Delete virtual_hardware

virtual_hardware/ai.py

@@ -1,446 +0,0 @@
-"""
-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

@@ -1,312 +0,0 @@
-"""
-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

@@ -1,407 +0,0 @@
-"""
-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

@@ -1,283 +0,0 @@
-"""
-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

@@ -1,348 +0,0 @@
-"""
-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

@@ -1,385 +0,0 @@
-"""
-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

@@ -1,318 +0,0 @@
-
-"""
-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

@@ -1,361 +0,0 @@
-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}")
-
-
-
-