_time = time.time() - start_time
+            self.total_compute_time += compute_time
+            self.operations_performed += 1
+            self.flops_performed += flops
+            
+            print(f"Vector operation {operation.value} completed in {compute_time:.4f}s")
+            
+            return result_vector_id
+            
+        except Exception as e:
+            print(f"Error in vector operation {operation.value}: {e}")
+            return None
+            
+    def convolution_2d(self, input_id: str, kernel_id: str, 
+                      stride: int = 1, padding: int = 0,
+                      result_id: Optional[str] = None) -> Optional[str]:
+        """Perform 2D convolution operation."""
+        start_time = time.time()
+        
+        # Retrieve input and kernel from VRAM
+        input_data = self.get_matrix(input_id)
+        kernel = self.get_matrix(kernel_id)
+        
+        if input_data is None or kernel is None:
+            print(f"Error: Could not retrieve input or kernel")
+            return None
+            
+        try:
+            # Simple 2D convolution implementation
+            # In a real GPU implementation, this would be highly optimized
+            # and distributed across many cores
+            
+            if len(input_data.shape) == 2:
+                input_h, input_w = input_data.shape
+                channels = 1
+            else:
+                input_h, input_w, channels = input_data.shape
+                
+            kernel_h, kernel_w = kernel.shape[:2]
+            
+            # Calculate output dimensions
+            output_h = (input_h + 2 * padding - kernel_h) // stride + 1
+            output_w = (input_w + 2 * padding - kernel_w) // stride + 1
+            
+            # Initialize output
+            if channels == 1:
+                output = np.zeros((output_h, output_w))
+            else:
+                output = np.zeros((output_h, output_w, channels))
+                
+            # Pad input if necessary
+            if padding > 0:
+                if channels == 1:
+                    padded_input = np.pad(input_data, padding, mode='constant')
+                else:
+                    padded_input = np.pad(input_data, 
+                                        ((padding, padding), (padding, padding), (0, 0)), 
+                                        mode='constant')
+            else:
+                padded_input = input_data
+                
+            # Perform convolution
+            flops = 0
+            for y in range(0, output_h):
+                for x in range(0, output_w):
+                    y_start = y * stride
+                    x_start = x * stride
+                    
+                    if channels == 1:
+                        patch = padded_input[y_start:y_start+kernel_h, x_start:x_start+kernel_w]
+                        output[y, x] = np.sum(patch * kernel)
+                        flops += kernel_h * kernel_w * 2  # Multiply and add
+                    else:
+                        for c in range(channels):
+                            patch = padded_input[y_start:y_start+kernel_h, 
+                                               x_start:x_start+kernel_w, c]
+                            output[y, x, c] = np.sum(patch * kernel)
+                            flops += kernel_h * kernel_w * 2
+                            
+            # Store result in VRAM
+            if result_id is None:
+                result_id = f"conv_result_{self.matrix_counter}"
+                self.matrix_counter += 1
+                
+            result_conv_id = self.load_matrix(output, result_id)
+            
+            # Update statistics
+            compute_time = time.time() - start_time
+            self.total_compute_time += compute_time
+            self.operations_performed += 1
+            self.flops_performed += flops
+            
+            print(f"2D Convolution completed: {input_data.shape} * {kernel.shape} "
+                  f"= {output.shape} in {compute_time:.4f}s")
+            print(f"Simulated {flops:,} FLOPs")
+            
+            return result_conv_id
+            
+        except Exception as e:
+            print(f"Error in 2D convolution: {e}")
+            return None
+            
+    def get_stats(self) -> Dict[str, Any]:
+        """Get AI accelerator statistics."""
+        avg_compute_time = self.total_compute_time / max(1, self.operations_performed)
+        flops_per_second = self.flops_performed / max(0.001, self.total_compute_time)
+        
+        return {
+            "operations_performed": self.operations_performed,
+            "total_compute_time": self.total_compute_time,
+            "avg_compute_time": avg_compute_time,
+            "flops_performed": self.flops_performed,
+            "flops_per_second": flops_per_second,
+            "matrices_in_memory": len(self.matrix_registry),
+            "simulated_cores": self.total_cores,
+            "simulated_sms": self.num_sms
+        }
+        
+    def reset_stats(self) -> None:
+        """Reset AI accelerator statistics."""
+        self.operations_performed = 0
+        self.total_compute_time = 0.0
+        self.flops_performed = 0
+
+
+if __name__ == "__main__":
+    # Test the AI accelerator
+    from vram import VRAM
+    
+    # Create VRAM and AI accelerator
+    vram = VRAM(memory_size_gb=1)
+    ai = AIAccelerator(vram)
+    
+    print("Testing AI Accelerator...")
+    
+    # Test matrix operations
+    # Create test matrices
+    matrix_a = np.random.rand(100, 50).astype(np.float32)
+    matrix_b = np.random.rand(50, 75).astype(np.float32)
+    
+    # Load matrices into VRAM
+    a_id = ai.load_matrix(matrix_a, "test_matrix_a")
+    b_id = ai.load_matrix(matrix_b, "test_matrix_b")
+    
+    # Perform matrix multiplication
+    result_id = ai.matrix_multiply(a_id, b_id, "multiplication_result")
+    
+    if result_id:
+        result = ai.get_matrix(result_id)
+        print(f"Matrix multiplication result shape: {result.shape}")
+        
+        # Verify result
+        expected = np.dot(matrix_a, matrix_b)
+        if np.allclose(result, expected):
+            print("Matrix multiplication result is correct!")
+        else:
+            print("Matrix multiplication result is incorrect!")
+            
+    # Test vector operations
+    vector_a = np.random.rand(1000).astype(np.float32)
+    vector_b = np.random.rand(1000).astype(np.float32)
+    
+    va_id = ai.load_matrix(vector_a, "vector_a")
+    vb_id = ai.load_matrix(vector_b, "vector_b")
+    
+    # Test vector addition
+    add_result_id = ai.vector_operation(VectorOperation.ADD, va_id, vb_id)
+    if add_result_id:
+        add_result = ai.get_matrix(add_result_id)
+        expected_add = vector_a + vector_b
+        if np.allclose(add_result, expected_add):
+            print("Vector addition result is correct!")
+            
+    # Test dot product
+    dot_result_id = ai.vector_operation(VectorOperation.DOT_PRODUCT, va_id, vb_id)
+    if dot_result_id:
+        dot_result = ai.get_matrix(dot_result_id)
+        expected_dot = np.dot(vector_a, vector_b)
+        if np.allclose(dot_result[0], expected_dot):
+            print("Dot product result is correct!")
+            
+    # Test 2D convolution
+    input_image = np.random.rand(32, 32).astype(np.float32)
+    kernel = np.array([[1, 0, -1], [2, 0, -2], [1, 0, -1]], dtype=np.float32)  # Sobel edge detector
+    
+    img_id = ai.load_matrix(input_image, "test_image")
+    kernel_id = ai.load_matrix(kernel, "sobel_kernel")
+    
+    conv_result_id = ai.convolution_2d(img_id, kernel_id)
+    if conv_result_id:
+        conv_result = ai.get_matrix(conv_result_id)
+        print(f"Convolution result shape: {conv_result.shape}")
+        
+    # Print final statistics
+    stats = ai.get_stats()
+    print(f"AI Accelerator stats: {stats}")
+    
+    print("AI Accelerator test completed!")
+

virtual_hardware/driver.py

@@ -0,0 +1,312 @@
+"""
+GPU Driver Module
+
+This module acts as the interface between a virtual CPU (or external command source)
+and the vGPU, handling command queuing and interpretation.
+"""
+
+import asyncio
+from collections import deque
+from enum import Enum
+from typing import Dict, Any, Optional, List
+from dataclasses import dataclass
+
+
+class CommandType(Enum):
+    """Enumeration of supported GPU commands."""
+    CLEAR = "clear"
+    DRAW_RECT = "draw_rect"
+    DRAW_PIXEL = "draw_pixel"
+    DRAW_IMAGE = "draw_image"
+    SET_SHADER = "set_shader"
+    MATRIX_MULTIPLY = "matrix_multiply"
+    VECTOR_OP = "vector_op"
+    CREATE_FRAMEBUFFER = "create_framebuffer"
+    SET_FRAMEBUFFER = "set_framebuffer"
+    LOAD_TEXTURE = "load_texture"
+
+
+@dataclass
+class Command:
+    """Represents a single command to be executed by the vGPU."""
+    command_id: str
+    command_type: CommandType
+    parameters: Dict[str, Any]
+    priority: int = 0
+    timestamp: float = 0.0
+
+
+class GPUDriver:
+    """
+    GPU Driver that manages command queues and interfaces with the vGPU.
+    
+    This class receives commands from external sources (virtual CPU, applications)
+    and translates them into tasks that can be processed by the vGPU.
+    """
+    
+    def __init__(self, vgpu=None):
+        self.vgpu = vgpu
+        
+        # Command queue management
+        self.command_queue = deque()
+        self.command_counter = 0
+        
+        # Current state
+        self.current_framebuffer = None
+        self.current_shader = None
+        
+        # Command processing statistics
+        self.commands_processed = 0
+        self.commands_failed = 0
+        
+    def set_vgpu(self, vgpu):
+        """Set the vGPU reference."""
+        self.vgpu = vgpu
+        
+    def submit_command(self, command_type: CommandType, parameters: Dict[str, Any], 
+                      priority: int = 0) -> str:
+        """Submit a command to the GPU driver."""
+        command_id = f"cmd_{self.command_counter}"
+        self.command_counter += 1
+        
+        command = Command(
+            command_id=command_id,
+            command_type=command_type,
+            parameters=parameters,
+            priority=priority,
+            timestamp=asyncio.get_event_loop().time()
+        )
+        
+        # Insert command based on priority (higher priority first)
+        if priority > 0:
+            # Find insertion point for priority queue
+            inserted = False
+            for i, existing_cmd in enumerate(self.command_queue):
+                if existing_cmd.priority < priority:
+                    self.command_queue.insert(i, command)
+                    inserted = True
+                    break
+            if not inserted:
+                self.command_queue.append(command)
+        else:
+            self.command_queue.append(command)
+            
+        return command_id
+        
+    async def process_commands(self) -> None:
+        """Process all pending commands in the queue."""
+        while self.command_queue:
+            command = self.command_queue.popleft()
+            await self._execute_command(command)
+            
+    async def _execute_command(self, command: Command) -> None:
+        """Execute a single command."""
+        try:
+            if command.command_type == CommandType.CLEAR:
+                await self._handle_clear(command)
+            elif command.command_type == CommandType.DRAW_RECT:
+                await self._handle_draw_rect(command)
+            elif command.command_type == CommandType.DRAW_PIXEL:
+                await self._handle_draw_pixel(command)
+            elif command.command_type == CommandType.DRAW_IMAGE:
+                await self._handle_draw_image(command)
+            elif command.command_type == CommandType.SET_SHADER:
+                await self._handle_set_shader(command)
+            elif command.command_type == CommandType.MATRIX_MULTIPLY:
+                await self._handle_matrix_multiply(command)
+            elif command.command_type == CommandType.VECTOR_OP:
+                await self._handle_vector_op(command)
+            elif command.command_type == CommandType.CREATE_FRAMEBUFFER:
+                await self._handle_create_framebuffer(command)
+            elif command.command_type == CommandType.SET_FRAMEBUFFER:
+                await self._handle_set_framebuffer(command)
+            elif command.command_type == CommandType.LOAD_TEXTURE:
+                await self._handle_load_texture(command)
+            else:
+                print(f"Unknown command type: {command.command_type}")
+                self.commands_failed += 1
+                return
+                
+            self.commands_processed += 1
+            
+        except Exception as e:
+            print(f"Error executing command {command.command_id}: {e}")
+            self.commands_failed += 1
+            
+    async def _handle_clear(self, command: Command) -> None:
+        """Handle CLEAR command."""
+        if self.vgpu and self.current_framebuffer:
+            from vgpu import TaskType
+            task_id = self.vgpu.submit_task(
+                TaskType.RENDER_CLEAR,
+                {
+                    "framebuffer_id": self.current_framebuffer,
+                    **command.parameters
+                }
+            )
+            
+    async def _handle_draw_rect(self, command: Command) -> None:
+        """Handle DRAW_RECT command."""
