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virtual_gpu_setup/virtual_gpu/README.md

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+# Virtual GPU (vGPU) Project
+
+This project aims to build a fully functional software-defined GPU (vGPU) using Python, without relying on any physical GPU hardware or existing low-level graphics APIs (like CUDA, Metal, Vulkan, or OpenGL). The vGPU is designed to simulate the behavior of a real GPU, including its core components, memory hierarchy, and parallel processing capabilities.
+
+## Project Goals
+
+*   **Software-Defined Hardware**: Replace traditional GPU hardware components with pure software abstractions.
+*   **Massive Parallelism Simulation**: Simulate 50,000 processing cores and 800 Streaming Multiprocessors (SMs).
+*   **High-Bandwidth Memory Abstraction**: Implement a 500GB GDDR7 memory abstraction using symbolic memory management.
+*   **Graphical and AI Processing**: Capable of processing graphical logic, AI matrix operations, and rendering output.
+*   **Modular Architecture**: Designed with distinct modules for clear separation of concerns and extensibility.
+
+## Modules Overview
+
+This project is structured into several key modules, each responsible for a specific aspect of the vGPU's functionality:
+
+*   `vgpu.py`: The core GPU processor, managing overall state, workload distribution, and the main GPU tick cycle.
+*   `vram.py`: The video memory module, abstracting 500GB of GDDR7 memory using symbolic representation and efficient data handling.
+*   `driver.py`: The CPU-to-GPU command interpreter, responsible for receiving and queuing commands from a virtual CPU.
+*   `render.py`: The pixel renderer, implementing the software raster pipeline for drawing primitives and images.
+*   `ai.py`: The simulated AI accelerator, handling matrix and vector operations using the vGPU's simulated parallelism.
+*   `shader.py`: Provides a mechanism for simulating programmable shader logic.
+*   `display.py`: The output system, handling the presentation of rendered frames to a display (e.g., WebSocket to JS canvas, GUI window, or image files).
+*   `bus.py`: Simulates memory movement and data transfer logic between different logical components.
+
+## Getting Started
+
+Further instructions on setting up the environment, running examples, and contributing will be provided as the project develops.
+
+

virtual_gpu_setup/virtual_gpu/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!")
+

