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src/ai.py

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+"""
+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!")
+

src/driver.py

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

src/render.py

@@ -0,0 +1,382 @@
+"""
+Render Module - Software Raster Pipeline
+
+This module implements the software raster pipeline for drawing primitives
+and images onto framebuffers stored in VRAM.
+"""
+
+import numpy as np
+from typing import Tuple, Optional, Any, Dict
+import time
+
+
+class Renderer:
+    """
+    Software-based renderer that implements basic drawing operations.
+    
+    This renderer operates on framebuffers stored in VRAM and provides
+    functions for drawing primitives like rectangles, lines, and pixels.
+    """
+    
+    def __init__(self, vram=None):
+        self.vram = vram
+        self.current_shader = None
+        
+        # Rendering statistics
+        self.pixels_drawn = 0
+        self.draw_calls = 0
+        self.render_time = 0.0
+        
+    def set_vram(self, vram):
+        """Set the VRAM reference."""
+        self.vram = vram
+        
+    def set_shader(self, shader):
+        """Set the current shader for rendering operations."""
+        self.current_shader = shader
+        
+    def clear(self, framebuffer_id: str, color: Tuple[int, int, int] = (0, 0, 0)) -> bool:
+        """Clear a framebuffer with the specified color."""
+        if not self.vram:
+            return False
+            
+        start_time = time.time()
+        
+        framebuffer = self.vram.get_framebuffer(framebuffer_id)
+        if not framebuffer:
+            return False
+            
+        try:
+            framebuffer.clear(color)
+            self.pixels_drawn += framebuffer.width * framebuffer.height
+            self.draw_calls += 1
+            self.render_time += time.time() - start_time
+            return True
+        except Exception as e:
+            print(f"Error clearing framebuffer {framebuffer_id}: {e}")
+            return False
+            
+    def draw_pixel(self, framebuffer_id: str, x: int, y: int, 
+                   color: Tuple[int, int, int] = (255, 255, 255)) -> bool:
+        """Draw a single pixel on the framebuffer."""
+        if not self.vram:
+            return False
+            
+        start_time = time.time()
+        
+        framebuffer = self.vram.get_framebuffer(framebuffer_id)
+        if not framebuffer:
+            return False
+            
+        try:
+            # Apply shader if available
+            final_color = color
+            if self.current_shader:
+                final_color = self.current_shader.process_pixel(x, y, color)
+                
+            framebuffer.set_pixel(x, y, final_color)
+            self.pixels_drawn += 1
+            self.draw_calls += 1
+            self.render_time += time.time() - start_time
+            return True
+        except Exception as e:
+            print(f"Error drawing pixel at ({x}, {y}): {e}")
+            return False
+            
+    def draw_rect(self, framebuffer_id: str, x: int, y: int, width: int, height: int,
+                  color: Tuple[int, int, int] = (255, 255, 255)) -> bool:
+        """Draw a filled rectangle on the framebuffer."""
+        if not self.vram:
+            return False
+            
+        start_time = time.time()
+        
+        framebuffer = self.vram.get_framebuffer(framebuffer_id)
+        if not framebuffer:
+            return False
+            
+        try:
+            # Clamp rectangle to framebuffer bounds
+            x1 = max(0, x)
+            y1 = max(0, y)
+            x2 = min(framebuffer.width, x + width)
+            y2 = min(framebuffer.height, y + height)
+            
+            if x2 <= x1 or y2 <= y1:
+                return True  # Nothing to draw
+                
+            # Use NumPy for efficient rectangle filling
+            if self.current_shader:
+                # Apply shader to each pixel (slower but more flexible)
+                for py in range(y1, y2):
+                    for px in range(x1, x2):
+                        final_color = self.current_shader.process_pixel(px, py, color)
+                        framebuffer.pixel_buffer[py, px] = final_color[:framebuffer.channels]
+            else:
+                # Direct fill (faster)
+                framebuffer.pixel_buffer[y1:y2, x1:x2] = color[:framebuffer.channels]
+                
+            pixels_affected = (x2 - x1) * (y2 - y1)
+            self.pixels_drawn += pixels_affected
+            self.draw_calls += 1
+            self.render_time += time.time() - start_time
+            return True
+            
+        except Exception as e:
+            print(f"Error drawing rectangle at ({x}, {y}, {width}, {height}): {e}")
+            return False
+            
+    def draw_line(self, framebuffer_id: str, x1: int, y1: int, x2: int, y2: int,