+        if self.vgpu and self.current_framebuffer:
+            from vgpu import TaskType
+            task_id = self.vgpu.submit_task(
+                TaskType.RENDER_RECT,
+                {
+                    "framebuffer_id": self.current_framebuffer,
+                    **command.parameters
+                }
+            )
+            
+    async def _handle_draw_pixel(self, command: Command) -> None:
+        """Handle DRAW_PIXEL command."""
+        if self.vgpu and self.current_framebuffer:
+            from vgpu import TaskType
+            # Convert single pixel to a 1x1 rectangle
+            params = command.parameters.copy()
+            params.update({
+                "framebuffer_id": self.current_framebuffer,
+                "width": 1,
+                "height": 1
+            })
+            task_id = self.vgpu.submit_task(TaskType.RENDER_RECT, params)
+            
+    async def _handle_draw_image(self, command: Command) -> None:
+        """Handle DRAW_IMAGE command."""
+        if self.vgpu and self.current_framebuffer:
+            from vgpu import TaskType
+            task_id = self.vgpu.submit_task(
+                TaskType.RENDER_IMAGE,
+                {
+                    "framebuffer_id": self.current_framebuffer,
+                    **command.parameters
+                }
+            )
+            
+    async def _handle_set_shader(self, command: Command) -> None:
+        """Handle SET_SHADER command."""
+        shader_id = command.parameters.get("shader_id")
+        if shader_id:
+            self.current_shader = shader_id
+            
+    async def _handle_matrix_multiply(self, command: Command) -> None:
+        """Handle MATRIX_MULTIPLY command."""
+        if self.vgpu:
+            from vgpu import TaskType
+            task_id = self.vgpu.submit_task(
+                TaskType.AI_MATRIX_MULTIPLY,
+                command.parameters
+            )
+            
+    async def _handle_vector_op(self, command: Command) -> None:
+        """Handle VECTOR_OP command."""
+        if self.vgpu:
+            from vgpu import TaskType
+            task_id = self.vgpu.submit_task(
+                TaskType.AI_VECTOR_OP,
+                command.parameters
+            )
+            
+    async def _handle_create_framebuffer(self, command: Command) -> None:
+        """Handle CREATE_FRAMEBUFFER command."""
+        if self.vgpu and self.vgpu.vram:
+            width = command.parameters.get("width", 800)
+            height = command.parameters.get("height", 600)
+            channels = command.parameters.get("channels", 3)
+            name = command.parameters.get("name")
+            
+            framebuffer_id = self.vgpu.vram.create_framebuffer(width, height, channels, name)
+            
+            # Set as current framebuffer if none is set
+            if self.current_framebuffer is None:
+                self.current_framebuffer = framebuffer_id
+                
+    async def _handle_set_framebuffer(self, command: Command) -> None:
+        """Handle SET_FRAMEBUFFER command."""
+        framebuffer_id = command.parameters.get("framebuffer_id")
+        if framebuffer_id and self.vgpu and self.vgpu.vram:
+            if self.vgpu.vram.get_framebuffer(framebuffer_id):
+                self.current_framebuffer = framebuffer_id
+                
+    async def _handle_load_texture(self, command: Command) -> None:
+        """Handle LOAD_TEXTURE command."""
+        if self.vgpu and self.vgpu.vram:
+            texture_data = command.parameters.get("texture_data")
+            name = command.parameters.get("name")
+            
+            if texture_data is not None:
+                texture_id = self.vgpu.vram.load_texture(texture_data, name)
+                
+    def get_current_framebuffer(self) -> Optional[str]:
+        """Get the current active framebuffer ID."""
+        return self.current_framebuffer
+        
+    def get_current_shader(self) -> Optional[str]:
+        """Get the current active shader ID."""
+        return self.current_shader
+        
+    def get_stats(self) -> Dict[str, Any]:
+        """Get driver statistics."""
+        return {
+            "commands_in_queue": len(self.command_queue),
+            "commands_processed": self.commands_processed,
+            "commands_failed": self.commands_failed,
+            "current_framebuffer": self.current_framebuffer,
+            "current_shader": self.current_shader
+        }
+        
+    # Convenience methods for common operations
+    def clear_screen(self, color: tuple = (0, 0, 0)) -> str:
+        """Clear the current framebuffer with the specified color."""
+        return self.submit_command(CommandType.CLEAR, {"color": color})
+        
+    def draw_rectangle(self, x: int, y: int, width: int, height: int, 
+                      color: tuple = (255, 255, 255)) -> str:
+        """Draw a rectangle on the current framebuffer."""
+        return self.submit_command(
+            CommandType.DRAW_RECT,
+            {"x": x, "y": y, "width": width, "height": height, "color": color}
+        )
+        
+    def draw_pixel(self, x: int, y: int, color: tuple = (255, 255, 255)) -> str:
+        """Draw a single pixel on the current framebuffer."""
+        return self.submit_command(
+            CommandType.DRAW_PIXEL,
+            {"x": x, "y": y, "color": color}
+        )
+        
+    def create_framebuffer(self, width: int, height: int, channels: int = 3, 
+                          name: Optional[str] = None) -> str:
+        """Create a new framebuffer."""
+        return self.submit_command(
+            CommandType.CREATE_FRAMEBUFFER,
+            {"width": width, "height": height, "channels": channels, "name": name}
+        )
+        
+    def set_framebuffer(self, framebuffer_id: str) -> str:
+        """Set the active framebuffer."""
+        return self.submit_command(
+            CommandType.SET_FRAMEBUFFER,
+            {"framebuffer_id": framebuffer_id}
+        )
+
+
+if __name__ == "__main__":
+    # Test the driver
+    async def test_driver():
+        driver = GPUDriver()
+        
+        # Submit some test commands
+        driver.create_framebuffer(800, 600)
+        driver.clear_screen((255, 0, 0))
+        driver.draw_rectangle(100, 100, 200, 150, (0, 255, 0))
+        driver.draw_pixel(400, 300, (0, 0, 255))
+        
+        print(f"Driver stats: {driver.get_stats()}")
+        
+        # Process commands (without vGPU, they won't actually execute)
+        await driver.process_commands()
+        
+        print(f"Driver stats after processing: {driver.get_stats()}")
+    
+    asyncio.run(test_driver())
+

virtual_hardware/enhanced_cpu.py

@@ -0,0 +1,407 @@
+"""
+Enhanced CPU Module with GPU Integration
+
+This module extends the original CPU implementation to include GPU communication
+capabilities and enhanced threading support for the 50 cores / 100 threads configuration.
+"""
+
+import multiprocessing
+import threading
+import time
+import queue
+from typing import Dict, Any, Optional, List
+from dataclasses import dataclass
+
+# Import original CPU components
+from virtual_hardware_display_system.src.cpu import Core, MultiCoreCPU, CPULogger
+
+# Import GPU driver interface
+from virtual_gpu_driver import VirtualGPUDriver, CPUGPUInterface, GPUCommandType
+
+
+@dataclass
+class VirtualThread:
+    """Represents a virtual thread running on a CPU core."""
+    thread_id: int
+    core_id: int
+    program_counter: int = 0
+    stack_pointer: int = 255
+    registers: Dict[str, int] = None
+    status: str = "ready"  # ready, running, waiting, terminated
+    priority: int = 1
+    
+    def __post_init__(self):
+        if self.registers is None:
+            self.registers = {"AX": 0, "BX": 0, "CX": 0, "DX": 0}
+
+
+class ThreadScheduler:
+    """Simple round-robin thread scheduler for virtual threads."""
+    
+    def __init__(self, max_threads_per_core: int = 2):
+        self.max_threads_per_core = max_threads_per_core
+        self.threads: Dict[int, List[VirtualThread]] = {}  # core_id -> list of threads
+        self.current_thread_index: Dict[int, int] = {}  # core_id -> current thread index
+        self.thread_counter = 0
+        
+    def create_thread(self, core_id: int, program_counter: int = 0) -> int:
+        """Create a new virtual thread on the specified core."""
+        if core_id not in self.threads:
+            self.threads[core_id] = []
+            self.current_thread_index[core_id] = 0
+            
+        if len(self.threads[core_id]) >= self.max_threads_per_core:
+            return -1  # Core is at thread capacity
+            
+        thread_id = self.thread_counter
+        self.thread_counter += 1
+        
+        thread = VirtualThread(
+            thread_id=thread_id,
+            core_id=core_id,
+            program_counter=program_counter
+        )
+        
+        self.threads[core_id].append(thread)
+        return thread_id
+        
+    def get_current_thread(self, core_id: int) -> Optional[VirtualThread]:
+        """Get the currently scheduled thread for a core."""
+        if core_id not in self.threads or not self.threads[core_id]:
+            return None
+            
+        threads = self.threads[core_id]
+        current_index = self.current_thread_index[core_id]
+        
+        if current_index < len(threads):
+            return threads[current_index]
+        return None
+        
+    def schedule_next_thread(self, core_id: int) -> Optional[VirtualThread]:
+        """Schedule the next thread for execution on a core."""
+        if core_id not in self.threads or not self.threads[core_id]:
+            return None
+            
+        threads = self.threads[core_id]
+        if not threads:
+            return None
+            
+        # Round-robin scheduling
+        self.current_thread_index[core_id] = (self.current_thread_index[core_id] + 1) % len(threads)
+        return self.get_current_thread(core_id)
+        
+    def terminate_thread(self, thread_id: int) -> bool:
+        """Terminate a virtual thread."""
+        for core_id, threads in self.threads.items():
+            for i, thread in enumerate(threads):
+                if thread.thread_id == thread_id:
+                    thread.status = "terminated"
+                    threads.pop(i)
+                    # Adjust current thread index if necessary
+                    if self.current_thread_index[core_id] >= len(threads):
+                        self.current_thread_index[core_id] = 0
+                    return True
+        return False
+        
+    def get_thread_count(self, core_id: int) -> int:
+        """Get the number of active threads on a core."""
+        return len(self.threads.get(core_id, []))
+        
+    def get_total_thread_count(self) -> int:
+        """Get the total number of active threads across all cores."""
+        return sum(len(threads) for threads in self.threads.values())
+
+
+class EnhancedCore(Core):
+    """Enhanced CPU core with GPU integration and threading support."""
+    
+    def __init__(self, core_id: int, gpu_interface: Optional[CPUGPUInterface] = None):
+        super().__init__(core_id)
+        self.gpu_interface = gpu_interface
+        self.thread_scheduler = ThreadScheduler(max_threads_per_core=2)  # 2 threads per core for 100 total
+        self.gpu_command_queue = queue.Queue()
+        self.gpu_results = {}
+        
+        # Enhanced instruction set for GPU operations
+        self.gpu_instructions = {
+            'GPU_CLEAR', 'GPU_RECT', 'GPU_TRANSFER', 'GPU_ALLOC', 'GPU_AI_INFER',
+            'GPU_MATRIX_MUL', 'GPU_WAIT', 'GPU_STATUS'
+        }
+        
+    def connect_gpu_interface(self, gpu_interface: CPUGPUInterface):
+        """Connect the GPU interface to this core."""
+        self.gpu_interface = gpu_interface
+        
+    def create_virtual_thread(self, program_counter: int = 0) -> int:
+        """Create a new virtual thread on this core."""
+        return self.thread_scheduler.create_thread(self.core_id, program_counter)
+        
+    def execute_with_threading(self, instruction):
+        """Execute instruction with threading support."""
+        current_thread = self.thread_scheduler.get_current_thread(self.core_id)
+        
+        if current_thread is None:
+            # No threads, execute normally
+            return self.execute(instruction)
+            
+        # Save current core state to thread
+        current_thread.registers["AX"] = self.AX
+        current_thread.registers["BX"] = self.BX
+        current_thread.registers["CX"] = self.CX
+        current_thread.registers["DX"] = self.DX
+        current_thread.program_counter = self.PC
+        current_thread.stack_pointer = self.SP
+        
+        # Execute instruction
+        result = self.execute(instruction)
+        
+        # Restore thread state to core
+        self.AX = current_thread.registers["AX"]
+        self.BX = current_thread.registers["BX"]
+        self.CX = current_thread.registers["CX"]
+        self.DX = current_thread.registers["DX"]
+        self.PC = current_thread.program_counter
+        self.SP = current_thread.stack_pointer
+        
+        return result
+        
+    def execute(self, instruction):
+        """Enhanced execute method with GPU instruction support."""