virtual_gpu_setup/virtual_gpu/bus.py

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+"""
+Bus Module - Data Transfer Logic
+
+This module simulates memory movement and data transfer logic between
+different logical components (SSD, RAM, VRAM) with bandwidth simulation.
+"""
+
+import asyncio
+import time
+import numpy as np
+from typing import Dict, Any, Optional, Tuple, List
+from enum import Enum
+from dataclasses import dataclass
+
+
+class BusType(Enum):
+    """Types of data buses in the system."""
+    SYSTEM_RAM = "system_ram"
+    VRAM_BUS = "vram_bus"
+    STORAGE_BUS = "storage_bus"
+    PCIE = "pcie"
+    MEMORY_CONTROLLER = "memory_controller"
+
+
+@dataclass
+class BusSpecification:
+    """Specifications for a data bus."""
+    name: str
+    bandwidth_gbps: float  # Gigabytes per second
+    latency_ms: float      # Milliseconds
+    max_concurrent_transfers: int
+    bus_width_bits: int
+
+
+@dataclass
+class TransferRequest:
+    """Represents a data transfer request."""
+    transfer_id: str
+    transfer_type: TransferType
+    source_address: int
+    destination_address: int
+    size_bytes: int
+    priority: int = 0
+    created_time: float = 0.0
+    start_time: float = 0.0
+    end_time: float = 0.0
+    status: str = "pending"  # pending, in_progress, completed, failed
+
+
+class DataBus:
+    """Represents a single data bus with bandwidth and latency simulation."""
+    
+    def __init__(self, spec: BusSpecification):
+        self.spec = spec
+        self.active_transfers: List[TransferRequest] = []
+        self.completed_transfers: List[TransferRequest] = []
+        self.transfer_queue = asyncio.Queue()
+        
+        # Statistics
+        self.total_bytes_transferred = 0
+        self.total_transfer_time = 0.0
+        self.transfer_count = 0
+        
+    async def submit_transfer(self, request: TransferRequest) -> str:
+        """Submit a transfer request to the bus."""
+        request.created_time = time.time()
+        await self.transfer_queue.put(request)
+        return request.transfer_id
+        
+    async def process_transfers(self):
+        """Process transfer requests with bandwidth and latency simulation."""
+        while True:
+            try:
+                # Wait for a transfer request
+                request = await self.transfer_queue.get()
+                
+                # Check if we can start this transfer (concurrent limit)
+                if len(self.active_transfers) >= self.spec.max_concurrent_transfers:
+                    # Put it back and wait
+                    await self.transfer_queue.put(request)
+                    await asyncio.sleep(0.001)  # Small delay
+                    continue
+                    
+                # Start the transfer
+                await self._execute_transfer(request)
+                
+            except asyncio.CancelledError:
+                break
+            except Exception as e:
+                print(f"Error processing transfer on bus {self.spec.name}: {e}")
+                
+    async def _execute_transfer(self, request: TransferRequest):
+        """Execute a single transfer with realistic timing."""
+        request.status = "in_progress"
+        request.start_time = time.time()
+        self.active_transfers.append(request)
+        
+        try:
+            # Calculate transfer time based on bandwidth
+            transfer_time_seconds = request.size_bytes / (self.spec.bandwidth_gbps * 1e9)
+            
+            # Add latency
+            total_time = transfer_time_seconds + (self.spec.latency_ms / 1000.0)
+            
+            # Simulate the transfer delay
+            await asyncio.sleep(total_time)
+            
+            # Complete the transfer
+            request.status = "completed"
+            request.end_time = time.time()
+            
+            # Update statistics
+            self.total_bytes_transferred += request.size_bytes
+            self.total_transfer_time += total_time
+            self.transfer_count += 1
+            
+            print(f"Transfer {request.transfer_id} completed: "
+                  f"{request.size_bytes:,} bytes in {total_time:.4f}s "
+                  f"({request.size_bytes / (1024**2) / total_time:.2f} MB/s)")
+            
+        except Exception as e:
+            request.status = "failed"
+            print(f"Transfer {request.transfer_id} failed: {e}")
+            
+        finally:
+            # Remove from active transfers
+            if request in self.active_transfers:
+                self.active_transfers.remove(request)
+            self.completed_transfers.append(request)
+            
+    def get_utilization(self) -> float:
+        """Get current bus utilization (0.0 to 1.0)."""
+        return len(self.active_transfers) / max(1, self.spec.max_concurrent_transfers)
+        
+    def get_stats(self) -> Dict[str, Any]:
+        """Get bus statistics."""
+        avg_transfer_time = self.total_transfer_time / max(1, self.transfer_count)
+        effective_bandwidth = (self.total_bytes_transferred / (1024**3)) / max(0.001, self.total_transfer_time)
+        
+        return {
+            "bus_name": self.spec.name,
+            "bandwidth_gbps": self.spec.bandwidth_gbps,
+            "latency_ms": self.spec.latency_ms,
+            "total_transfers": self.transfer_count,
+            "total_bytes_transferred": self.total_bytes_transferred,
+            "total_transfer_time": self.total_transfer_time,
+            "avg_transfer_time": avg_transfer_time,
+            "effective_bandwidth_gbps": effective_bandwidth,
+            "current_utilization": self.get_utilization(),
+            "active_transfers": len(self.active_transfers),
+            "queued_transfers": self.transfer_queue.qsize()
+        }
+
+
+class BusManager:
+    """Manages multiple data buses and coordinates transfers between components."""
+    
+    def __init__(self):
+        self.buses: Dict[str, DataBus] = {}
+        self.transfer_counter = 0
+        self.running = False
+        
+        # Initialize standard buses
+        self._initialize_standard_buses()
+        
+    def _initialize_standard_buses(self):
+        """Initialize standard system buses with realistic specifications."""
+        
+        # GDDR7 VRAM Bus (500GB capacity, high bandwidth)
+        gddr7_spec = BusSpecification(
+            name="GDDR7_VRAM",
+            bandwidth_gbps=128.0,  # 128 GB/s (realistic for GDDR7)
+            latency_ms=0.1,        # Very low latency
+            max_concurrent_transfers=16,
+            bus_width_bits=512
+        )
+        self.add_bus("vram", gddr7_spec)
+        
+        # PCIe 5.0 Bus (for GPU-CPU communication)
+        pcie_spec = BusSpecification(
+            name="PCIe_5.0_x16",
+            bandwidth_gbps=64.0,   # 64 GB/s for PCIe 5.0 x16
+            latency_ms=0.5,        # Higher latency than VRAM
+            max_concurrent_transfers=8,
+            bus_width_bits=256
+        )
+        self.add_bus("pcie", pcie_spec)
+        
+        # System RAM Bus (DDR5)
+        ddr5_spec = BusSpecification(
+            name="DDR5_System_RAM",
+            bandwidth_gbps=51.2,   # 51.2 GB/s for DDR5-6400
+            latency_ms=0.2,
+            max_concurrent_transfers=4,
+            bus_width_bits=128
+        )
+        self.add_bus("system_ram", ddr5_spec)
+        
+        # NVMe SSD Bus
+        nvme_spec = BusSpecification(
+            name="NVMe_SSD",
+            bandwidth_gbps=7.0,    # 7 GB/s for high-end NVMe
+            latency_ms=0.1,
+            max_concurrent_transfers=32,
+            bus_width_bits=64
+        )
+        self.add_bus("storage", nvme_spec)
+        
+    def add_bus(self, bus_id: str, spec: BusSpecification):
+        """Add a new bus to the system."""
+        self.buses[bus_id] = DataBus(spec)
+        
+    async def start(self):
+        """Start all bus processing tasks."""
+        if self.running:
+            return
+            
+        self.running = True
+        
+        # Start processing tasks for all buses
+        self.bus_tasks = []
+        for bus in self.buses.values():
+            task = asyncio.create_task(bus.process_transfers())
+            self.bus_tasks.append(task)
+            
+        print(f"Bus manager started with {len(self.buses)} buses")