+                  color: Tuple[int, int, int] = (255, 255, 255)) -> bool:
+        """Draw a line using Bresenham's algorithm."""
+        if not self.vram:
+            return False
+            
+        start_time = time.time()
+        
+        framebuffer = self.vram.get_framebuffer(framebuffer_id)
+        if not framebuffer:
+            return False
+            
+        try:
+            # Bresenham's line algorithm
+            dx = abs(x2 - x1)
+            dy = abs(y2 - y1)
+            sx = 1 if x1 < x2 else -1
+            sy = 1 if y1 < y2 else -1
+            err = dx - dy
+            
+            x, y = x1, y1
+            pixels_drawn = 0
+            
+            while True:
+                # Draw pixel if within bounds
+                if 0 <= x < framebuffer.width and 0 <= y < framebuffer.height:
+                    final_color = color
+                    if self.current_shader:
+                        final_color = self.current_shader.process_pixel(x, y, color)
+                    framebuffer.set_pixel(x, y, final_color)
+                    pixels_drawn += 1
+                    
+                if x == x2 and y == y2:
+                    break
+                    
+                e2 = 2 * err
+                if e2 > -dy:
+                    err -= dy
+                    x += sx
+                if e2 < dx:
+                    err += dx
+                    y += sy
+                    
+            self.pixels_drawn += pixels_drawn
+            self.draw_calls += 1
+            self.render_time += time.time() - start_time
+            return True
+            
+        except Exception as e:
+            print(f"Error drawing line from ({x1}, {y1}) to ({x2}, {y2}): {e}")
+            return False
+            
+    def draw_circle(self, framebuffer_id: str, center_x: int, center_y: int, radius: int,
+                    color: Tuple[int, int, int] = (255, 255, 255), filled: bool = False) -> bool:
+        """Draw a circle using the midpoint circle algorithm."""
+        if not self.vram:
+            return False
+            
+        start_time = time.time()
+        
+        framebuffer = self.vram.get_framebuffer(framebuffer_id)
+        if not framebuffer:
+            return False
+            
+        try:
+            pixels_drawn = 0
+            
+            if filled:
+                # Draw filled circle
+                for y in range(center_y - radius, center_y + radius + 1):
+                    for x in range(center_x - radius, center_x + radius + 1):
+                        if (x - center_x) ** 2 + (y - center_y) ** 2 <= radius ** 2:
+                            if 0 <= x < framebuffer.width and 0 <= y < framebuffer.height:
+                                final_color = color
+                                if self.current_shader:
+                                    final_color = self.current_shader.process_pixel(x, y, color)
+                                framebuffer.set_pixel(x, y, final_color)
+                                pixels_drawn += 1
+            else:
+                # Draw circle outline using midpoint algorithm
+                x = 0
+                y = radius
+                d = 1 - radius
+                
+                def draw_circle_points(cx, cy, x, y):
+                    points = [
+                        (cx + x, cy + y), (cx - x, cy + y),
+                        (cx + x, cy - y), (cx - x, cy - y),
+                        (cx + y, cy + x), (cx - y, cy + x),
+                        (cx + y, cy - x), (cx - y, cy - x)
+                    ]
+                    drawn = 0
+                    for px, py in points:
+                        if 0 <= px < framebuffer.width and 0 <= py < framebuffer.height:
+                            final_color = color
+                            if self.current_shader:
+                                final_color = self.current_shader.process_pixel(px, py, color)
+                            framebuffer.set_pixel(px, py, final_color)
+                            drawn += 1
+                    return drawn
+                
+                pixels_drawn += draw_circle_points(center_x, center_y, x, y)
+                
+                while x < y:
+                    if d < 0:
+                        d += 2 * x + 3
+                    else:
+                        d += 2 * (x - y) + 5
+                        y -= 1
+                    x += 1
+                    pixels_drawn += draw_circle_points(center_x, center_y, x, y)
+                    
+            self.pixels_drawn += pixels_drawn
+            self.draw_calls += 1
+            self.render_time += time.time() - start_time
+            return True
+            
+        except Exception as e:
+            print(f"Error drawing circle at ({center_x}, {center_y}) with radius {radius}: {e}")
+            return False
+            
+    def draw_image(self, framebuffer_id: str, x: int, y: int, texture_id: str,