+        op = instruction.get("op")
+        
+        # Handle GPU instructions
+        if op in self.gpu_instructions:
+            return self._execute_gpu_instruction(instruction)
+        
+        # Handle regular CPU instructions
+        return super().execute(instruction)
+        
+    def _execute_gpu_instruction(self, instruction):
+        """Execute GPU-specific instructions."""
+        if not self.gpu_interface:
+            print(f"Core {self.core_id} Error: GPU interface not connected")
+            return
+            
+        op = instruction.get("op")
+        
+        try:
+            if op == 'GPU_CLEAR':
+                color = instruction.get('color', (0, 0, 0))
+                cmd_id = self.gpu_interface.gpu_clear_screen(color, self.core_id)
+                self.gpu_results[cmd_id] = "pending"
+                self.AX = hash(cmd_id) & 0xFFFF  # Store command ID hash in AX
+                
+            elif op == 'GPU_RECT':
+                x = instruction.get('x', 0)
+                y = instruction.get('y', 0)
+                width = instruction.get('width', 100)
+                height = instruction.get('height', 100)
+                color = instruction.get('color', (255, 255, 255))
+                cmd_id = self.gpu_interface.gpu_draw_rect(x, y, width, height, color, self.core_id)
+                self.gpu_results[cmd_id] = "pending"
+                self.AX = hash(cmd_id) & 0xFFFF
+                
+            elif op == 'GPU_TRANSFER':
+                data = instruction.get('data', b'')
+                name = instruction.get('name', f'transfer_{self.core_id}')
+                cmd_id = self.gpu_interface.gpu_transfer_data(data, name, self.core_id)
+                self.gpu_results[cmd_id] = "pending"
+                self.AX = hash(cmd_id) & 0xFFFF
+                
+            elif op == 'GPU_ALLOC':
+                width = instruction.get('width', 1920)
+                height = instruction.get('height', 1080)
+                channels = instruction.get('channels', 3)
+                name = instruction.get('name')
+                cmd_id = self.gpu_interface.gpu_alloc_framebuffer(width, height, channels, name, self.core_id)
+                self.gpu_results[cmd_id] = "pending"
+                self.AX = hash(cmd_id) & 0xFFFF
+                
+            elif op == 'GPU_AI_INFER':
+                model_data = instruction.get('model_data')
+                input_data = instruction.get('input_data')
+                cmd_id = self.gpu_interface.gpu_ai_inference(model_data, input_data, self.core_id)
+                self.gpu_results[cmd_id] = "pending"
+                self.AX = hash(cmd_id) & 0xFFFF
+                
+            elif op == 'GPU_MATRIX_MUL':
+                matrix_a = instruction.get('matrix_a')
+                matrix_b = instruction.get('matrix_b')
+                cmd_id = self.gpu_interface.gpu_matrix_multiply(matrix_a, matrix_b, self.core_id)
+                self.gpu_results[cmd_id] = "pending"
+                self.AX = hash(cmd_id) & 0xFFFF
+                
+            elif op == 'GPU_WAIT':
+                cmd_id_hash = instruction.get('cmd_id_hash', self.AX)
+                timeout = instruction.get('timeout', 10.0)
+                
+                # Find command ID by hash (simplified)
+                cmd_id = None
+                for cid in self.gpu_results:
+                    if (hash(cid) & 0xFFFF) == cmd_id_hash:
+                        cmd_id = cid
+                        break
+                        
+                if cmd_id:
+                    success = self.gpu_interface.wait_for_gpu_task(cmd_id, timeout)
+                    self.ZF = 1 if success else 0
+                    if success:
+                        self.gpu_results[cmd_id] = "completed"
+                else:
+                    self.ZF = 0
+                    
+            elif op == 'GPU_STATUS':
+                cmd_id_hash = instruction.get('cmd_id_hash', self.AX)
+                
+                # Find command ID by hash and check status
+                for cid in self.gpu_results:
+                    if (hash(cid) & 0xFFFF) == cmd_id_hash:
+                        status = self.gpu_interface.gpu_driver.get_command_status(cid)
+                        if status == "completed":
+                            self.ZF = 1
+                        elif status == "error":
+                            self.ZF = 0
+                            self.CF = 1
+                        else:
+                            self.ZF = 0
+                            self.CF = 0
+                        break
+                        
+        except Exception as e:
+            print(f"Core {self.core_id} GPU instruction error: {e}")
+            self.CF = 1  # Set carry flag to indicate error
+            
+    def run_with_threading(self):
+        """Enhanced run method with threading support."""
+        # Create initial threads if none exist
+        if self.thread_scheduler.get_total_thread_count() == 0:
+            self.create_virtual_thread(0)  # Create at least one thread
+            
+        time_slice = 0.01  # 10ms time slice per thread
+        
+        while True:
+            current_thread = self.thread_scheduler.get_current_thread(self.core_id)
+            
+            if current_thread is None:
+                break  # No threads to execute
+                
+            if current_thread.status == "terminated":
+                self.thread_scheduler.schedule_next_thread(self.core_id)
+                continue
+                
+            # Execute instructions for current thread
+            start_time = time.time()
+            instruction_count = 0
+            
+            while (time.time() - start_time) < time_slice and instruction_count < 100:
+                try:
+                    instruction = self.fetch()
+                    decoded_instruction = self.decode(instruction)
+                    self.execute_with_threading(decoded_instruction)
+                    
+                    if decoded_instruction and decoded_instruction.get('op') == 'HLT':
+                        current_thread.status = "terminated"
+                        break
+                        
+                    instruction_count += 1
+                    
+                except Exception as e:
+                    print(f"Core {self.core_id} Thread {current_thread.thread_id} error: {e}")
+                    current_thread.status = "terminated"
+                    break
+                    
+            # Schedule next thread
+            self.thread_scheduler.schedule_next_thread(self.core_id)
+            
+            # Small delay to prevent busy waiting
+            time.sleep(0.001)
+
+
+class EnhancedMultiCoreCPU(MultiCoreCPU):
+    """Enhanced multi-core CPU with GPU integration and threading support."""
+    
+    def __init__(self, num_cores: int = 50, gpu_driver: Optional[VirtualGPUDriver] = None):
+        # Initialize with enhanced cores
+        self.num_cores = num_cores
+        self.total_cores = 50000  # Virtual GPU cores, not CPU cores
+        self.cores_per_sm = self.total_cores // num_cores
+        
+        # Create enhanced cores
+        self.cores = []
+        for i in range(num_cores):
+            core = EnhancedCore(i)
+            if gpu_driver:
+                gpu_interface = CPUGPUInterface(gpu_driver)
+                core.connect_gpu_interface(gpu_interface)
+            self.cores.append(core)
+            
+        self.shared_ram = None
+        self.shared_interrupt_handler = None
+        self.gpu_driver = gpu_driver
+        
+        # Threading statistics
+        self.total_threads_created = 0
+        
+    def create_threads_on_all_cores(self, threads_per_core: int = 2):
+        """Create virtual threads on all cores to achieve 100 total threads."""
+        total_threads = 0
+        for core in self.cores:
+            for _ in range(threads_per_core):
+                thread_id = core.create_virtual_thread()
+                if thread_id != -1:
+                    total_threads += 1
+                    self.total_threads_created += 1
+                    
+        print(f"Created {total_threads} virtual threads across {self.num_cores} cores")
+        return total_threads
+        
+    def get_threading_stats(self) -> Dict[str, Any]:
+        """Get threading statistics across all cores."""
+        stats = {
+            "total_cores": self.num_cores,
+            "total_threads_created": self.total_threads_created,
+            "active_threads_per_core": {},
+            "total_active_threads": 0
+        }
+        
+        for core in self.cores:
+            thread_count = core.thread_scheduler.get_total_thread_count()
+            stats["active_threads_per_core"][core.core_id] = thread_count
+            stats["total_active_threads"] += thread_count
+            
+        return stats
+        
+    def get_gpu_stats(self) -> Dict[str, Any]:
+        """Get GPU-related statistics."""
+        if self.gpu_driver:
+            return self.gpu_driver.get_driver_stats()
+        return {"error": "No GPU driver connected"}
+        
+    def __str__(self):
+        threading_stats = self.get_threading_stats()
+        return (f"EnhancedMultiCoreCPU with {self.num_cores} cores, "
+                f"{threading_stats['total_active_threads']} active threads, "
+                f"GPU {'connected' if self.gpu_driver else 'not connected'}")
+
+
+if __name__ == "__main__":
+    # Test the enhanced CPU with GPU integration
+    print("Testing Enhanced CPU with GPU Integration...")
+    
+    # This would normally be connected to actual GPU components
+    # For testing, we'll create a mock setup
+    
+    # Create enhanced CPU
+    enhanced_cpu = EnhancedMultiCoreCPU(num_cores=4)  # Use 4 cores for testing
+    
+    # Create threads
+    threads_created = enhanced_cpu.create_threads_on_all_cores(threads_per_core=2)
+    print(f"Created {threads_created} threads")
+    
+    # Get stats
+    threading_stats = enhanced_cpu.get_threading_stats()
+    print(f"Threading stats: {threading_stats}")
+    
+    print(f"Enhanced CPU: {enhanced_cpu}")
+    print("Enhanced CPU test completed")
+

virtual_hardware/vgpu.py

@@ -0,0 +1,283 @@
+"""
+vGPU Core Processor Module
+
+This module implements the central orchestrator of the virtual GPU, managing
+workload distribution across 800 SMs and 50,000 cores, and coordinating
+operations between all other modules.
+"""
+
+import asyncio
+import time
+from collections import deque
+from enum import Enum
+from typing import Dict, List, Optional, Any
+from dataclasses import dataclass
+
+
+class TaskType(Enum):
+    """Enumeration of task types that can be processed by the vGPU."""
+    RENDER_PIXEL_BLOCK = "render_pixel_block"
+    RENDER_CLEAR = "render_clear"
+    RENDER_RECT = "render_rect"
+    RENDER_IMAGE = "render_image"
+    AI_MATRIX_MULTIPLY = "ai_matrix_multiply"
+    AI_VECTOR_OP = "ai_vector_op"
+
+
+class TaskStatus(Enum):
+    """Enumeration of task statuses."""
+    PENDING = "pending"
+    IN_PROGRESS = "in_progress"
+    COMPLETED = "completed"
+    FAILED = "failed"
+
+
+@dataclass
+class Task:
+    """Represents a single task to be processed by the vGPU."""
+    task_id: str
+    task_type: TaskType
+    payload: Dict[str, Any]
+    sm_id: Optional[int] = None
+    status: TaskStatus = TaskStatus.PENDING
+    created_time: float = 0.0
+    start_time: float = 0.0
+    end_time: float = 0.0
+
+
+class StreamingMultiprocessor:
+    """Represents a single Streaming Multiprocessor (SM) in the vGPU."""
+    
+    def __init__(self, sm_id: int, cores_per_sm: int = 62):
+        self.sm_id = sm_id
+        self.cores_per_sm = cores_per_sm
+        self.task_queue = deque()
+        self.current_task: Optional[Task] = None
+        self.is_busy = False
+        self.total_tasks_processed = 0
+        
+    def add_task(self, task: Task) -> None:
+        """Add a task to this SM's queue."""
+        task.sm_id = self.sm_id
+        self.task_queue.append(task)
+        
+    def get_next_task(self) -> Optional[Task]:
+        """Get the next task from the queue."""
+        if self.task_queue and not self.is_busy:
+            task = self.task_queue.popleft()
+            self.current_task = task
+            self.is_busy = True
+            task.status = TaskStatus.IN_PROGRESS
+            task.start_time = time.time()
+            return task
+        return None
+        
+    def complete_task(self) -> Optional[Task]:
+        """Mark the current task as completed."""
+        if self.current_task:
+            self.current_task.status = TaskStatus.COMPLETED
+            self.current_task.end_time = time.time()
+            completed_task = self.current_task
+            self.current_task = None
+            self.is_busy = False
+            self.total_tasks_processed += 1
+            return completed_task
+        return None
+        
+    def get_queue_length(self) -> int:
+        """Get the current queue length."""