+        
+    async def stop(self):
+        """Stop all bus processing tasks."""
+        if not self.running:
+            return
+            
+        self.running = False
+        
+        # Cancel all bus tasks
+        for task in self.bus_tasks:
+            task.cancel()
+            
+        await asyncio.gather(*self.bus_tasks, return_exceptions=True)
+        print("Bus manager stopped")
+        
+    async def transfer_data(self, bus_id: str, transfer_type: TransferType,
+                           source_address: int, destination_address: int,
+                           size_bytes: int, priority: int = 0) -> str:
+        """Initiate a data transfer on the specified bus."""
+        if bus_id not in self.buses:
+            raise ValueError(f"Bus {bus_id} not found")
+            
+        transfer_id = f"transfer_{self.transfer_counter}"
+        self.transfer_counter += 1
+        
+        request = TransferRequest(
+            transfer_id=transfer_id,
+            transfer_type=transfer_type,
+            source_address=source_address,
+            destination_address=destination_address,
+            size_bytes=size_bytes,
+            priority=priority
+        )
+        
+        bus = self.buses[bus_id]
+        await bus.submit_transfer(request)
+        
+        return transfer_id
+        
+    async def copy_to_vram(self, source_address: int, vram_address: int, 
+                          size_bytes: int) -> str:
+        """Copy data from system memory to VRAM."""
+        return await self.transfer_data(
+            "vram", TransferType.WRITE, source_address, vram_address, size_bytes
+        )
+        
+    async def copy_from_vram(self, vram_address: int, destination_address: int,
+                            size_bytes: int) -> str:
+        """Copy data from VRAM to system memory."""
+        return await self.transfer_data(
+            "vram", TransferType.READ, vram_address, destination_address, size_bytes
+        )
+        
+    async def load_from_storage(self, storage_address: int, ram_address: int,
+                               size_bytes: int) -> str:
+        """Load data from storage to system RAM."""
+        return await self.transfer_data(
+            "storage", TransferType.READ, storage_address, ram_address, size_bytes
+        )
+        
+    async def save_to_storage(self, ram_address: int, storage_address: int,
+                             size_bytes: int) -> str:
+        """Save data from system RAM to storage."""
+        return await self.transfer_data(
+            "storage", TransferType.WRITE, ram_address, storage_address, size_bytes
+        )
+        
+    def get_bus_stats(self, bus_id: str) -> Optional[Dict[str, Any]]:
+        """Get statistics for a specific bus."""
+        if bus_id in self.buses:
+            return self.buses[bus_id].get_stats()
+        return None
+        
+    def get_all_stats(self) -> Dict[str, Any]:
+        """Get statistics for all buses."""
+        stats = {
+            "total_buses": len(self.buses),
+            "running": self.running,
+            "buses": {}
+        }
+        
+        total_bandwidth = 0
+        total_utilization = 0
+        
+        for bus_id, bus in self.buses.items():
+            bus_stats = bus.get_stats()
+            stats["buses"][bus_id] = bus_stats
+            total_bandwidth += bus_stats["bandwidth_gbps"]
+            total_utilization += bus_stats["current_utilization"]
+            
+        stats["total_bandwidth_gbps"] = total_bandwidth
+        stats["avg_utilization"] = total_utilization / len(self.buses) if self.buses else 0
+        
+        return stats
+        
+    async def benchmark_bus(self, bus_id: str, test_size_mb: int = 100) -> Dict[str, Any]:
+        """Benchmark a specific bus with test transfers."""
+        if bus_id not in self.buses:
+            raise ValueError(f"Bus {bus_id} not found")
+            
+        print(f"Benchmarking bus {bus_id} with {test_size_mb} MB transfers...")
+        
+        test_size_bytes = test_size_mb * 1024 * 1024
+        num_tests = 10
+        
+        start_time = time.time()
+        transfer_ids = []
+        
+        # Submit multiple test transfers
+        for i in range(num_tests):
+            transfer_id = await self.transfer_data(
+                bus_id, TransferType.COPY, 
+                i * test_size_bytes, (i + 1000) * test_size_bytes, 
+                test_size_bytes
+            )
+            transfer_ids.append(transfer_id)
+            
+        # Wait for all transfers to complete
+        bus = self.buses[bus_id]
+        while len(bus.active_transfers) > 0 or bus.transfer_queue.qsize() > 0:
+            await asyncio.sleep(0.1)
+            
+        end_time = time.time()
+        total_time = end_time - start_time
+        total_data_gb = (test_size_bytes * num_tests) / (1024**3)
+        effective_bandwidth = total_data_gb / total_time
+        
+        return {
+            "bus_id": bus_id,
+            "test_size_mb": test_size_mb,
+            "num_transfers": num_tests,
+            "total_time_seconds": total_time,
+            "total_data_gb": total_data_gb,
+            "effective_bandwidth_gbps": effective_bandwidth,
+            "theoretical_bandwidth_gbps": bus.spec.bandwidth_gbps,
+            "efficiency_percent": (effective_bandwidth / bus.spec.bandwidth_gbps) * 100
+        }
+
+
+if __name__ == "__main__":
+    # Test the bus system
+    async def test_bus_system():
+        print("Testing Bus System...")
+        
+        # Create bus manager
+        bus_manager = BusManager()
+        await bus_manager.start()
+        
+        # Test individual transfers
+        print("\nTesting individual transfers...")
+        
+        # Test VRAM transfer (large texture upload)
+        texture_size = 64 * 1024 * 1024  # 64 MB texture
+        vram_transfer = await bus_manager.copy_to_vram(0x1000, 0x10000000, texture_size)
+        print(f"Submitted VRAM transfer: {vram_transfer}")
+        
+        # Test storage transfer (loading assets)
+        asset_size = 128 * 1024 * 1024  # 128 MB asset
+        storage_transfer = await bus_manager.load_from_storage(0x0, 0x2000, asset_size)
+        print(f"Submitted storage transfer: {storage_transfer}")
+        
+        # Test PCIe transfer (CPU-GPU communication)
+        command_size = 4 * 1024  # 4 KB command buffer
+        pcie_transfer = await bus_manager.transfer_data(
+            "pcie", TransferType.WRITE, 0x3000, 0x20000000, command_size
+        )
+        print(f"Submitted PCIe transfer: {pcie_transfer}")
+        
+        # Wait for transfers to complete
+        print("\nWaiting for transfers to complete...")
+        await asyncio.sleep(2.0)
+        
+        # Print statistics
+        print("\nBus Statistics:")
+        all_stats = bus_manager.get_all_stats()
+        for bus_id, bus_stats in all_stats["buses"].items():
+            print(f"\n{bus_id}:")
+            print(f"  Bandwidth: {bus_stats["bandwidth_gbps"]:.1f} GB/s")
+            print(f"  Transfers: {bus_stats["total_transfers"]}")
+            print(f"  Data transferred: {bus_stats["total_bytes_transferred"] / (1024**2):.1f} MB")
+            print(f"  Effective bandwidth: {bus_stats["effective_bandwidth_gbps"]:.2f} GB/s")
+            print(f"  Utilization: {bus_stats["current_utilization"]:.1%}")
+            
+        # Benchmark each bus
+        print("\nBenchmarking buses...")
+        for bus_id in ["vram", "pcie", "system_ram", "storage"]:
+            try:
+                benchmark_result = await bus_manager.benchmark_bus(bus_id, test_size_mb=50)
+                print(f"\n{bus_id} benchmark:")
+                print(f"  Effective bandwidth: {benchmark_result["effective_bandwidth_gbps"]:.2f} GB/s")
+                print(f"  Theoretical bandwidth: {benchmark_result["theoretical_bandwidth_gbps"]:.2f} GB/s")
+                print(f"  Efficiency: {benchmark_result["efficiency_percent"]:.1f}%")
+            except Exception as e:
+                print(f"Benchmark failed for {bus_id}: {e}")
+                
+        # Stop bus manager
+        await bus_manager.stop()
+        print("\nBus system test completed!")
+        
+    # Run the test
+    asyncio.run(test_bus_system())
+