+                   scale_x: float = 1.0, scale_y: float = 1.0) -> bool:
+        """Draw an image/texture onto the framebuffer."""
+        if not self.vram:
+            return False
+            
+        start_time = time.time()
+        
+        framebuffer = self.vram.get_framebuffer(framebuffer_id)
+        texture = self.vram.get_texture(texture_id)
+        
+        if not framebuffer or texture is None:
+            return False
+            
+        try:
+            # Get texture dimensions
+            if len(texture.shape) == 3:
+                tex_height, tex_width, tex_channels = texture.shape
+            else:
+                tex_height, tex_width = texture.shape
+                tex_channels = 1
+                
+            # Calculate scaled dimensions
+            scaled_width = int(tex_width * scale_x)
+            scaled_height = int(tex_height * scale_y)
+            
+            pixels_drawn = 0
+            
+            # Simple nearest-neighbor scaling and blitting
+            for dy in range(scaled_height):
+                for dx in range(scaled_width):
+                    # Calculate destination pixel
+                    dest_x = x + dx
+                    dest_y = y + dy
+                    
+                    # Check bounds
+                    if (dest_x < 0 or dest_x >= framebuffer.width or 
+                        dest_y < 0 or dest_y >= framebuffer.height):
+                        continue
+                        
+                    # Calculate source pixel (nearest neighbor)
+                    src_x = int(dx / scale_x)
+                    src_y = int(dy / scale_y)
+                    
+                    # Clamp source coordinates
+                    src_x = min(src_x, tex_width - 1)
+                    src_y = min(src_y, tex_height - 1)
+                    
+                    # Get source pixel color
+                    if tex_channels == 1:
+                        color = (texture[src_y, src_x], texture[src_y, src_x], texture[src_y, src_x])
+                    else:
+                        color = tuple(texture[src_y, src_x, :min(3, tex_channels)])
+                        
+                    # Apply shader if available
+                    final_color = color
+                    if self.current_shader:
+                        final_color = self.current_shader.process_pixel(dest_x, dest_y, color)
+                        
+                    # Set pixel
+                    framebuffer.set_pixel(dest_x, dest_y, final_color)
+                    pixels_drawn += 1
+                    
+            self.pixels_drawn += pixels_drawn
+            self.draw_calls += 1
+            self.render_time += time.time() - start_time
+            return True
+            
+        except Exception as e:
+            print(f"Error drawing image {texture_id} at ({x}, {y}): {e}")
+            return False
+            
+    def get_stats(self) -> Dict[str, Any]:
+        """Get rendering statistics."""
+        return {
+            "pixels_drawn": self.pixels_drawn,
+            "draw_calls": self.draw_calls,
+            "total_render_time": self.render_time,
+            "avg_render_time": self.render_time / max(1, self.draw_calls),
+            "pixels_per_second": self.pixels_drawn / max(0.001, self.render_time)
+        }
+        
+    def reset_stats(self) -> None:
+        """Reset rendering statistics."""
+        self.pixels_drawn = 0
+        self.draw_calls = 0
+        self.render_time = 0.0
+
+
+if __name__ == "__main__":
+    # Test the renderer
+    from vram import VRAM
+    
+    # Create VRAM and renderer
+    vram = VRAM(memory_size_gb=1)
+    renderer = Renderer(vram)
+    
+    # Create a test framebuffer
+    fb_id = vram.create_framebuffer(800, 600, 3)
+    
+    # Test rendering operations
+    print("Testing renderer...")
+    
+    # Clear screen
+    renderer.clear(fb_id, (64, 128, 255))
+    
+    # Draw some rectangles
+    renderer.draw_rect(fb_id, 100, 100, 200, 150, (255, 0, 0))
+    renderer.draw_rect(fb_id, 200, 200, 100, 100, (0, 255, 0))
+    
+    # Draw some lines
+    renderer.draw_line(fb_id, 0, 0, 799, 599, (255, 255, 255))
+    renderer.draw_line(fb_id, 799, 0, 0, 599, (255, 255, 255))
+    
+    # Draw a circle
+    renderer.draw_circle(fb_id, 400, 300, 50, (255, 255, 0), filled=True)
+    
+    # Draw some pixels
+    for i in range(100):
+        renderer.draw_pixel(fb_id, 50 + i, 50, (255, 0, 255))
+        
+    # Print statistics
+    stats = renderer.get_stats()
+    print(f"Renderer stats: {stats}")
+    
+    # Get framebuffer and check a pixel
+    fb = vram.get_framebuffer(fb_id)
+    if fb:
+        pixel = fb.get_pixel(100, 100)
+        print(f"Pixel at (100, 100): {pixel}")
+        
+    print("Renderer test completed!")
+

src/vgpu.py

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

src/vram.py

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