+        return len(self.task_queue)
+
+
+class VirtualGPU:
+    """
+    The main Virtual GPU class that orchestrates all operations.
+    
+    This class manages 800 SMs with a total of 50,000 cores, handles task
+    distribution, and coordinates with other modules like VRAM, renderer, and AI.
+    """
+    
+    def __init__(self, num_sms: int = 800, total_cores: int = 50000):
+        self.num_sms = num_sms
+        self.total_cores = total_cores
+        self.cores_per_sm = total_cores // num_sms
+        
+        # Initialize Streaming Multiprocessors
+        self.sms: List[StreamingMultiprocessor] = []
+        for i in range(num_sms):
+            # Distribute cores evenly, with some SMs getting an extra core if needed
+            cores_for_this_sm = self.cores_per_sm
+            if i < (total_cores % num_sms):
+                cores_for_this_sm += 1
+            self.sms.append(StreamingMultiprocessor(i, cores_for_this_sm))
+        
+        # Global task management
+        self.pending_tasks = deque()
+        self.completed_tasks = deque()
+        self.task_counter = 0
+        
+        # GPU state
+        self.is_running = False
+        self.clock_cycle = 0
+        self.tick_rate = 60  # Hz
+        
+        # Module references (to be set by external initialization)
+        self.vram = None
+        self.renderer = None
+        self.ai_accelerator = None
+        self.driver = None
+        
+    def set_modules(self, vram, renderer, ai_accelerator, driver):
+        """Set references to other vGPU modules."""
+        self.vram = vram
+        self.renderer = renderer
+        self.ai_accelerator = ai_accelerator
+        self.driver = driver
+        
+    def submit_task(self, task_type: TaskType, payload: Dict[str, Any]) -> str:
+        """Submit a new task to the vGPU."""
+        task_id = f"task_{self.task_counter}"
+        self.task_counter += 1
+        
+        task = Task(
+            task_id=task_id,
+            task_type=task_type,
+            payload=payload,
+            created_time=time.time()
+        )
+        
+        self.pending_tasks.append(task)
+        return task_id
+        
+    def distribute_tasks(self) -> None:
+        """Distribute pending tasks to available SMs using round-robin."""
+        sm_index = 0
+        max_queue_length = 10  # Prevent any SM from being overloaded
+        
+        while self.pending_tasks:
+            # Find an SM that's not overloaded
+            attempts = 0
+            while attempts < self.num_sms:
+                current_sm = self.sms[sm_index]
+                if current_sm.get_queue_length() < max_queue_length:
+                    task = self.pending_tasks.popleft()
+                    current_sm.add_task(task)
+                    break
+                sm_index = (sm_index + 1) % self.num_sms
+                attempts += 1
+            
+            if attempts >= self.num_sms:
+                # All SMs are overloaded, break to avoid infinite loop
+                break
+                
+            sm_index = (sm_index + 1) % self.num_sms
+            
+    def process_sm_tasks(self) -> None:
+        """Process tasks on all SMs."""
+        for sm in self.sms:
+            # Start a new task if the SM is idle
+            if not sm.is_busy:
+                task = sm.get_next_task()
+                if task:
+                    # Task will be processed in the next step
+                    pass
+            
+            # Process the current task (simulate work completion)
+            if sm.current_task:
+                # Simulate task processing by calling appropriate module
+                self._execute_task(sm.current_task)
+                completed_task = sm.complete_task()
+                if completed_task:
+                    self.completed_tasks.append(completed_task)
+                    
+    def _execute_task(self, task: Task) -> None:
+        """Execute a specific task by calling the appropriate module."""
+        try:
+            if task.task_type == TaskType.RENDER_CLEAR and self.renderer:
+                self.renderer.clear(**task.payload)
+            elif task.task_type == TaskType.RENDER_RECT and self.renderer:
+                self.renderer.draw_rect(**task.payload)
+            elif task.task_type == TaskType.RENDER_IMAGE and self.renderer:
+                self.renderer.draw_image(**task.payload)
+            elif task.task_type == TaskType.AI_MATRIX_MULTIPLY and self.ai_accelerator:
+                self.ai_accelerator.matrix_multiply(**task.payload)
+            elif task.task_type == TaskType.AI_VECTOR_OP and self.ai_accelerator:
+                self.ai_accelerator.vector_operation(**task.payload)
+            else:
+                print(f"Unknown task type: {task.task_type}")
+                task.status = TaskStatus.FAILED
+        except Exception as e:
+            print(f"Error executing task {task.task_id}: {e}")
+            task.status = TaskStatus.FAILED
+            
+    async def tick(self) -> None:
+        """Main GPU tick cycle."""
+        self.clock_cycle += 1
+        
+        # 1. Distribute pending tasks to SMs
+        self.distribute_tasks()
+        
+        # 2. Process tasks on all SMs
+        self.process_sm_tasks()
+        
+        # 3. Handle any driver commands
+        if self.driver:
+            await self.driver.process_commands()
+            
+    async def run(self) -> None:
+        """Main GPU execution loop."""
+        self.is_running = True
+        tick_interval = 1.0 / self.tick_rate
+        
+        print(f"Starting vGPU with {self.num_sms} SMs and {self.total_cores} cores")
+        print(f"Tick rate: {self.tick_rate} Hz")
+        
+        while self.is_running:
+            start_time = time.time()
+            
+            await self.tick()
+            
+            # Maintain consistent tick rate
+            elapsed = time.time() - start_time
+            if elapsed < tick_interval:
+                await asyncio.sleep(tick_interval - elapsed)
+                
+    def stop(self) -> None:
+        """Stop the GPU execution."""
+        self.is_running = False
+        
+    def get_stats(self) -> Dict[str, Any]:
+        """Get current GPU statistics."""
+        total_tasks_processed = sum(sm.total_tasks_processed for sm in self.sms)
+        total_queue_length = sum(sm.get_queue_length() for sm in self.sms)
+        busy_sms = sum(1 for sm in self.sms if sm.is_busy)
+        
+        return {
+            "clock_cycle": self.clock_cycle,
+            "total_sms": self.num_sms,
+            "total_cores": self.total_cores,
+            "busy_sms": busy_sms,
+            "total_tasks_processed": total_tasks_processed,
+            "pending_tasks": len(self.pending_tasks),
+            "total_queue_length": total_queue_length,
+            "completed_tasks": len(self.completed_tasks)
+        }
+
+
+if __name__ == "__main__":
+    # Basic test of the vGPU
+    async def test_vgpu():
+        vgpu = VirtualGPU()
+        
+        # Submit some test tasks
+        vgpu.submit_task(TaskType.RENDER_CLEAR, {"color": (255, 0, 0)})
+        vgpu.submit_task(TaskType.RENDER_RECT, {"x": 10, "y": 10, "width": 100, "height": 50, "color": (0, 255, 0)})
+        
+        # Run a few ticks
+        for _ in range(5):
+            await vgpu.tick()
+            print(f"Stats: {vgpu.get_stats()}")
+            await asyncio.sleep(0.1)
+    
+    asyncio.run(test_vgpu())
+

virtual_hardware/virtual_gpu_driver.py

@@ -0,0 +1,348 @@
+"""
+Virtual GPU Driver Module
+
+This module provides the interface between the simulated CPU and the virtual GPU.
+It handles command queues, data transfers, and status management for CPU-GPU communication.
+"""
+
+import asyncio
+import queue
+import threading
+import time
+from typing import Dict, Any, Optional, List
+from dataclasses import dataclass
+from enum import Enum
+
+# Import virtual GPU components
+from virtual_gpu.vgpu import VirtualGPU, TaskType, Task
+from virtual_gpu.vram import VRAM
+
+
+class GPUCommandType(Enum):
+    """Types of commands that can be sent to the GPU."""
+    RENDER_CLEAR = "render_clear"
+    RENDER_RECT = "render_rect"
+    RENDER_IMAGE = "render_image"
+    AI_INFERENCE = "ai_inference"
+    AI_MATRIX_MULTIPLY = "ai_matrix_multiply"
+    AI_VECTOR_OP = "ai_vector_op"
+    DATA_TRANSFER = "data_transfer"
+    MEMORY_ALLOC = "memory_alloc"
+    MEMORY_FREE = "memory_free"
+
+
+@dataclass
+class GPUCommand:
+    """Represents a command to be executed by the GPU."""
+    command_id: str
+    command_type: GPUCommandType
+    payload: Dict[str, Any]
+    cpu_core_id: int
+    status: str = "pending"
+    result: Optional[Any] = None
+    created_time: float = 0.0
+    completed_time: float = 0.0
+
+
+class VirtualGPUDriver:
+    """
+    Virtual GPU Driver that provides the interface between CPU and GPU.
+    
+    This driver manages command queues, data transfers, and status tracking
+    for CPU-GPU communication in the integrated virtual hardware system.
+    """
+    
+    def __init__(self, vgpu: VirtualGPU, vram: VRAM):
+        self.vgpu = vgpu
+        self.vram = vram
+        
+        # Command management
+        self.command_queue = queue.Queue()
+        self.completed_commands = {}
+        self.command_counter = 0
+        
+        # Status tracking
+        self.is_running = False
+        self.driver_thread = None
+        
+        # Statistics
+        self.total_commands_processed = 0
+        self.total_data_transferred = 0
+        
+    def start_driver(self):
+        """Start the GPU driver in a separate thread."""
+        if not self.is_running:
+            self.is_running = True
+            self.driver_thread = threading.Thread(target=self._driver_loop, daemon=True)
+            self.driver_thread.start()
+            print("Virtual GPU Driver started")
+            
+    def stop_driver(self):
+        """Stop the GPU driver."""
+        self.is_running = False
+        if self.driver_thread:
+            self.driver_thread.join(timeout=1.0)
+        print("Virtual GPU Driver stopped")
+        
+    def submit_command(self, command_type: GPUCommandType, payload: Dict[str, Any], 
+                      cpu_core_id: int = 0) -> str:
+        """Submit a command to the GPU and return command ID."""
+        command_id = f"gpu_cmd_{self.command_counter}"
+        self.command_counter += 1
+        
+        command = GPUCommand(
+            command_id=command_id,
+            command_type=command_type,
+            payload=payload,
+            cpu_core_id=cpu_core_id,
+            created_time=time.time()
+        )
+        
+        self.command_queue.put(command)
+        return command_id
+        
+    def get_command_status(self, command_id: str) -> Optional[str]:
+        """Get the status of a command."""
+        if command_id in self.completed_commands:
+            return self.completed_commands[command_id].status
+        return "pending"
+        
+    def get_command_result(self, command_id: str) -> Optional[Any]:
+        """Get the result of a completed command."""
+        if command_id in self.completed_commands:
+            command = self.completed_commands[command_id]
+            if command.status == "completed":
+                return command.result
+        return None
+        
+    def wait_for_command(self, command_id: str, timeout: float = 10.0) -> bool:
+        """Wait for a command to complete."""
+        start_time = time.time()
+        while time.time() - start_time < timeout:
+            if command_id in self.completed_commands:
+                return self.completed_commands[command_id].status == "completed"
+            time.sleep(0.01)
+        return False
+        
+    def transfer_data_to_vram(self, data: Any, name: str, delay_ms: float = 0.0) -> Optional[str]:
+        """Transfer data from CPU RAM to VRAM."""
+        try:
+            texture_id = self.vram.transfer_from_ram(name, data, delay_ms)
+            if texture_id:
+                self.total_data_transferred += len(data) if hasattr(data, '__len__') else 0
+            return texture_id
+        except Exception as e:
+            print(f"Error transferring data to VRAM: {e}")
+            return None
+            
+    def create_framebuffer(self, width: int, height: int, channels: int = 3, 
+                          name: Optional[str] = None) -> Optional[str]:
+        """Create a framebuffer in VRAM."""
+        try:
+            return self.vram.create_framebuffer(width, height, channels, name)
+        except Exception as e:
+            print(f"Error creating framebuffer: {e}")
+            return None
+            
+    def _driver_loop(self):
+        """Main driver loop that processes commands."""
+        while self.is_running:
+            try:
+                # Process commands from the queue
+                try:
+                    command = self.command_queue.get_nowait()
+                    self._process_command(command)
+                except queue.Empty:
+                    pass
+                    
+                # Small delay to prevent busy waiting
+                time.sleep(0.001)
+                
+            except Exception as e:
+                print(f"Error in GPU driver loop: {e}")
+                
+    def _process_command(self, command: GPUCommand):
+        """Process a single GPU command."""