virtual_gpu_setup/virtual_gpu/display.py

@@ -0,0 +1,501 @@
+"""
+Display Module - Output System
+
+This module handles the final output of rendered frames, supporting multiple
+output methods including WebSocket to browser, GUI windows, and image files.
+"""
+
+import asyncio
+import json
+import base64
+import time
+import numpy as np
+from typing import Optional, Dict, Any, Callable
+from io import BytesIO
+import threading
+
+try:
+    import websockets
+    WEBSOCKETS_AVAILABLE = True
+except ImportError:
+    WEBSOCKETS_AVAILABLE = False
+    print("Warning: websockets not available. WebSocket display will not work.")
+
+try:
+    import tkinter as tk
+    from tkinter import Canvas
+    from PIL import Image, ImageTk
+    TKINTER_AVAILABLE = True
+except ImportError:
+    TKINTER_AVAILABLE = False
+    print("Warning: tkinter or PIL not available. GUI display will not work.")
+
+try:
+    from PIL import Image
+    PIL_AVAILABLE = True
+except ImportError:
+    PIL_AVAILABLE = False
+    print("Warning: PIL not available. Image saving will not work.")
+
+
+class DisplayMode:
+    """Enumeration of display modes."""
+    WEBSOCKET = "websocket"
+    GUI = "gui"
+    FILE = "file"
+    CONSOLE = "console"
+
+
+class WebSocketDisplay:
+    """WebSocket-based display that sends frames to a web browser."""
+    
+    def __init__(self, host: str = "localhost", port: int = 8765):
+        self.host = host
+        self.port = port
+        self.server = None
+        self.clients = set()
+        self.is_running = False
+        
+    async def start_server(self):
+        """Start the WebSocket server."""
+        if not WEBSOCKETS_AVAILABLE:
+            raise RuntimeError("WebSocket support not available")
+            
+        async def handle_client(websocket, path):
+            self.clients.add(websocket)
+            print(f"Client connected: {websocket.remote_address}")
+            try:
+                await websocket.wait_closed()
+            finally:
+                self.clients.remove(websocket)
+                print(f"Client disconnected: {websocket.remote_address}")
+                
+        self.server = await websockets.serve(handle_client, self.host, self.port)
+        self.is_running = True
+        print(f"WebSocket server started on ws://{self.host}:{self.port}")
+        
+    async def stop_server(self):
+        """Stop the WebSocket server."""
+        if self.server:
+            self.server.close()
+            await self.server.wait_closed()
+            self.is_running = False
+            print("WebSocket server stopped")
+            
+    async def send_frame(self, frame_data: np.ndarray, frame_id: int = 0):
+        """Send a frame to all connected clients."""
+        if not self.clients or not PIL_AVAILABLE:
+            return
+            
+        try:
+            # Convert numpy array to PIL Image
+            if len(frame_data.shape) == 3:
+                height, width, channels = frame_data.shape
+                if channels == 3:
+                    image = Image.fromarray(frame_data.astype(np.uint8), 'RGB')
+                elif channels == 4:
+                    image = Image.fromarray(frame_data.astype(np.uint8), 'RGBA')
+                else:
+                    # Convert single channel to RGB
+                    rgb_data = np.stack([frame_data[:,:,0]] * 3, axis=-1)
+                    image = Image.fromarray(rgb_data.astype(np.uint8), 'RGB')
+            else:
+                # Grayscale
+                image = Image.fromarray(frame_data.astype(np.uint8), 'L')
+                
+            # Convert to base64
+            buffer = BytesIO()
+            image.save(buffer, format='PNG')
+            img_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
+            
+            # Create message
+            message = {
+                "type": "frame",
+                "frame_id": frame_id,
+                "width": image.width,
+                "height": image.height,
+                "data": f"data:image/png;base64,{img_base64}",
+                "timestamp": time.time()
+            }
+            
+            # Send to all clients
+            if self.clients:
+                await asyncio.gather(
+                    *[client.send(json.dumps(message)) for client in self.clients],
+                    return_exceptions=True
+                )
+                
+        except Exception as e:
+            print(f"Error sending frame via WebSocket: {e}")
+
+
+class GUIDisplay:
+    """Tkinter-based GUI display window."""
+    
+    def __init__(self, title: str = "vGPU Display", width: int = 800, height: int = 600):
+        if not TKINTER_AVAILABLE:
+            raise RuntimeError("GUI display not available (tkinter/PIL missing)")
+            
+        self.title = title
+        self.width = width
+        self.height = height
+        self.window = None
+        self.canvas = None
+        self.is_running = False
+        self.update_callback = None
+        
+    def start(self):
+        """Start the GUI display in a separate thread."""
+        if self.is_running:
+            return
+            
+        def run_gui():
+            self.window = tk.Tk()
+            self.window.title(self.title)
+            self.window.geometry(f"{self.width}x{self.height}")
+            
+            self.canvas = Canvas(self.window, width=self.width, height=self.height, bg='black')
+            self.canvas.pack()
+            
+            self.is_running = True
+            
+            # Set up periodic update
+            def update():
+                if self.update_callback:
+                    self.update_callback()
+                if self.is_running:
+                    self.window.after(16, update)  # ~60 FPS
+                    
+            update()
+            
+            self.window.protocol("WM_DELETE_WINDOW", self.stop)
+            self.window.mainloop()
+            
+        self.gui_thread = threading.Thread(target=run_gui, daemon=True)
+        self.gui_thread.start()
+        
+    def stop(self):
+        """Stop the GUI display."""
+        self.is_running = False
+        if self.window:
+            self.window.quit()
+            
+    def show_frame(self, frame_data: np.ndarray):
+        """Display a frame in the GUI window."""
+        if not self.is_running or not self.canvas:
+            return
+            
+        try:
+            # Convert numpy array to PIL Image
+            if len(frame_data.shape) == 3:
+                height, width, channels = frame_data.shape
+                if channels >= 3:
+                    image = Image.fromarray(frame_data[:,:,:3].astype(np.uint8), 'RGB')
+                else:
+                    # Convert single channel to RGB
+                    rgb_data = np.stack([frame_data[:,:,0]] * 3, axis=-1)
+                    image = Image.fromarray(rgb_data.astype(np.uint8), 'RGB')
+            else:
+                # Grayscale
+                image = Image.fromarray(frame_data.astype(np.uint8), 'L')
+                
+            # Resize to fit canvas
+            image = image.resize((self.width, self.height), Image.Resampling.LANCZOS)
+            
+            # Convert to PhotoImage
+            photo = ImageTk.PhotoImage(image)
+            
+            # Update canvas
+            self.canvas.delete("all")
+            self.canvas.create_image(self.width//2, self.height//2, image=photo)
+            
+            # Keep a reference to prevent garbage collection
+            self.canvas.image = photo
+            
+        except Exception as e:
+            print(f"Error displaying frame in GUI: {e}")
+            
+    def set_update_callback(self, callback: Callable):
+        """Set a callback function to be called periodically."""
+        self.update_callback = callback
+
+
+class FileDisplay:
+    """File-based display that saves frames as image files."""
+    
+    def __init__(self, output_dir: str = "./frames", format: str = "png"):
+        self.output_dir = output_dir
+        self.format = format.lower()
+        self.frame_counter = 0
+        
+        # Create output directory
+        import os
+        os.makedirs(output_dir, exist_ok=True)
+        
+    def save_frame(self, frame_data: np.ndarray, filename: Optional[str] = None):
+        """Save a frame to a file."""
+        if not PIL_AVAILABLE:
+            print("Error: PIL not available for saving images")
+            return False
+            
+        try:
+            if filename is None:
+                filename = f"frame_{self.frame_counter:06d}.{self.format}"
+                self.frame_counter += 1
+                
+            filepath = f"{self.output_dir}/{filename}"
+            
+            # Convert numpy array to PIL Image
+            if len(frame_data.shape) == 3:
+                height, width, channels = frame_data.shape
+                if channels == 3:
+                    image = Image.fromarray(frame_data.astype(np.uint8), 'RGB')
+                elif channels == 4:
+                    image = Image.fromarray(frame_data.astype(np.uint8), 'RGBA')
+                else:
+                    # Convert single channel to RGB
+                    rgb_data = np.stack([frame_data[:,:,0]] * 3, axis=-1)
