""" 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())