+        try:
+            command.status = "processing"
+            
+            if command.command_type == GPUCommandType.RENDER_CLEAR:
+                # Submit clear task to vGPU
+                task_id = self.vgpu.submit_task(TaskType.RENDER_CLEAR, command.payload)
+                command.result = {"task_id": task_id}
+                
+            elif command.command_type == GPUCommandType.RENDER_RECT:
+                # Submit rectangle rendering task to vGPU
+                task_id = self.vgpu.submit_task(TaskType.RENDER_RECT, command.payload)
+                command.result = {"task_id": task_id}
+                
+            elif command.command_type == GPUCommandType.RENDER_IMAGE:
+                # Submit image rendering task to vGPU
+                task_id = self.vgpu.submit_task(TaskType.RENDER_IMAGE, command.payload)
+                command.result = {"task_id": task_id}
+                
+            elif command.command_type == GPUCommandType.AI_MATRIX_MULTIPLY:
+                # Submit matrix multiplication task to vGPU
+                task_id = self.vgpu.submit_task(TaskType.AI_MATRIX_MULTIPLY, command.payload)
+                command.result = {"task_id": task_id}
+                
+            elif command.command_type == GPUCommandType.AI_VECTOR_OP:
+                # Submit vector operation task to vGPU
+                task_id = self.vgpu.submit_task(TaskType.AI_VECTOR_OP, command.payload)
+                command.result = {"task_id": task_id}
+                
+            elif command.command_type == GPUCommandType.DATA_TRANSFER:
+                # Handle data transfer to VRAM
+                data = command.payload.get("data")
+                name = command.payload.get("name", f"transfer_{command.command_id}")
+                delay_ms = command.payload.get("delay_ms", 0.0)
+                
+                texture_id = self.transfer_data_to_vram(data, name, delay_ms)
+                command.result = {"texture_id": texture_id}
+                
+            elif command.command_type == GPUCommandType.MEMORY_ALLOC:
+                # Create framebuffer or allocate memory
+                width = command.payload.get("width", 1920)
+                height = command.payload.get("height", 1080)
+                channels = command.payload.get("channels", 3)
+                name = command.payload.get("name")
+                
+                fb_id = self.create_framebuffer(width, height, channels, name)
+                command.result = {"framebuffer_id": fb_id}
+                
+            elif command.command_type == GPUCommandType.MEMORY_FREE:
+                # Free framebuffer or memory
+                name = command.payload.get("name")
+                success = self.vram.delete_framebuffer(name)
+                command.result = {"success": success}
+                
+            else:
+                command.status = "error"
+                command.result = {"error": f"Unknown command type: {command.command_type}"}
+                
+            if command.status != "error":
+                command.status = "completed"
+                
+            command.completed_time = time.time()
+            self.completed_commands[command.command_id] = command
+            self.total_commands_processed += 1
+            
+        except Exception as e:
+            command.status = "error"
+            command.result = {"error": str(e)}
+            command.completed_time = time.time()
+            self.completed_commands[command.command_id] = command
+            print(f"Error processing GPU command {command.command_id}: {e}")
+            
+    def get_driver_stats(self) -> Dict[str, Any]:
+        """Get driver statistics."""
+        vgpu_stats = self.vgpu.get_stats()
+        vram_stats = self.vram.get_stats()
+        
+        return {
+            "driver_status": "running" if self.is_running else "stopped",
+            "total_commands_processed": self.total_commands_processed,
+            "total_data_transferred": self.total_data_transferred,
+            "pending_commands": self.command_queue.qsize(),
+            "completed_commands": len(self.completed_commands),
+            "vgpu_stats": vgpu_stats,
+            "vram_stats": vram_stats
+        }
+
+
+# CPU Extensions for GPU Communication
+class CPUGPUInterface:
+    """
+    Interface for CPU cores to communicate with the GPU driver.
+    
+    This class provides high-level methods that CPU cores can use to
+    interact with the virtual GPU without dealing with low-level details.
+    """
+    
+    def __init__(self, gpu_driver: VirtualGPUDriver):
+        self.gpu_driver = gpu_driver
+        
+    def gpu_clear_screen(self, color: tuple = (0, 0, 0), core_id: int = 0) -> str:
+        """Clear the screen with the specified color."""
+        payload = {"color": color}
+        return self.gpu_driver.submit_command(GPUCommandType.RENDER_CLEAR, payload, core_id)
+        
+    def gpu_draw_rect(self, x: int, y: int, width: int, height: int, 
+                     color: tuple = (255, 255, 255), core_id: int = 0) -> str:
+        """Draw a rectangle on the screen."""
+        payload = {
+            "x": x, "y": y, "width": width, "height": height, "color": color
+        }
+        return self.gpu_driver.submit_command(GPUCommandType.RENDER_RECT, payload, core_id)
+        
+    def gpu_ai_inference(self, model_data: Any, input_data: Any, core_id: int = 0) -> str:
+        """Perform AI inference using the GPU."""
+        payload = {"model_data": model_data, "input_data": input_data}
+        return self.gpu_driver.submit_command(GPUCommandType.AI_INFERENCE, payload, core_id)
+        
+    def gpu_matrix_multiply(self, matrix_a: Any, matrix_b: Any, core_id: int = 0) -> str:
+        """Perform matrix multiplication on the GPU."""
+        payload = {"matrix_a": matrix_a, "matrix_b": matrix_b}
+        return self.gpu_driver.submit_command(GPUCommandType.AI_MATRIX_MULTIPLY, payload, core_id)
+        
+    def gpu_transfer_data(self, data: Any, name: str, core_id: int = 0) -> str:
+        """Transfer data to GPU VRAM."""
+        payload = {"data": data, "name": name}
+        return self.gpu_driver.submit_command(GPUCommandType.DATA_TRANSFER, payload, core_id)
+        
+    def gpu_alloc_framebuffer(self, width: int, height: int, channels: int = 3, 
+                             name: Optional[str] = None, core_id: int = 0) -> str:
+        """Allocate a framebuffer in GPU VRAM."""
+        payload = {"width": width, "height": height, "channels": channels, "name": name}
+        return self.gpu_driver.submit_command(GPUCommandType.MEMORY_ALLOC, payload, core_id)
+        
+    def wait_for_gpu_task(self, command_id: str, timeout: float = 10.0) -> bool:
+        """Wait for a GPU task to complete."""
+        return self.gpu_driver.wait_for_command(command_id, timeout)
+        
+    def get_gpu_result(self, command_id: str) -> Optional[Any]:
+        """Get the result of a completed GPU task."""
+        return self.gpu_driver.get_command_result(command_id)
+
+
+if __name__ == "__main__":
+    # Test the GPU driver
+    from virtual_gpu.vgpu import VirtualGPU
+    from virtual_gpu.vram import VRAM
+    
+    # Create virtual GPU and VRAM
+    vgpu = VirtualGPU(num_sms=800, total_cores=50000)
+    vram = VRAM(memory_size_gb=500)
+    
+    # Create and start the driver
+    driver = VirtualGPUDriver(vgpu, vram)
+    driver.start_driver()
+    
+    # Create CPU-GPU interface
+    cpu_gpu = CPUGPUInterface(driver)
+    
+    # Test some operations
+    print("Testing GPU driver...")
+    
+    # Clear screen
+    cmd_id = cpu_gpu.gpu_clear_screen((255, 0, 0))
+    print(f"Clear screen command: {cmd_id}")
+    
+    # Draw rectangle
+    cmd_id = cpu_gpu.gpu_draw_rect(100, 100, 200, 150, (0, 255, 0))
+    print(f"Draw rectangle command: {cmd_id}")
+    
+    # Transfer some data
+    test_data = b"Hello GPU!"
+    cmd_id = cpu_gpu.gpu_transfer_data(test_data, "test_data")
+    print(f"Data transfer command: {cmd_id}")
+    
+    # Wait a bit and check stats
+    time.sleep(1)
+    stats = driver.get_driver_stats()
+    print(f"Driver stats: {stats}")
+    
+    # Stop the driver
+    driver.stop_driver()
+    print("GPU driver test completed")
+

virtual_hardware/virtual_ram.py

@@ -0,0 +1,385 @@
+"""
+Virtual RAM Module - 128GB System Memory Abstraction
+
+This module implements a symbolic representation of 128GB system RAM using
+efficient data structures and lazy allocation strategies. It avoids allocating
+real memory and uses dictionaries or sparse mappings to simulate blocks.
+"""
+
+import time
+from typing import Dict, Any, Optional, Union
+from dataclasses import dataclass
+import numpy as np
+
+
+@dataclass
+class RAMBlock:
+    """Represents a block of memory in the symbolic RAM."""
+    name: str
+    size_bytes: int
+    allocated_time: float
+    last_accessed: float
+    access_count: int = 0
+    # We use a symbolic representation instead of actual data
+    # The data field will be None for large blocks to avoid memory allocation
+    data: Optional[Union[np.ndarray, bytes]] = None
+    is_symbolic: bool = True  # True if this is a symbolic block (no real data)
+
+
+class VirtualRAM:
+    """
+    Virtual RAM class that simulates 128GB of system memory symbolically.
+    
+    This class provides block allocation, tracking, and transfer capabilities
+    without actually allocating large amounts of physical memory.
+    """
+    
+    def __init__(self, capacity_gb: int = 128):
+        self.capacity_bytes = capacity_gb * 1024 * 1024 * 1024  # Convert GB to bytes
+        self.capacity_gb = capacity_gb
+        
+        # Block registry - stores metadata about allocated blocks
+        self.blocks: Dict[str, RAMBlock] = {}
+        
+        # Memory usage tracking
+        self.allocated_bytes = 0
+        self.allocation_counter = 0
+        
+        # Access simulation parameters
+        self.access_delay_ms = 0.1  # Simulated RAM access delay
+        self.transfer_bandwidth_gbps = 51.2  # DDR5-6400 bandwidth
+        
+        # Statistics
+        self.total_allocations = 0
+        self.total_deallocations = 0
+        self.total_accesses = 0
+        self.total_transfers = 0
+        
+        print(f"VirtualRAM initialized with {capacity_gb}GB capacity")
+        
+    def allocate_block(self, name: str, size_bytes: int, 
+                      store_data: bool = False) -> bool:
+        """
+        Allocate a block of memory symbolically.
+        
+        Args:
+            name: Unique name for the block
+            size_bytes: Size of the block in bytes
+            store_data: If True, actually allocate small amounts of real data for testing
+                       If False (default), only store metadata symbolically
+        
+        Returns:
+            True if allocation successful, False if not enough space or name exists
+        """
+        # Check if name already exists
+        if name in self.blocks:
+            print(f"Block '{name}' already exists")
+            return False
+            
+        # Check if we have enough capacity
+        if self.allocated_bytes + size_bytes > self.capacity_bytes:
+            print(f"Not enough capacity: requested {size_bytes:,} bytes, "
+                  f"available {self.capacity_bytes - self.allocated_bytes:,} bytes")
+            return False
+            
+        # Create the block
+        current_time = time.time()
+              # For all blocks, we store only metadata to avoid memory issues
+        actual_data = None
+        is_symbolic = True
+        
+        # If store_data is explicitly requested and size is very small, we can store actual data
+        if store_data and size_bytes <= 1024 * 1024 * 10: # Up to 10MB for actual data
+            actual_data = np.zeros(size_bytes, dtype=np.uint8)
+            is_symbolic = False
+            print(f"Allocated real data for block \'{name}\' ({size_bytes:,} bytes)")
+        else:
+            print(f"Created symbolic block \'{name}\' of {size_bytes:,} bytes")            
+        block = RAMBlock(
+            name=name,
+            size_bytes=size_bytes,
+            allocated_time=current_time,
+            last_accessed=current_time,
+            data=actual_data,
+            is_symbolic=is_symbolic
+        )
+        
+        self.blocks[name] = block
+        self.allocated_bytes += size_bytes
+        self.total_allocations += 1
+        self.allocation_counter += 1
+        
+        print(f"Allocated block '{name}': {size_bytes:,} bytes "
+              f"({'symbolic' if is_symbolic else 'real data'})")
+        
+        return True
+        
+    def get_block(self, name: str) -> Optional[RAMBlock]:
+        """
+        Retrieve a block by name and simulate access delay.