+                    image = Image.fromarray(rgb_data.astype(np.uint8), 'RGB')
+            else:
+                # Grayscale
+                image = Image.fromarray(frame_data.astype(np.uint8), 'L')
+                
+            # Save image
+            image.save(filepath)
+            print(f"Frame saved: {filepath}")
+            return True
+            
+        except Exception as e:
+            print(f"Error saving frame: {e}")
+            return False
+
+
+class ConsoleDisplay:
+    """Console-based display that shows ASCII art representation."""
+    
+    def __init__(self, width: int = 80, height: int = 24):
+        self.width = width
+        self.height = height
+        self.ascii_chars = " .:-=+*#%@"
+        
+    def show_frame(self, frame_data: np.ndarray):
+        """Display frame as ASCII art in console."""
+        try:
+            # Convert to grayscale if needed
+            if len(frame_data.shape) == 3:
+                # Convert RGB to grayscale
+                gray = np.dot(frame_data[...,:3], [0.299, 0.587, 0.114])
+            else:
+                gray = frame_data
+                
+            # Resize to console dimensions
+            from scipy import ndimage
+            resized = ndimage.zoom(gray, (self.height / gray.shape[0], self.width / gray.shape[1]))
+            
+            # Convert to ASCII
+            ascii_frame = []
+            for row in resized:
+                ascii_row = ""
+                for pixel in row:
+                    # Map pixel value to ASCII character
+                    char_index = int((pixel / 255.0) * (len(self.ascii_chars) - 1))
+                    ascii_row += self.ascii_chars[char_index]
+                ascii_frame.append(ascii_row)
+                
+            # Clear screen and display
+            print("\033[2J\033[H")  # Clear screen and move cursor to top
+            for row in ascii_frame:
+                print(row)
+                
+        except Exception as e:
+            print(f"Error displaying ASCII frame: {e}")
+
+
+class DisplayManager:
+    """Manages multiple display outputs and coordinates frame updates."""
+    
+    def __init__(self, vram=None):
+        self.vram = vram
+        self.displays = {}
+        self.active_framebuffer = None
+        self.frame_counter = 0
+        self.fps_target = 60
+        self.last_frame_time = 0
+        
+        # Statistics
+        self.frames_displayed = 0
+        self.total_display_time = 0.0
+        
+    def add_display(self, name: str, display_type: str, **kwargs):
+        """Add a display output."""
+        if display_type == DisplayMode.WEBSOCKET:
+            display = WebSocketDisplay(**kwargs)
+        elif display_type == DisplayMode.GUI:
+            display = GUIDisplay(**kwargs)
+        elif display_type == DisplayMode.FILE:
+            display = FileDisplay(**kwargs)
+        elif display_type == DisplayMode.CONSOLE:
+            display = ConsoleDisplay(**kwargs)
+        else:
+            raise ValueError(f"Unknown display type: {display_type}")
+            
+        self.displays[name] = {
+            "display": display,
+            "type": display_type,
+            "enabled": True
+        }
+        
+        return display
+        
+    def remove_display(self, name: str):
+        """Remove a display output."""
+        if name in self.displays:
+            display_info = self.displays[name]
+            if display_info["type"] == DisplayMode.WEBSOCKET:
+                asyncio.create_task(display_info["display"].stop_server())
+            elif display_info["type"] == DisplayMode.GUI:
+                display_info["display"].stop()
+            del self.displays[name]
+            
+    def set_active_framebuffer(self, framebuffer_id: str):
+        """Set the active framebuffer to display."""
+        self.active_framebuffer = framebuffer_id
+        
+    async def update_displays(self):
+        """Update all active displays with the current framebuffer."""
+        if not self.vram or not self.active_framebuffer:
+            return
+            
+        start_time = time.time()
+        
+        # Get framebuffer data
+        framebuffer = self.vram.get_framebuffer(self.active_framebuffer)
+        if not framebuffer:
+            return
+            
+        frame_data = framebuffer.pixel_buffer
+        
+        # Update each display
+        for name, display_info in self.displays.items():
+            if not display_info["enabled"]:
+                continue
+                
+            display = display_info["display"]
+            display_type = display_info["type"]
+            
+            try:
+                if display_type == DisplayMode.WEBSOCKET:
+                    await display.send_frame(frame_data, self.frame_counter)
+                elif display_type == DisplayMode.GUI:
+                    display.show_frame(frame_data)
+                elif display_type == DisplayMode.FILE:
+                    display.save_frame(frame_data)
+                elif display_type == DisplayMode.CONSOLE:
+                    display.show_frame(frame_data)
+                    
+            except Exception as e:
+                print(f"Error updating display {name}: {e}")
+                
+        # Update statistics
+        self.frame_counter += 1
+        self.frames_displayed += 1
+        self.total_display_time += time.time() - start_time
+        self.last_frame_time = time.time()
+        
+    def enable_display(self, name: str, enabled: bool = True):
+        """Enable or disable a specific display."""
+        if name in self.displays:
+            self.displays[name]["enabled"] = enabled
+            
+    def get_stats(self) -> Dict[str, Any]:
+        """Get display manager statistics."""
+        avg_display_time = self.total_display_time / max(1, self.frames_displayed)
+        current_fps = 1.0 / max(0.001, time.time() - self.last_frame_time) if self.last_frame_time > 0 else 0
+        
+        return {
+            "frames_displayed": self.frames_displayed,
+            "total_display_time": self.total_display_time,
+            "avg_display_time": avg_display_time,
+            "current_fps": current_fps,
+            "target_fps": self.fps_target,
+            "active_displays": len([d for d in self.displays.values() if d["enabled"]]),
+            "total_displays": len(self.displays),
+            "active_framebuffer": self.active_framebuffer
+        }
+
+
+if __name__ == "__main__":
+    # Test the display system
+    async def test_display():
+        from vram import VRAM
+        from render import Renderer
+        
+        print("Testing Display System...")
+        
+        # Create VRAM and renderer
+        vram = VRAM(memory_size_gb=1)
+        renderer = Renderer(vram)
+        
+        # Create display manager
+        display_manager = DisplayManager(vram)
+        
+        # Create a test framebuffer
+        fb_id = vram.create_framebuffer(400, 300, 3)
+        display_manager.set_active_framebuffer(fb_id)
+        
+        # Add displays
+        if WEBSOCKETS_AVAILABLE:
+            ws_display = display_manager.add_display("websocket", DisplayMode.WEBSOCKET)
+            await ws_display.start_server()
+            
+        if TKINTER_AVAILABLE:
+            gui_display = display_manager.add_display("gui", DisplayMode.GUI, width=400, height=300)
+            gui_display.start()
+            
+        file_display = display_manager.add_display("file", DisplayMode.FILE, output_dir="./test_frames")
+        console_display = display_manager.add_display("console", DisplayMode.CONSOLE, width=40, height=20)
+        
+        # Render some test content
+        renderer.clear(fb_id, (64, 128, 255))
+        renderer.draw_rect(fb_id, 50, 50, 100, 80, (255, 0, 0))
+        renderer.draw_circle(fb_id, 200, 150, 40, (0, 255, 0), filled=True)
+        
+        # Update displays
+        await display_manager.update_displays()
+        
+        # Animate for a few seconds
+        for i in range(60):  # 1 second at 60 FPS
+            # Clear and draw animated content
+            renderer.clear(fb_id, (32, 64, 128))
+            
+            # Moving rectangle
+            x = 50 + int(50 * np.sin(i * 0.1))
+            renderer.draw_rect(fb_id, x, 50, 50, 50, (255, 255, 0))
+            
+            # Rotating line effect
+            center_x, center_y = 200, 150
+            for j in range(8):
+                angle = (i + j * 8) * 0.1
+                end_x = center_x + int(40 * np.cos(angle))
+                end_y = center_y + int(40 * np.sin(angle))
+                renderer.draw_line(fb_id, center_x, center_y, end_x, end_y, (0, 255, 255))
+                
+            # Update displays
+            await display_manager.update_displays()
+            await asyncio.sleep(1/60)  # 60 FPS
+            
+        # Print statistics
+        stats = display_manager.get_stats()
+        print(f"Display Manager stats: {stats}")
+        
+        # Cleanup
+        if WEBSOCKETS_AVAILABLE:
+            await ws_display.stop_server()
+        if TKINTER_AVAILABLE:
+            gui_display.stop()
+            
+        print("Display system test completed!")
+        
+    # Run the test
+    asyncio.run(test_display())
+