+        
+        Args:
+            name: Name of the block to retrieve
+            
+        Returns:
+            RAMBlock if found, None otherwise
+        """
+        if name not in self.blocks:
+            return None
+            
+        # Simulate access delay
+        time.sleep(self.access_delay_ms / 1000.0)
+        
+        # Update access statistics
+        block = self.blocks[name]
+        block.last_accessed = time.time()
+        block.access_count += 1
+        self.total_accesses += 1
+        
+        return block
+        
+    def release_block(self, name: str) -> bool:
+        """
+        Deallocate a block of memory.
+        
+        Args:
+            name: Name of the block to deallocate
+            
+        Returns:
+            True if deallocation successful, False if block not found
+        """
+        if name not in self.blocks:
+            print(f"Block '{name}' not found")
+            return False
+            
+        block = self.blocks[name]
+        self.allocated_bytes -= block.size_bytes
+        self.total_deallocations += 1
+        
+        del self.blocks[name]
+        
+        print(f"Released block '{name}': {block.size_bytes:,} bytes")
+        return True
+        
+    def transfer_to_vram(self, block_name: str, vram_instance, 
+                        vram_name: Optional[str] = None) -> Optional[str]:
+        """
+        Transfer a RAM block to VRAM with delay simulation.
+        
+        Args:
+            block_name: Name of the RAM block to transfer
+            vram_instance: Instance of VRAM to transfer to
+            vram_name: Optional name for the block in VRAM
+            
+        Returns:
+            VRAM block ID if successful, None otherwise
+        """
+        # Get the block from RAM
+        block = self.get_block(block_name)
+        if block is None:
+            print(f"Block '{block_name}' not found in RAM")
+            return None
+            
+        # Calculate transfer time based on bandwidth
+        transfer_time_ms = (block.size_bytes / (self.transfer_bandwidth_gbps * 1e9)) * 1000
+        
+        print(f"Transferring '{block_name}' ({block.size_bytes:,} bytes) "
+              f"from RAM to VRAM (estimated {transfer_time_ms:.2f}ms)")
+        
+        # Prepare data for transfer
+        if block.is_symbolic:
+            # For symbolic blocks, create a small representative data sample
+            sample_size = min(1024, block.size_bytes)  # 1KB sample
+            transfer_data = np.random.randint(0, 256, sample_size, dtype=np.uint8)
+            print(f"Using {sample_size} byte sample for symbolic block transfer")
+        else:
+            # Use actual data
+            transfer_data = block.data
+            
+        # Perform the transfer to VRAM
+        if vram_name is None:
+            vram_name = f"ram_transfer_{block_name}"
+            
+        vram_id = vram_instance.transfer_from_ram(vram_name, transfer_data, 
+                                                 delay_ms=transfer_time_ms)
+        
+        if vram_id:
+            self.total_transfers += 1
+            print(f"Successfully transferred '{block_name}' to VRAM as '{vram_id}'")
+        else:
+            print(f"Failed to transfer '{block_name}' to VRAM")
+            
+        return vram_id
+        
+    def create_tensor_block(self, name: str, shape: tuple, dtype=np.float32) -> bool:
+        """
+        Create a tensor block with specified shape and data type.
+        
+        Args:
+            name: Name for the tensor block
+            shape: Shape of the tensor (e.g., (1024, 1024, 3))
+            dtype: Data type of the tensor
+            
+        Returns:
+            True if creation successful, False otherwise
+        """
+        # Calculate size in bytes
+        element_size = np.dtype(dtype).itemsize
+        total_elements = np.prod(shape)
+        size_bytes = total_elements * element_size
+        
+        # Allocate the block symbolically
+        success = self.allocate_block(name, size_bytes, store_data=False)
+        
+        if success:
+            # Store tensor metadata
+            block = self.blocks[name]
+            block.tensor_shape = shape
+            block.tensor_dtype = dtype
+            print(f"Created tensor block '{name}' with shape {shape} and dtype {dtype}")
+            
+        return success
+        
+    def info(self) -> Dict[str, Any]:
+        """
+        Get comprehensive information about the Virtual RAM state.
+        
+        Returns:
+            Dictionary containing RAM usage statistics and metadata
+        """
+        used_bytes = self.allocated_bytes
+        free_bytes = self.capacity_bytes - used_bytes
+        utilization_percent = (used_bytes / self.capacity_bytes) * 100
+        
+        # Calculate average block size
+        avg_block_size = used_bytes / len(self.blocks) if self.blocks else 0
+        
+        # Find largest and smallest blocks
+        largest_block = max(self.blocks.values(), key=lambda b: b.size_bytes) if self.blocks else None
+        smallest_block = min(self.blocks.values(), key=lambda b: b.size_bytes) if self.blocks else None
+        
+        # Count symbolic vs real blocks
+        symbolic_blocks = sum(1 for b in self.blocks.values() if b.is_symbolic)
+        real_blocks = len(self.blocks) - symbolic_blocks
+        
+        info_dict = {
+            "capacity_gb": self.capacity_gb,
+            "capacity_bytes": self.capacity_bytes,
+            "used_bytes": used_bytes,
+            "free_bytes": free_bytes,
+            "utilization_percent": utilization_percent,
+            "total_blocks": len(self.blocks),
+            "symbolic_blocks": symbolic_blocks,
+            "real_data_blocks": real_blocks,
+            "avg_block_size_bytes": avg_block_size,
+            "largest_block_name": largest_block.name if largest_block else None,
+            "largest_block_size": largest_block.size_bytes if largest_block else 0,
+            "smallest_block_name": smallest_block.name if smallest_block else None,
+            "smallest_block_size": smallest_block.size_bytes if smallest_block else 0,
+            "total_allocations": self.total_allocations,
+            "total_deallocations": self.total_deallocations,
+            "total_accesses": self.total_accesses,
+            "total_transfers": self.total_transfers,
+            "block_names": list(self.blocks.keys())
+        }
+        
+        return info_dict
+        
+    def print_info(self) -> None:
+        """Print a formatted summary of Virtual RAM information."""
+        info = self.info()
+        
+        print("\n" + "="*50)
+        print("VIRTUAL RAM INFORMATION")
+        print("="*50)
+        print(f"Capacity: {info['capacity_gb']} GB ({info['capacity_bytes']:,} bytes)")
+        print(f"Used: {info['used_bytes']:,} bytes ({info['utilization_percent']:.2f}%)")
+        print(f"Free: {info['free_bytes']:,} bytes")
+        print(f"Total Blocks: {info['total_blocks']}")
+        print(f"  - Symbolic blocks: {info['symbolic_blocks']}")
+        print(f"  - Real data blocks: {info['real_data_blocks']}")
+        
+        if info['total_blocks'] > 0:
+            print(f"Average block size: {info['avg_block_size_bytes']:,.0f} bytes")
+            print(f"Largest block: '{info['largest_block_name']}' ({info['largest_block_size']:,} bytes)")
+            print(f"Smallest block: '{info['smallest_block_name']}' ({info['smallest_block_size']:,} bytes)")
+            
+        print(f"\nStatistics:")
+        print(f"  - Total allocations: {info['total_allocations']}")
+        print(f"  - Total deallocations: {info['total_deallocations']}")
+        print(f"  - Total accesses: {info['total_accesses']}")
+        print(f"  - Total transfers: {info['total_transfers']}")
+        
+        if info['block_names']:
+            print(f"\nBlock names: {', '.join(info['block_names'])}")
+            
+        print("="*50)
+        
+    def simulate_workload(self, num_operations: int = 100) -> None:
+        """
+        Simulate a typical workload with allocations, accesses, and deallocations.
+        
+        Args:
+            num_operations: Number of operations to simulate
+        """
+        print(f"\nSimulating workload with {num_operations} operations...")
+        
+        import random
+        
+        for i in range(num_operations):
+            operation = random.choice(['allocate', 'access', 'deallocate'])
+            
+            if operation == 'allocate' and len(self.blocks) < 50:  # Limit to 50 blocks
+                size = random.randint(1024, 100 * 1024 * 1024)  # 1KB to 100MB
+                name = f"workload_block_{i}"
+                self.allocate_block(name, size)
+                
+            elif operation == 'access' and self.blocks:
+                block_name = random.choice(list(self.blocks.keys()))
+                self.get_block(block_name)
+                
+            elif operation == 'deallocate' and self.blocks:
+                block_name = random.choice(list(self.blocks.keys()))
+                self.release_block(block_name)
+                
+        print(f"Workload simulation completed.")
+
+
+if __name__ == "__main__":
+    # Test the VirtualRAM module
+    print("Testing VirtualRAM module...")
+    
+    # Create a VirtualRAM instance with 128GB capacity
+    vram = VirtualRAM(capacity_gb=128)
+    
+    # Test basic allocation
+    print("\n1. Testing basic allocation...")
+    vram.allocate_block("small_buffer", 1024 * 1024, store_data=True)  # 1MB with real data
+    vram.allocate_block("medium_buffer", 50 * 1024 * 1024)  # 50MB symbolic
+    vram.allocate_block("large_tensor", 16 * 1024 * 1024 * 1024)  # 16GB symbolic
+    
+    # Test tensor creation
+    print("\n2. Testing tensor creation...")
+    vram.create_tensor_block("ai_weights", (1000, 1000, 512), np.float32)
+    vram.create_tensor_block("image_batch", (32, 224, 224, 3), np.uint8)
+    
+    # Test block access
+    print("\n3. Testing block access...")
+    block = vram.get_block("small_buffer")
+    if block:
+        print(f"Accessed block: {block.name}, size: {block.size_bytes:,} bytes")
+        
+    # Test info display
+    print("\n4. Testing info display...")
+    vram.print_info()
+    
+    # Test workload simulation
+    print("\n5. Testing workload simulation...")
+    vram.simulate_workload(20)
+    
+    # Final info
+    print("\n6. Final state...")
+    vram.print_info()
+    
+    print("\nVirtualRAM test completed!")
+

virtual_hardware/virtual_ssd.py

@@ -0,0 +1,318 @@
+
+"""
+Virtual SSD Main Module - Integrates all components
+"""
+import os
+import shutil
+import json
+import base64
+from typing import Optional, Dict
+from builtins import open
+
+from .virtual_flash import VirtualFlash
+from .file_system_map import FileSystemMap
+from .ssd_controller import SSDController
+from .virtual_ram_buffer import VirtualRAMBuffer
+from .virtual_driver import VirtualDriver
+from .virtual_os import VirtualOS
+from .app_interface import AppInterface
+
+from .persistent_virtual_disk import PersistentVirtualDisk
+from .volatile_virtual_disk import VolatileVirtualDisk
+
+class VirtualSSD:
+    """
+    Main class to integrate all virtual SSD components.
+    Provides mount, shutdown, and application-level file operations.
+    """
+    
+    def __init__(self, capacity_gb: int = 2048, page_size: int = 4096, pages_per_block: int = 256):
+        self.capacity_gb = capacity_gb
+        self.page_size = page_size
+        self.pages_per_block = pages_per_block
+        
+        # Calculate total logical blocks based on capacity and page size
+        # Assuming 1 logical block = 1 page for simplicity in initial FTL
+        self.total_logical_blocks = (capacity_gb * 1024 * 1024 * 1024) // page_size
+        
+        self.flash: Optional[VirtualFlash] = None
+        self.ram_buffer: Optional[VirtualRAMBuffer] = None
+        self.controller: Optional[SSDController] = None
+        self.file_system: Optional[FileSystemMap] = None
+        self.driver: Optional[VirtualDriver] = None
+        self.os: Optional[VirtualOS] = None
+        self.app_interface: Optional[AppInterface] = None
+        
+        self.mounted = False
+        
+        # Persistent Virtual Disk
+        self.pvd = PersistentVirtualDisk(capacity_gb, page_size, pages_per_block)
+        self.vvd: Optional[VolatileVirtualDisk] = None
+
+        print("VirtualSSD instance created.")
+    
+    def mount(self):
+        """
+        Mount the virtual SSD, initializing all components.
+        """
+        if self.mounted:
+            print("Virtual SSD already mounted.")
+            return
+        
+        print("Mounting Virtual SSD...")
+        
+        # Ensure storage directory exists for snapshot
+        os.makedirs("virtual_ssd_data", exist_ok=True)
+        
+        try:
+            # Load PVD state
+            pvd_snapshot_path = os.path.join("virtual_ssd_data", "pvd_snapshot.json")
+            pvd_snapshot_data = None
+            if os.path.exists(pvd_snapshot_path):
+                with open(pvd_snapshot_path, 'r') as f:
+                    pvd_snapshot_data = json.load(f)
+                self.pvd.load_from_storage(pvd_snapshot_data['pvd_state'])
+            else:
+                self.pvd.load_from_storage() # Initialize fresh state
+
+            self.flash = self.pvd.get_flash()
+            self.controller = self.pvd.get_controller()
+            self.file_system = self.pvd.get_file_system()
+
+            self.vvd = VolatileVirtualDisk(self.flash, self.file_system, self.controller)
+
+            self.ram_buffer = VirtualRAMBuffer(capacity_bytes=128 * 1024 * 1024) # 128MB buffer
+            self.driver = VirtualDriver(self.vvd, self.page_size) # Driver interacts with VVD
+            self.os = VirtualOS(self.file_system, self.driver, self.ram_buffer)
+            self.app_interface = AppInterface(self.os)
+
+            self.mounted = True
+            print("Virtual SSD mounted successfully.")
+        except Exception as e:
+            print(f"Error mounting Virtual SSD: {e}")
+            self.shutdown() # Attempt to clean up
+            self.mounted = False
+    
+    def shutdown(self):
+        """
+        Shutdown the virtual SSD, saving all states to a snapshot.
+        """
+        if not self.mounted:
+            print("Virtual SSD not mounted.")
+            return
+            
+        print("Shutting down Virtual SSD...")
+        
+        try:
+            if self.app_interface:
+                self.app_interface.sync() # Ensure all data is flushed
+            if self.vvd:
+                self.vvd.shutdown() # Flush dirty pages to PVD
+            if self.os:
+                self.os.shutdown()
+            if self.driver:
+                self.driver.shutdown()
+            
+            # Save PVD state
+            os.makedirs("virtual_ssd_data", exist_ok=True) # Ensure directory exists
+            pvd_snapshot_path = os.path.join("virtual_ssd_data", "pvd_snapshot.json")
+            pvd_state = self.pvd.save_to_storage()
+            with open(pvd_snapshot_path, 'w') as f:
+                json.dump({"pvd_state": pvd_state}, f, indent=2)
+            print(f"PVD state saved to snapshot: {pvd_snapshot_path}")
+
+            self.pvd.shutdown()
+            
+            self.mounted = False
+            print("Virtual SSD shutdown complete.")
+        except Exception as e:
+            print(f"Error during Virtual SSD shutdown: {e}")
+
+    def save_file(self, filename: str, data: bytes) -> bool:
+        """
+        Save a file to the virtual SSD.
+        """
+        if not self.mounted or not self.app_interface:
+            print("Error: Virtual SSD not mounted.")
+            return False
+        return self.app_interface.save(filename, data)
+    
+    def read_file(self, filename: str) -> Optional[bytes]:
+        """
+        Read a file from the virtual SSD.
+        """
+        if not self.mounted or not self.app_interface:
+            print("Error: Virtual SSD not mounted.")
+            return None
+        return self.app_interface.load(filename)
+    
+    def delete_file(self, filename: str) -> bool:
+        """
+        Delete a file from the virtual SSD.
+        """
+        if not self.mounted or not self.app_interface:
+            print("Error: Virtual SSD not mounted.")
+            return False
+        return self.app_interface.delete(filename)
+    
+    def list_files(self) -> Dict:
+        """
+        List all files and their metadata on the virtual SSD.
+        """
+        if not self.mounted or not self.app_interface:
+            print("Error: Virtual SSD not mounted.")
+            return {}
+        return self.app_interface.list_files()
+    
+    def get_capacity_info(self) -> Dict:
+        """
+        Get capacity information of the virtual SSD.
+        """
+        if not self.mounted or not self.app_interface:
+            print("Error: Virtual SSD not mounted.")
+            return {}
+        return self.app_interface.get_capacity_info()
+    
+    def get_full_stats(self) -> Dict:
+        """
+        Get comprehensive statistics from all layers.
+        """
+        if not self.mounted or not self.app_interface or not self.os or not self.flash or not self.controller:
+            print("Error: Virtual SSD not mounted or components missing.")
+            return {}
+        
+        return {
+            "flash_stats": self.flash.get_flash_stats(),
+            "ftl_stats": self.controller.get_ftl_stats(),
+            "file_system_stats": self.file_system.get_usage_stats(),
+            "ram_buffer_stats": self.ram_buffer.get_buffer_status()
+        }
+    
+    def format_ssd(self):
+        """
+        Formats the virtual SSD, deleting all data and resetting state.
+        """
+        if self.mounted:
+            self.shutdown()
+        
+        print("Formatting Virtual SSD...")
+        storage_dir = "virtual_ssd_data"
+        if os.path.exists(storage_dir):
+            shutil.rmtree(storage_dir)
+            print(f"Removed existing storage directory: {storage_dir}")
+
+        self.pvd = PersistentVirtualDisk(self.capacity_gb, self.page_size, self.pages_per_block)
+        self.pvd.format_disk()
+        self.flash = self.pvd.get_flash()
+        self.controller = self.pvd.get_controller()
+        self.file_system = self.pvd.get_file_system()
+
+        self.ram_buffer = VirtualRAMBuffer(capacity_bytes=128 * 1024 * 1024)
+        self.driver = VirtualDriver(self.controller, self.page_size)
+        self.os = VirtualOS(self.file_system, self.driver, self.ram_buffer)
+        self.app_interface = AppInterface(self.os)
+
+        self.mounted = True
+        print("Virtual SSD formatted and re-mounted.")
+
+    def __del__(self):
+        """
+        Ensure shutdown is called on object deletion.
+        """
+        try:
+            if self.mounted:
+                self.shutdown()
+        except:
+            pass
+
+if __name__ == "__main__":
+    # Example Usage
+    ssd = VirtualSSD(capacity_gb=2) # Create a 2GB virtual SSD for testing
+    ssd.mount()
+
+    # Test file operations
+    test_data_small = b"Hello, this is a small test file." * 10 # 330 bytes
+    test_data_large = b"This is a larger test file content." * 1000 # 35KB
+    test_data_very_large = b"A very large file for testing purposes. " * 100000 # ~4MB
+
+    print("\n--- Testing file saves ---")
+    ssd.save_file("small_file.txt", test_data_small)
+    ssd.save_file("large_file.bin", test_data_large)
+    ssd.save_file("video.mp4", test_data_very_large)
+    ssd.save_file("another_file.txt", b"Some more data.")
+
+    # Upload the created test file
+    with open("/home/ubuntu/test_upload_file.txt", "rb") as f:
+        uploaded_data = f.read()
+    ssd.save_file("uploaded_test_file.txt", uploaded_data)
+    print("Uploaded test_upload_file.txt to virtual SSD.")
+
+    print("\n--- Listing files ---")
+    files = ssd.list_files()
+    for filename, info in files.items():
+        print(f"File: {filename}, Size: {info['size']} bytes, Blocks: {len(info['blocks'])}")
+
+    print("\n--- Checking capacity info ---")
+    capacity_info = ssd.get_capacity_info()
+    print(f"Total: {capacity_info['total_gb']} GB, Used: {capacity_info['used_gb']} GB, Free: {capacity_info['free_gb']} GB, Usage: {capacity_info['usage_percent']:.2f}%")
+
+    print("\n--- Reading files ---")
+    read_small_data = ssd.read_file("small_file.txt")
+    print(f"Read small_file.txt: {read_small_data[:50]}...")
+    assert read_small_data == test_data_small
+
+    read_large_data = ssd.read_file("large_file.bin")
+    print(f"Read large_file.bin: {read_large_data[:50]}...")
+    assert read_large_data == test_data_large
+
+    read_video_data = ssd.read_file("video.mp4")
+    print(f"Read video.mp4: {read_video_data[:50]}...")
+    assert read_video_data == test_data_very_large
+
+    read_uploaded_data = ssd.read_file("uploaded_test_file.txt")
+    print(f"Read uploaded_test_file.txt: {read_uploaded_data[:50]}...")
+    assert read_uploaded_data == uploaded_data
+
+    print("\n--- Deleting a file ---")
+    ssd.delete_file("large_file.bin")
+    print("\n--- Listing files after deletion ---")
+    files = ssd.list_files()
+    for filename, info in files.items():
+        print(f"File: {filename}, Size: {info['size']} bytes, Blocks: {len(info['blocks'])}")
+
+    print("\n--- Checking capacity info after deletion ---")
+    capacity_info = ssd.get_capacity_info()
+    print(f"Total: {capacity_info['total_gb']} GB, Used: {capacity_info['used_gb']} GB, Free: {capacity_info['free_gb']} GB, Usage: {capacity_info['usage_percent']:.2f}%")
+
+    print("\n--- Testing persistence ---")
+    ssd.shutdown()
+    print("SSD shut down. Re-mounting to check persistence.")
+    ssd_reloaded = VirtualSSD(capacity_gb=2) # Same capacity
+    ssd_reloaded.mount()
+
+    print("\n--- Listing files after re-mount ---")
+    files_reloaded = ssd_reloaded.list_files()
+    for filename, info in files_reloaded.items():
+        print(f"File: {filename}, Size: {info['size']} bytes, Blocks: {len(info['blocks'])}")
+    
+    read_small_data_reloaded = ssd_reloaded.read_file("small_file.txt")
+    assert read_small_data_reloaded == test_data_small
+    print("small_file.txt read successfully after remount.")
+
+    read_uploaded_data_reloaded = ssd_reloaded.read_file("uploaded_test_file.txt")
+    assert read_uploaded_data_reloaded == uploaded_data
+    print("uploaded_test_file.txt read successfully after remount.")
+
+    # Test formatting
+    print("\n--- Testing format ---")
+    ssd_reloaded.format_ssd()
+    print("\n--- Listing files after format ---")
+    files_after_format = ssd_reloaded.list_files()
+    print(f"Files after format: {files_after_format}")
+    assert not files_after_format
+
+    ssd_reloaded.shutdown()
+    print("All tests complete.")
+
+
+

virtual_hardware/vram.py

@@ -0,0 +1,361 @@
+import numpy as np
+from collections import OrderedDict
+from typing import Dict, Any, Optional, Tuple, Union
+from dataclasses import dataclass
+import time
+
+
+@dataclass
+class MemoryBlock:
+    """Represents a block of memory in the symbolic VRAM."""
+    address: int
+    size: int
+    data: Optional[Any]
+    allocated_time: float
+    last_accessed: float
+
+
+class Framebuffer:
+    """Represents a 2D drawing surface in VRAM."""
+    
+    def __init__(self, width: int, height: int, channels: int = 3, dtype=np.uint8):
+        self.width = width
+        self.height = height
+        self.channels = channels
+        self.dtype = dtype
+        
+        # Create the pixel buffer symbolically to avoid large allocations
+        # The actual pixel data will be managed by the MemoryManager
+        self.pixel_buffer_address: Optional[int] = None
+        self.pixel_buffer_size: int = width * height * channels * np.dtype(dtype).itemsize
+        self.pixel_buffer = np.zeros((height, width, channels), dtype=dtype)
+        self.vram_address: Optional[int] = None # This is the address in the MemoryManager
+        
+    def resize(self, new_width: int, new_height: int) -> None:
+        # No actual data to resize, just update symbolic size
+        self.width = new_width
+        self.height = new_height
+        self.pixel_buffer_size = new_width * new_height * self.channels * np.dtype(self.dtype).itemsize
+
+    def clear(self, color: Tuple[int, int, int]) -> None:
+        self.pixel_buffer[:, :] = color
+
+    def get_pixel(self, x: int, y: int) -> np.ndarray:
+        if 0 <= x < self.width and 0 <= y < self.height:
+            return self.pixel_buffer[y, x]
+        return np.zeros(self.channels, dtype=self.dtype)
+    def set_pixel(self, x: int, y: int, color: Tuple[int, int, int]) -> None:
+        if 0 <= x < self.width and 0 <= y < self.height:
+            self.pixel_buffer[y, x] = color[:self.channels]
+
+    def get_memory_usage(self) -> int:
+        """Get the memory usage of this framebuffer in bytes."""