virtual_gpu_setup/virtual_gpu/driver.py

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

virtual_gpu_setup/virtual_gpu/examples/test_basic_rendering.py

@@ -0,0 +1,245 @@
+"""
+Basic Rendering Test Example
+
+This example demonstrates basic rendering capabilities of the vGPU,
+including creating framebuffers, drawing primitives, and displaying output.
+"""
+
+import asyncio
+import sys
+import os
+
+# Add parent directory to path to import vGPU modules
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from vgpu import VirtualGPU, TaskType
+from vram import VRAM
+from driver import GPUDriver, CommandType
+from render import Renderer
+from ai import AIAccelerator
+from display import DisplayManager, DisplayMode
+from shader import ShaderManager
+import numpy as np
+
+
+async def basic_rendering_test():
+    """Test basic rendering functionality of the vGPU."""
+    print("Starting Basic Rendering Test...")
+    print("=" * 50)
+    
+    # Initialize components
+    print("Initializing vGPU components...")
+    
+    # Create VRAM (1GB for testing)
+    vram = VRAM(memory_size_gb=1)
+    print(f"✓ VRAM initialized: {vram.get_stats()['total_memory_gb']} GB")
+    
+    # Create renderer
+    renderer = Renderer(vram)
+    print("✓ Renderer initialized")
+    
+    # Create AI accelerator
+    ai_accelerator = AIAccelerator(vram)
+    print("✓ AI Accelerator initialized")
+    
+    # Create vGPU with 800 SMs and 50,000 cores
+    vgpu = VirtualGPU(num_sms=800, total_cores=50000)
+    vgpu.set_modules(vram, renderer, ai_accelerator, None)
+    print(f"✓ vGPU initialized: {vgpu.num_sms} SMs, {vgpu.total_cores} cores")
+    
+    # Create driver
+    driver = GPUDriver(vgpu)
+    vgpu.driver = driver
+    print("✓ GPU Driver initialized")
+    
+    # Create display manager
+    display_manager = DisplayManager(vram)
+    print("✓ Display Manager initialized")
+    
+    # Create shader manager
+    shader_manager = ShaderManager()
+    print("✓ Shader Manager initialized")
+    
+    print("\nComponent initialization complete!")
+    print("=" * 50)
+    
+    # Create framebuffer
+    print("\nCreating framebuffer...")
+    fb_id = driver.create_framebuffer(800, 600, 3, "main_framebuffer")
+    await driver.process_commands()
+    
+    framebuffer = vram.get_framebuffer("main_framebuffer")
+    if framebuffer:
+        print(f"✓ Framebuffer created: {framebuffer.width}x{framebuffer.height}")
+        display_manager.set_active_framebuffer("main_framebuffer")
+    else:
+        print("✗ Failed to create framebuffer")
+        return
+        
+    # Add file display for saving frames
+    file_display = display_manager.add_display("file", DisplayMode.FILE, 
+                                              output_dir="./test_output")
+    print("✓ File display added")
+    
+    # Test 1: Clear screen
+    print("\nTest 1: Clear screen")
+    driver.clear_screen((64, 128, 255))  # Blue background
+    await driver.process_commands()
+    await vgpu.tick()
+    await display_manager.update_displays()
+    print("✓ Screen cleared to blue")
+    
+    # Test 2: Draw rectangles
+    print("\nTest 2: Draw rectangles")
+    driver.draw_rectangle(100, 100, 200, 150, (255, 0, 0))  # Red rectangle
+    driver.draw_rectangle(300, 200, 150, 100, (0, 255, 0))  # Green rectangle
+    driver.draw_rectangle(500, 50, 100, 200, (255, 255, 0))  # Yellow rectangle
+    await driver.process_commands()
+    await vgpu.tick()
+    await display_manager.update_displays()
+    print("✓ Rectangles drawn")
+    
+    # Test 3: Draw with shader
+    print("\nTest 3: Apply shader effects")
+    
+    # Set grayscale shader
+    grayscale_shader = shader_manager.get_shader("grayscale")
+    renderer.set_shader(grayscale_shader)
+    
+    # Draw some shapes with shader
+    driver.draw_rectangle(50, 400, 100, 100, (255, 0, 255))  # Magenta (will be grayscale)
+    driver.draw_rectangle(200, 450, 80, 80, (0, 255, 255))   # Cyan (will be grayscale)
+    await driver.process_commands()
+    await vgpu.tick()
+    await display_manager.update_displays()
+    print("✓ Shapes drawn with grayscale shader")
+    
+    # Test 4: Remove shader and draw more
+    print("\nTest 4: Remove shader and draw more shapes")
+    renderer.set_shader(None)  # Remove shader
+    
+    # Draw circles using line approximation
+    center_x, center_y = 400, 400
+    radius = 50
+    for angle in range(0, 360, 10):
+        x1 = center_x + int(radius * np.cos(np.radians(angle)))
+        y1 = center_y + int(radius * np.sin(np.radians(angle)))
+        x2 = center_x + int(radius * np.cos(np.radians(angle + 10)))
+        y2 = center_y + int(radius * np.sin(np.radians(angle + 10)))
+        
+        # Draw line segment
+        renderer.draw_line("main_framebuffer", x1, y1, x2, y2, (255, 255, 255))
+        
+    await display_manager.update_displays()
+    print("✓ Circle drawn using line segments")
+    
+    # Test 5: Performance test
+    print("\nTest 5: Performance test - drawing many pixels")
+    start_time = asyncio.get_event_loop().time()
+    
+    # Draw a pattern of pixels
+    for y in range(500, 550):
+        for x in range(600, 700):
+            color_r = (x - 600) * 255 // 100
+            color_g = (y - 500) * 255 // 50
+            color_b = 128
+            driver.draw_pixel(x, y, (color_r, color_g, color_b))
+            
+    await driver.process_commands()
+    
+    # Process multiple ticks to handle all the pixel drawing tasks
+    for _ in range(10):
+        await vgpu.tick()
+        
+    end_time = asyncio.get_event_loop().time()
+    await display_manager.update_displays()
+    
+    pixels_drawn = 50 * 100  # 5000 pixels
+    time_taken = end_time - start_time
+    print(f"✓ Drew {pixels_drawn:,} pixels in {time_taken:.3f}s "
+          f"({pixels_drawn/time_taken:.0f} pixels/sec)")
+    
+    # Test 6: AI operations
+    print("\nTest 6: AI 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 vGPU
+    a_id = ai_accelerator.load_matrix(matrix_a, "test_matrix_a")
+    b_id = ai_accelerator.load_matrix(matrix_b, "test_matrix_b")
+    
+    # Perform matrix multiplication
+    result_id = ai_accelerator.matrix_multiply(a_id, b_id, "result_matrix")
+    
+    if result_id:
+        result = ai_accelerator.get_matrix(result_id)
+        expected = np.dot(matrix_a, matrix_b)
+        
+        if np.allclose(result, expected, rtol=1e-5):
+            print(f"✓ Matrix multiplication successful: {matrix_a.shape} x {matrix_b.shape} = {result.shape}")
+        else:
+            print("✗ Matrix multiplication result incorrect")
+    else:
+        print("✗ Matrix multiplication failed")
+    
+    # Final statistics
+    print("\n" + "=" * 50)
+    print("FINAL STATISTICS")
+    print("=" * 50)
+    
+    # vGPU stats
+    vgpu_stats = vgpu.get_stats()
+    print(f"vGPU Statistics:")
+    print(f"  Clock cycles: {vgpu_stats['clock_cycle']:,}")
+    print(f"  Tasks processed: {vgpu_stats['total_tasks_processed']:,}")
+    print(f"  Busy SMs: {vgpu_stats['busy_sms']}/{vgpu_stats['total_sms']}")
+    
+    # Renderer stats
+    render_stats = renderer.get_stats()
+    print(f"\nRenderer Statistics:")
+    print(f"  Pixels drawn: {render_stats['pixels_drawn']:,}")
+    print(f"  Draw calls: {render_stats['draw_calls']:,}")
+    print(f"  Render time: {render_stats['total_render_time']:.3f}s")
+    print(f"  Pixels/second: {render_stats['pixels_per_second']:,.0f}")
+    
+    # VRAM stats
+    vram_stats = vram.get_stats()
+    print(f"\nVRAM Statistics:")
+    print(f"  Total memory: {vram_stats['total_memory_gb']:.1f} GB")
+    print(f"  Utilization: {vram_stats['utilization_percent']:.2f}%")
+    print(f"  Framebuffers: {vram_stats['framebuffers_count']}")
+    print(f"  Textures: {vram_stats['textures_count']}")
+    
+    # AI stats
+    ai_stats = ai_accelerator.get_stats()
+    print(f"\nAI Accelerator Statistics:")
+    print(f"  Operations: {ai_stats['operations_performed']}")
+    print(f"  FLOPs performed: {ai_stats['flops_performed']:,}")
+    print(f"  FLOPs/second: {ai_stats['flops_per_second']:,.0f}")
+    print(f"  Matrices in memory: {ai_stats['matrices_in_memory']}")
+    
+    # Display stats
+    display_stats = display_manager.get_stats()
+    print(f"\nDisplay Statistics:")
+    print(f"  Frames displayed: {display_stats['frames_displayed']}")
+    print(f"  Display time: {display_stats['total_display_time']:.3f}s")
+    print(f"  Active displays: {display_stats['active_displays']}")
+    
+    # Driver stats
+    driver_stats = driver.get_stats()
+    print(f"\nDriver Statistics:")
+    print(f"  Commands processed: {driver_stats['commands_processed']}")
+    print(f"  Commands failed: {driver_stats['commands_failed']}")
+    
+    print("\n" + "=" * 50)
+    print("Basic Rendering Test Complete!")
+    print("Check ./test_output/ for saved frame images.")
+    print("=" * 50)
+
+
+if __name__ == "__main__":
+    # Run the basic rendering test
+    asyncio.run(basic_rendering_test())
+

virtual_gpu_setup/virtual_gpu/examples/test_virtual_ram_transfer.py

@@ -0,0 +1,150 @@
+import asyncio
+import time
+import numpy as np
+import os
+
+from virtual_gpu.vram import VRAM
+from virtual_gpu.virtual_ram import VirtualRAM
+from virtual_gpu.vgpu import VirtualGPU
+from virtual_gpu.driver import GPUDriver
+from virtual_gpu.render import Renderer
+from virtual_gpu.ai import AIAccelerator
+from virtual_gpu.shader import ShaderManager
+from virtual_gpu.display import DisplayManager, DisplayMode
+
+async def test_virtual_ram_transfer():
+    print("Starting Virtual RAM to VRAM Transfer Test...")
+    print("=" * 50)
+
+    # 1. Initialize VirtualRAM(128) and VirtualVRAM(500)
+    print("Initializing VirtualRAM and VRAM...")
+    ram = VirtualRAM(capacity_gb=128)
+    vram = VRAM(memory_size_gb=500)
+    print("✓ VirtualRAM (128GB) and VRAM (500GB) initialized.")
+
+    # Initialize other vGPU components for a complete environment
+    renderer = Renderer(vram)
+    ai_accelerator = AIAccelerator(vram)
+    vgpu = VirtualGPU(num_sms=800, total_cores=50000)
+    vgpu.set_modules(vram, renderer, ai_accelerator, None)
+    driver = GPUDriver(vgpu)
+    vgpu.driver = driver
+    shader_manager = ShaderManager()
+    display_manager = DisplayManager(vram)
+    file_display = display_manager.add_display("file", DisplayMode.FILE, output_dir="./test_output_ram_transfer")
+    print("✓ Other vGPU components initialized.")
+
+    print("\n" + "=" * 50)
+
+    # 2. Allocate a 1MB tensor in RAM.
+    tensor_size_mb = 1 # Use 1MB for actual data allocation
+    tensor_size_bytes = tensor_size_mb * 1024 * 1024
+    
+    print(f"Allocating a {tensor_size_mb}MB tensor in VirtualRAM...")
+    ram.allocate_block("my_ai_tensor", tensor_size_bytes, store_data=True) # Request real data
+    ram.print_info()
+    print("✓ 1MB tensor allocated with real data in VirtualRAM.")
+
+    print("\n" + "=" * 50)
+
+    # 3. Transfer that tensor into VRAM as 
+    print(f"Transferring \'my_ai_tensor\' from VirtualRAM to VRAM as \'ai_tensor_buffer\'")
+    transfer_start_time = time.time()
+    vram_tensor_id = ram.transfer_to_vram("my_ai_tensor", vram, "ai_tensor_buffer")
+    transfer_end_time = time.time()
+    time_taken_to_transfer = transfer_end_time - transfer_start_time
+    
+    if vram_tensor_id:
+        print(f"✓ Tensor transferred to VRAM. VRAM ID: {vram_tensor_id}")
+        print(f"Time taken to transfer: {time_taken_to_transfer:.4f} seconds")
+    else:
+        print("✗ Failed to transfer tensor to VRAM.")
+        return
+
+    print("\n" + "=" * 50)
+
+    # 4. Apply a real AI operation (matrix multiplication) on the VRAM buffer.
+    print("Applying a real AI operation (matrix multiplication) on the VRAM buffer...")   
+    # Retrieve the transferred data from VRAM (now it should contain actual data)
+    vram_block_data = vram.get_texture(vram_tensor_id) # This should now return actual data
+    if vram_block_data is None:
+        print("✗ Could not retrieve actual tensor data from VRAM for AI operation.")
+        return
+
+    # Create a dummy matrix B with real data for multiplication
+    # Ensure dimensions are compatible for multiplication
+    # Let\'s assume the transferred data is a flattened 1MB (1024*1024 bytes) of uint8
+    # We\'ll reshape it to (1024, 1024) for a dummy matrix A
+    try:
+        matrix_a_reshaped = vram_block_data.reshape((1024, 1024)).astype(np.float32)
+    except ValueError:
+        print("✗ Could not reshape transferred data for matrix A. Data size might not be suitable.")
+        return
+
+    # Create a dummy matrix B (e.g., 1024x512) for multiplication
+    matrix_b_data = np.random.rand(1024, 512).astype(np.float32)
+
+    # Load these real matrices into AI Accelerator (which will then load them into VRAM)
+    ai_accelerator.set_vram(vram) # Ensure AI accelerator has VRAM reference
+    matrix_a_id = ai_accelerator.load_matrix(matrix_a_reshaped, "real_ai_matrix_a")
+    matrix_b_id = ai_accelerator.load_matrix(matrix_b_data, "real_ai_matrix_b")
+
+    # Perform real matrix multiplication
+    result_id = ai_accelerator.matrix_multiply(matrix_a_id, matrix_b_id, "real_ai_op_result")
+    
+    if result_id:
+        result_matrix = ai_accelerator.get_matrix(result_id)
+        if result_matrix is not None:
+            print(f"✓ Real AI operation (matrix multiplication) completed. Result shape: {result_matrix.shape}")
+            # Optional: Verify a small part of the result
+            expected_result = np.dot(matrix_a_reshaped, matrix_b_data)
+            if np.allclose(result_matrix[:5,:5], expected_result[:5,:5]): # Compare a small section
+                print("✓ Result verification (partial) successful.")
+            else:
+                print("✗ Result verification (partial) failed.")
+        else:
+            print("✗ Could not retrieve result matrix from VRAM.")
+    else:
+        print("✗ Real AI operation failed.")
+
+    print("\n" + "=" * 50)
+
+    # 5. Output logs showing:
+    # RAM usage
+    # VRAM usage
+    # Time taken to transfer
+    # Operation success
+
+    print("FINAL LOGS AND STATISTICS")
+    print("=" * 50)
+
+    print("\nVirtual RAM Usage:")
+    ram.print_info()
+
+    print("\nVRAM Usage:")
+    vram_stats = vram.get_stats()
+    print(f'  Total memory: {vram_stats["total_memory_gb"]:.1f} GB')
+    print(f'  Utilization: {vram_stats["utilization_percent"]:.2f}%')
+    print(f'  Framebuffers: {vram_stats["framebuffers_count"]}')
+    print(f'  Textures/Buffers: {vram_stats["textures_count"]}')
+    print(f'  Allocated bytes: {vram_stats["allocated_bytes"]:,} bytes')
+
+    print(f"\nTime taken to transfer tensor from RAM to VRAM: {time_taken_to_transfer:.4f} seconds")
+    success_status = "Success" if result_id else "Failed"
+    print(f"AI Operation Success: {success_status}")
+
+    print("\n" + "=" * 50)
+    print("Virtual RAM Transfer Test Complete!")
+    print("Check ./test_output_ram_transfer/ for any saved frame images (if applicable).")
+    print("=" * 50)
+
+    # Clean up (optional)
+    # ram.release_block("my_ai_tensor")
+    # vram.delete_texture(vram_tensor_id)
+
+if __name__ == "__main__":
+    # Ensure the output directory exists
+    os.makedirs("./test_output_ram_transfer", exist_ok=True)
+    asyncio.run(test_virtual_ram_transfer())
+
+