+        return self.pixel_buffer_size
+
+
+class MemoryManager:
+    """Manages the symbolic 500GB GDDR7 memory space."""
+    
+    def __init__(self, total_memory_gb: int = 500, block_size_kb: int = 4):
+        self.total_memory_bytes = total_memory_gb * 1024 * 1024 * 1024  # 500GB
+        self.block_size_bytes = block_size_kb * 1024  # 4KB blocks
+        self.total_blocks = self.total_memory_bytes // self.block_size_bytes
+        
+        # Symbolic memory space - only allocated blocks are stored
+        self.memory_blocks: Dict[int, MemoryBlock] = {}
+        
+        # Free block tracking - use a list of free block ranges instead of a set of all blocks
+        self.free_block_ranges = [(0, self.total_blocks - 1)] # (start_block_id, end_block_id)
+        self.allocated_blocks = set() # Still track allocated blocks for quick lookup
+        
+        # Address allocation counter
+        self.next_address = 0
+        
+    def allocate_block(self, size_bytes: int) -> Optional[int]:
+        """Allocate a block of memory and return its address."""
+        blocks_needed = (size_bytes + self.block_size_bytes - 1) // self.block_size_bytes
+        
+        # Find a suitable contiguous block range
+        for i, (start, end) in enumerate(self.free_block_ranges):
+            available_blocks = end - start + 1
+            if available_blocks >= blocks_needed:
+                # Found a suitable range
+                base_block_id = start
+                
+                # Update free_block_ranges
+                new_start = start + blocks_needed
+                if new_start <= end:
+                    self.free_block_ranges[i] = (new_start, end)
+                else:
+                    self.free_block_ranges.pop(i)
+                
+                # Add to allocated_blocks
+                for j in range(blocks_needed):
+                    self.allocated_blocks.add(base_block_id + j)
+                    
+                # Create memory block
+                base_address = base_block_id * self.block_size_bytes
+                
+                memory_block = MemoryBlock(
+                    address=base_address,
+                    size=size_bytes,
+                    data=bytearray(size_bytes), # Allocate actual bytearray for data
+                    allocated_time=time.time(),
+                    last_accessed=time.time()
+                )
+                self.memory_blocks[base_address] = memory_block
+                return base_address
+        
+        return None  # Out of memory
+
+    def deallocate_block(self, address: int) -> bool:
+        """Deallocate a block of memory."""
+        if address in self.memory_blocks:
+            memory_block = self.memory_blocks[address]
+            blocks_to_free = (memory_block.size + self.block_size_bytes - 1) // self.block_size_bytes
+            
+            base_block_id = address // self.block_size_bytes
+            for i in range(blocks_to_free):
+                block_id = base_block_id + i
+                if block_id in self.allocated_blocks:
+                    self.allocated_blocks.remove(block_id)
+                    # Add back to free_block_ranges (simple merge for now)
+                    self.free_block_ranges.append((block_id, block_id))
+                    self.free_block_ranges.sort() # Keep sorted for efficient merging
+                    
+            del self.memory_blocks[address]
+            return True
+        return False
+        
+    def read_data(self, address: int, size: int) -> Optional[np.ndarray]:
+        """Read data from memory."""
+        if address in self.memory_blocks:
+            memory_block = self.memory_blocks[address]
+            if memory_block.data is not None and size <= memory_block.size:
+                return np.frombuffer(memory_block.data[:size], dtype=np.uint8) # Return as numpy array
+        return None
+        
+    def write_data(self, address: int, data: Union[np.ndarray, bytes]) -> bool:
+        """Write data to memory."""
+        if address in self.memory_blocks:
+            memory_block = self.memory_blocks[address]
+            if memory_block.data is not None:
+                if isinstance(data, np.ndarray):
+                    data_bytes = data.tobytes()
+                elif isinstance(data, bytes):
+                    data_bytes = data
+                else:
+                    raise TypeError("Data must be a NumPy array or bytes.")
+
+                if len(data_bytes) <= memory_block.size:
+                    memory_block.data[:len(data_bytes)] = data_bytes
+                    return True
+        return False
+        
+    def get_memory_stats(self) -> Dict[str, Any]:
+        """Get memory usage statistics."""
+        allocated_bytes = sum(block.size for block in self.memory_blocks.values())
+        free_bytes = self.total_memory_bytes - allocated_bytes
+        
+        return {
+            "total_memory_gb": self.total_memory_bytes / (1024**3),
+            "allocated_bytes": allocated_bytes,
+            "free_bytes": free_bytes,
+            "allocated_blocks_count": len(self.allocated_blocks),
+            "free_block_ranges_count": len(self.free_block_ranges),
+            "utilization_percent": (allocated_bytes / self.total_memory_bytes) * 100 if self.total_memory_bytes > 0 else 0
+        }
+
+
+class VRAM:
+    """
+    Main VRAM class that provides the interface for the 500GB GDDR7 memory.
+    
+    This class combines the MemoryManager for low-level memory operations
+    with higher-level abstractions like Framebuffers.
+    """
+    
+    def __init__(self, memory_size_gb: int = 500):
+        self.memory_manager = MemoryManager(memory_size_gb)
+        
+        # Cache for frequently accessed data (simulates L1/L2 cache)
+        self.cache_size = 1000  # Number of cache entries
+        self.cache = OrderedDict()
+        
+        # Framebuffer registry
+        self.framebuffers: Dict[str, Framebuffer] = {}
+        self.framebuffer_counter = 0
+        
+        # Texture registry
+        self.textures: Dict[str, np.ndarray] = {}
+        self.texture_counter = 0
+        
+    def create_framebuffer(self, width: int, height: int, channels: int = 3, 
+                          name: Optional[str] = None) -> str:
+        """Create a new framebuffer and return its ID."""
+        if name is None:
+            name = f"framebuffer_{self.framebuffer_counter}"
+            self.framebuffer_counter += 1
+            
+        framebuffer = Framebuffer(width, height, channels)
+        
+        # Allocate memory for the framebuffer
+        memory_size = framebuffer.get_memory_usage()
+        address = self.memory_manager.allocate_block(memory_size)
+        
+        if address is not None:
+            framebuffer.vram_address = address
+            self.framebuffers[name] = framebuffer
+            return name
+        else:
+            raise MemoryError("Failed to allocate memory for framebuffer")
+            
+    def get_framebuffer(self, name: str) -> Optional[Framebuffer]:
+        """Get a framebuffer by name."""
+        return self.framebuffers.get(name)
+        
+    def delete_framebuffer(self, name: str) -> bool:
+        """Delete a framebuffer and free its memory."""
+        if name in self.framebuffers:
+            framebuffer = self.framebuffers[name]
+            if framebuffer.vram_address is not None:
+                self.memory_manager.deallocate_block(framebuffer.vram_address)
+            del self.framebuffers[name]
+            return True
+        return False
+        
+    def load_texture(self, texture_data: Union[np.ndarray, bytes], name: Optional[str] = None) -> str:
+        """Load texture data into VRAM and return its ID."""
+        if name is None:
+            name = f"texture_{self.texture_counter}"
+            self.texture_counter += 1
+            
+        size_bytes = 0
+        if isinstance(texture_data, np.ndarray):
+            size_bytes = texture_data.nbytes
+        elif isinstance(texture_data, bytes):
+            size_bytes = len(texture_data)
+        else:
+            raise TypeError("Texture data must be a NumPy array or bytes.")
+            
+        # Allocate memory for the texture
+        address = self.memory_manager.allocate_block(size_bytes)
+        
+        if address is not None:
+            self.memory_manager.write_data(address, texture_data) # Write actual data
+            self.textures[name] = texture_data # Store actual data for reference
+            return name
+        else:
+            raise MemoryError("Failed to allocate memory for texture")
+            
+    def get_texture(self, name: str) -> Optional[np.ndarray]:
+        """Get texture data by name."""
+        return self.textures.get(name)
+        
+    def cache_read(self, address: int, size: int) -> Optional[np.ndarray]:
+        """Read data with caching support."""
+        cache_key = (address, size)
+        
+        # Check cache first
+        if cache_key in self.cache:
+            # Move to end (most recently used)
+            data = self.cache.pop(cache_key)
+            self.cache[cache_key] = data
+            return data.copy()
+            
+        # Read from memory
+        data = self.memory_manager.read_data(address, size)
+        if data is not None:
+            # Add to cache
+            if len(self.cache) >= self.cache_size:
+                # Remove least recently used item
+                self.cache.popitem(last=False)
+            self.cache[cache_key] = data.copy()
+            
+        return data
+        
+    def transfer_from_ram(self, name: str, data: Union[np.ndarray, bytes], 
+                          delay_ms: float = 0.0) -> Optional[str]:
+        """Transfer a block of data from RAM to VRAM."""
+        if isinstance(data, np.ndarray):
+            size_bytes = data.nbytes
+            data_to_store = data.flatten()
+        elif isinstance(data, bytes):
+            size_bytes = len(data)
+            data_to_store = np.frombuffer(data, dtype=np.uint8)
+        else:
+            raise TypeError("Data must be a NumPy array or bytes.")
+            
+        # Simulate delay
+        if delay_ms > 0:
+            time.sleep(delay_ms / 1000.0)
+            
+        # Allocate memory in VRAM
+        address = self.memory_manager.allocate_block(size_bytes)
+        
+        if address is not None:
+            # Store data in VRAM
+            self.memory_manager.write_data(address, data_to_store)
+            
+            # Register the transferred data as a texture/buffer in VRAM
+            # For simplicity, we\"ll register it as a texture for now
+            texture_id = f"ram_transfer_{self.texture_counter}"
+            self.texture_counter += 1
+            self.textures[texture_id] = data # Store actual data for reference
+            print(f"Transferred {size_bytes} bytes from RAM to VRAM at address {address} as {texture_id}")
+            return texture_id
+        else:
+            print(f"Failed to transfer {size_bytes} bytes from RAM to VRAM: Out of VRAM memory.")
+            return None
+
+    def get_stats(self) -> Dict[str, Any]:
+        """Get comprehensive VRAM statistics."""
+        memory_stats = self.memory_manager.get_memory_stats()
+        
+        framebuffer_memory = sum(fb.get_memory_usage() for fb in self.framebuffers.values())
+        texture_memory = sum(tex.nbytes for tex in self.textures.values())
+        
+        return {
+            **memory_stats,
+            "framebuffers_count": len(self.framebuffers),
+            "textures_count": len(self.textures),
+            "framebuffer_memory_bytes": framebuffer_memory,
+            "texture_memory_bytes": texture_memory,
+            "cache_entries": len(self.cache),
+            "cache_hit_ratio": 0.0  # TODO: Implement cache hit tracking
+        }
+
+
+if __name__ == "__main__":
+    # Test the VRAM module
+    vram = VRAM(memory_size_gb=1)  # Use 1GB for testing
+    
+    # Create a framebuffer
+    fb_id = vram.create_framebuffer(1920, 1080, 3)
+    print(f"Created framebuffer: {fb_id}")
+    
+    # Get the framebuffer and modify it
+    fb = vram.get_framebuffer(fb_id)
+    if fb:
+        fb.clear((255, 0, 0))  # Clear to red
+        fb.set_pixel(100, 100, (0, 255, 0))  # Set a green pixel
+        print(f"Framebuffer size: {fb.width}x{fb.height}")
+        print(f"Pixel at (100, 100): {fb.get_pixel(100, 100)}")
+        
+    # Load a test texture
+    test_texture = np.random.randint(0, 256, (256, 256, 3), dtype=np.uint8)
+    tex_id = vram.load_texture(test_texture)
+    print(f"Loaded texture: {tex_id}")
+    
+    # Test transfer_from_ram
+    ram_data = b"\x01\x02\x03\x04\x05\x06\x07\x08"
+    transferred_id = vram.transfer_from_ram("test_ram_data", ram_data, delay_ms=10)
+    print(f"Transferred RAM data ID: {transferred_id}")
+    
+    # Print statistics
+    stats = vram.get_stats()
+    print(f"VRAM Stats: {stats}")
+
+
+
+