virtual_gpu_setup/virtual_gpu/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!")
+

virtual_gpu_setup/virtual_gpu/shader.py

@@ -0,0 +1,386 @@
+"""
+Shader Module - Programmable Shader Logic
+
+This module provides a mechanism for simulating programmable shader logic,
+allowing custom functions to be applied to pixels or vertices during rendering.
+"""
+
+import numpy as np
+from typing import Callable, Dict, Any, Tuple, Optional
+from abc import ABC, abstractmethod
+import math
+
+
+class Shader(ABC):
+    """Abstract base class for all shaders."""
+    
+    @abstractmethod
+    def process_pixel(self, x: int, y: int, color: Tuple[int, int, int], 
+                     **kwargs) -> Tuple[int, int, int]:
+        """Process a single pixel and return the modified color."""
+        pass
+        
+    @abstractmethod
+    def process_vertex(self, x: float, y: float, z: float = 0.0, 
+                      **kwargs) -> Tuple[float, float, float]:
+        """Process a single vertex and return the modified position."""
+        pass
+
+
+class PixelShader(Shader):
+    """Base class for pixel shaders that only modify pixel colors."""
+    
+    def process_vertex(self, x: float, y: float, z: float = 0.0, 
+                      **kwargs) -> Tuple[float, float, float]:
+        """Default vertex processing (no change)."""
+        return (x, y, z)
+
+
+class VertexShader(Shader):
+    """Base class for vertex shaders that only modify vertex positions."""
+    
+    def process_pixel(self, x: int, y: int, color: Tuple[int, int, int], 
+                     **kwargs) -> Tuple[int, int, int]:
+        """Default pixel processing (no change)."""
+        return color
+
+
+class ColorTintShader(PixelShader):
+    """Shader that applies a color tint to all pixels."""
+    
+    def __init__(self, tint_color: Tuple[float, float, float], strength: float = 0.5):
+        self.tint_color = tint_color
+        self.strength = strength
+        
+    def process_pixel(self, x: int, y: int, color: Tuple[int, int, int], 
+                     **kwargs) -> Tuple[int, int, int]:
+        """Apply color tint to the pixel."""
+        r, g, b = color
+        tr, tg, tb = self.tint_color
+        
+        # Blend original color with tint
+        new_r = int(r * (1 - self.strength) + tr * 255 * self.strength)
+        new_g = int(g * (1 - self.strength) + tg * 255 * self.strength)
+        new_b = int(b * (1 - self.strength) + tb * 255 * self.strength)
+        
+        # Clamp values
+        new_r = max(0, min(255, new_r))
+        new_g = max(0, min(255, new_g))
+        new_b = max(0, min(255, new_b))
+        
+        return (new_r, new_g, new_b)
+
+
+class GrayscaleShader(PixelShader):
+    """Shader that converts colors to grayscale."""
+    
+    def process_pixel(self, x: int, y: int, color: Tuple[int, int, int], 
+                     **kwargs) -> Tuple[int, int, int]:
+        """Convert pixel to grayscale."""
+        r, g, b = color
+        
+        # Use luminance formula for better grayscale conversion
+        gray = int(0.299 * r + 0.587 * g + 0.114 * b)
+        gray = max(0, min(255, gray))
+        
+        return (gray, gray, gray)
+
+
+class SepiaShader(PixelShader):
+    """Shader that applies a sepia tone effect."""
+    
+    def process_pixel(self, x: int, y: int, color: Tuple[int, int, int], 
+                     **kwargs) -> Tuple[int, int, int]:
+        """Apply sepia tone to the pixel."""
+        r, g, b = color
+        
+        # Sepia transformation matrix
+        new_r = int(0.393 * r + 0.769 * g + 0.189 * b)
+        new_g = int(0.349 * r + 0.686 * g + 0.168 * b)
+        new_b = int(0.272 * r + 0.534 * g + 0.131 * b)
+        
+        # Clamp values
+        new_r = max(0, min(255, new_r))
+        new_g = max(0, min(255, new_g))
+        new_b = max(0, min(255, new_b))
+        
+        return (new_r, new_g, new_b)
+
+
+class InvertShader(PixelShader):
+    """Shader that inverts pixel colors."""
+    
+    def process_pixel(self, x: int, y: int, color: Tuple[int, int, int], 
+                     **kwargs) -> Tuple[int, int, int]:
+        """Invert pixel colors."""
+        r, g, b = color
+        return (255 - r, 255 - g, 255 - b)
+
+
+class BrightnessShader(PixelShader):
+    """Shader that adjusts pixel brightness."""
+    
+    def __init__(self, brightness: float = 0.0):
+        """
+        Initialize brightness shader.
+        
+        Args:
+            brightness: Brightness adjustment (-1.0 to 1.0)
+                       -1.0 = completely dark, 0.0 = no change, 1.0 = completely bright
+        """
+        self.brightness = max(-1.0, min(1.0, brightness))
+        
+    def process_pixel(self, x: int, y: int, color: Tuple[int, int, int], 
+                     **kwargs) -> Tuple[int, int, int]:
+        """Adjust pixel brightness."""
+        r, g, b = color
+        
+        if self.brightness >= 0:
+            # Brighten
+            new_r = int(r + (255 - r) * self.brightness)
+            new_g = int(g + (255 - g) * self.brightness)
+            new_b = int(b + (255 - b) * self.brightness)
+        else:
+            # Darken
+            new_r = int(r * (1 + self.brightness))
+            new_g = int(g * (1 + self.brightness))
+            new_b = int(b * (1 + self.brightness))
+            
+        # Clamp values
+        new_r = max(0, min(255, new_r))
+        new_g = max(0, min(255, new_g))
+        new_b = max(0, min(255, new_b))
+        
+        return (new_r, new_g, new_b)
+
+
+class ContrastShader(PixelShader):
+    """Shader that adjusts pixel contrast."""
+    
+    def __init__(self, contrast: float = 0.0):
+        """
+        Initialize contrast shader.
+        
+        Args:
+            contrast: Contrast adjustment (-1.0 to 1.0)
+                     -1.0 = no contrast, 0.0 = no change, 1.0 = maximum contrast
+        """
+        self.contrast = max(-1.0, min(1.0, contrast))
+        self.factor = (259 * (self.contrast * 255 + 255)) / (255 * (259 - self.contrast * 255))
+        
+    def process_pixel(self, x: int, y: int, color: Tuple[int, int, int], 
+                     **kwargs) -> Tuple[int, int, int]:
+        """Adjust pixel contrast."""
+        r, g, b = color
+        
+        new_r = int(self.factor * (r - 128) + 128)
+        new_g = int(self.factor * (g - 128) + 128)
+        new_b = int(self.factor * (b - 128) + 128)
+        
+        # Clamp values
+        new_r = max(0, min(255, new_r))
+        new_g = max(0, min(255, new_g))
+        new_b = max(0, min(255, new_b))
+        
+        return (new_r, new_g, new_b)
+
+
+class CheckerboardShader(PixelShader):
+    """Shader that creates a checkerboard pattern overlay."""
+    
+    def __init__(self, size: int = 8, color1: Tuple[int, int, int] = (255, 255, 255),
+                 color2: Tuple[int, int, int] = (0, 0, 0), blend: float = 0.5):
+        self.size = size
+        self.color1 = color1
+        self.color2 = color2
+        self.blend = blend
+        
+    def process_pixel(self, x: int, y: int, color: Tuple[int, int, int], 
+                     **kwargs) -> Tuple[int, int, int]:
+        """Apply checkerboard pattern."""
+        # Determine which checker square we're in
+        checker_x = x // self.size
+        checker_y = y // self.size
+        
+        # Determine checker color
+        if (checker_x + checker_y) % 2 == 0:
+            checker_color = self.color1
+        else:
+            checker_color = self.color2
+            
+        # Blend with original color
+        r, g, b = color
+        cr, cg, cb = checker_color
+        
+        new_r = int(r * (1 - self.blend) + cr * self.blend)
+        new_g = int(g * (1 - self.blend) + cg * self.blend)
+        new_b = int(b * (1 - self.blend) + cb * self.blend)
+        
+        return (new_r, new_g, new_b)
+
+
+class WaveDistortionShader(VertexShader):
+    """Shader that applies wave distortion to vertices."""
+    
+    def __init__(self, amplitude: float = 10.0, frequency: float = 0.1, time: float = 0.0):
+        self.amplitude = amplitude
+        self.frequency = frequency
+        self.time = time
+        
+    def process_vertex(self, x: float, y: float, z: float = 0.0, 
+                      **kwargs) -> Tuple[float, float, float]:
+        """Apply wave distortion to vertex position."""
+        # Apply sine wave distortion
+        offset_x = self.amplitude * math.sin(y * self.frequency + self.time)
+        offset_y = self.amplitude * math.sin(x * self.frequency + self.time)
+        
+        return (x + offset_x, y + offset_y, z)
+        
+    def update_time(self, time: float):
+        """Update the time parameter for animation."""
+        self.time = time
+
+
+class ShaderManager:
+    """Manages shader instances and provides shader registry functionality."""
+    
+    def __init__(self):
+        self.shaders: Dict[str, Shader] = {}
+        self.shader_counter = 0
+        
+        # Register built-in shaders
+        self._register_builtin_shaders()
+        
+    def _register_builtin_shaders(self):
+        """Register built-in shader types."""
+        self.register_shader("grayscale", GrayscaleShader())
+        self.register_shader("sepia", SepiaShader())
+        self.register_shader("invert", InvertShader())
+        self.register_shader("red_tint", ColorTintShader((1.0, 0.0, 0.0), 0.3))
+        self.register_shader("blue_tint", ColorTintShader((0.0, 0.0, 1.0), 0.3))
+        self.register_shader("bright", BrightnessShader(0.3))
+        self.register_shader("dark", BrightnessShader(-0.3))
+        self.register_shader("high_contrast", ContrastShader(0.5))
+        self.register_shader("checkerboard", CheckerboardShader())
+        
+    def register_shader(self, name: str, shader: Shader) -> None:
+        """Register a shader with a given name."""
+        self.shaders[name] = shader
+        
+    def get_shader(self, name: str) -> Optional[Shader]:
+        """Get a shader by name."""
+        return self.shaders.get(name)
+        
+    def create_custom_shader(self, pixel_func: Optional[Callable] = None,
+                           vertex_func: Optional[Callable] = None,
+                           name: Optional[str] = None) -> str:
+        """Create a custom shader from functions."""
+        if name is None:
+            name = f"custom_shader_{self.shader_counter}"
+            self.shader_counter += 1
+            
+        class CustomShader(Shader):
+            def __init__(self, pf, vf):
+                self.pixel_func = pf
+                self.vertex_func = vf
+                
+            def process_pixel(self, x: int, y: int, color: Tuple[int, int, int], 
+                            **kwargs) -> Tuple[int, int, int]:
+                if self.pixel_func:
+                    return self.pixel_func(x, y, color, **kwargs)
+                return color
+                
+            def process_vertex(self, x: float, y: float, z: float = 0.0, 
+                             **kwargs) -> Tuple[float, float, float]:
+                if self.vertex_func:
+                    return self.vertex_func(x, y, z, **kwargs)
+                return (x, y, z)
+                
+        shader = CustomShader(pixel_func, vertex_func)
+        self.register_shader(name, shader)
+        return name
+        
+    def list_shaders(self) -> list:
+        """Get a list of all registered shader names."""
+        return list(self.shaders.keys())
+        
+    def get_stats(self) -> Dict[str, Any]:
+        """Get shader manager statistics."""
+        return {
+            "total_shaders": len(self.shaders),
+            "shader_names": self.list_shaders()
+        }
+
+
+if __name__ == "__main__":
+    # Test the shader system
+    print("Testing Shader System...")
+    
+    # Create shader manager
+    shader_manager = ShaderManager()
+    
+    # Test built-in shaders
+    test_color = (128, 64, 192)
+    test_x, test_y = 100, 50
+    
+    print(f"Original color: {test_color}")
+    
+    # Test each built-in shader
+    for shader_name in shader_manager.list_shaders():
+        shader = shader_manager.get_shader(shader_name)
+        if shader:
+            result_color = shader.process_pixel(test_x, test_y, test_color)
+            print(f"{shader_name}: {result_color}")
+            
+    # Test custom shader
+    def rainbow_pixel(x, y, color, **kwargs):
+        """Custom shader that creates a rainbow effect based on position."""
+        r, g, b = color
+        
+        # Create rainbow effect based on x position
+        hue = (x % 360) / 360.0
+        
+        # Simple HSV to RGB conversion for rainbow effect
+        if hue < 1/6:
+            new_r, new_g, new_b = 255, int(255 * hue * 6), 0
+        elif hue < 2/6:
+            new_r, new_g, new_b = int(255 * (2/6 - hue) * 6), 255, 0
+        elif hue < 3/6:
+            new_r, new_g, new_b = 0, 255, int(255 * (hue - 2/6) * 6)
+        elif hue < 4/6:
+            new_r, new_g, new_b = 0, int(255 * (4/6 - hue) * 6), 255
+        elif hue < 5/6:
+            new_r, new_g, new_b = int(255 * (hue - 4/6) * 6), 0, 255
+        else:
+            new_r, new_g, new_b = 255, 0, int(255 * (1 - hue) * 6)
+            
+        # Blend with original color
+        blend = 0.7
+        final_r = int(r * (1 - blend) + new_r * blend)
+        final_g = int(g * (1 - blend) + new_g * blend)
+        final_b = int(b * (1 - blend) + new_b * blend)
+        
+        return (final_r, final_g, final_b)
+        
+    # Register custom shader
+    custom_name = shader_manager.create_custom_shader(pixel_func=rainbow_pixel, name="rainbow")
+    custom_shader = shader_manager.get_shader(custom_name)
+    
+    if custom_shader:
+        for x in range(0, 360, 60):
+            result = custom_shader.process_pixel(x, 0, test_color)
+            print(f"Rainbow shader at x={x}: {result}")
+            
+    # Test vertex shader
+    wave_shader = WaveDistortionShader(amplitude=5.0, frequency=0.1)
+    test_vertex = (100.0, 50.0, 0.0)
+    distorted_vertex = wave_shader.process_vertex(*test_vertex)
+    print(f"Original vertex: {test_vertex}")
+    print(f"Distorted vertex: {distorted_vertex}")
+    
+    # Print statistics
+    stats = shader_manager.get_stats()
+    print(f"Shader Manager stats: {stats}")
+    
+    print("Shader system test completed!")
+

virtual_gpu_setup/virtual_gpu/vgpu.py

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

virtual_gpu_setup/virtual_gpu/virtual_ram.py

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

virtual_gpu_setup/virtual_gpu/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}")
+
+
+
+