try: import numpy as np except ImportError: import pip pip.main(['install', 'numpy']) import numpy as np from typing import Dict, Any, List, Optional, Tuple import time import json import logging import duckdb from huggingface_hub import HfApi, HfFileSystem from .hal.hal import HardwareAbstractionLayer, HardwareType from .memory.duckdb_memory_manager import DuckDBMemoryManager from .commands.command_processor import CommandProcessor from .graphics.graphics_api import GraphicsAPI from .memory.memory_pool import MemoryPool, SharedMemoryPool from .graphics.texture_buffer_manager import ( Texture, Buffer, Framebuffer, TextureFormat, FilterMode, WrapMode, BufferType, MSAASamples, AttachmentType ) from .graphics.rasterizer import Rasterizer from tensor_core import TensorCore from config import get_db_url, get_hf_token_cached from multi_gpu_system_http import MultiGPUSystem from gpu_parallel_distributor import GPUParallelDistributor from parallel_array_distributor import ParallelArrayDistributor from streaming_multiprocessor import StreamingMultiprocessor from electron_speed import max_switch_freq, GATE_DELAY from logic_gates import LogicGate, NANDGate, ANDGate from .warp import Warp # GPU Architecture Constants DEFAULT_NUM_GPUS = 8 DEFAULT_SMS_PER_GPU = 1500 DEFAULT_CORES_PER_SM = 128 THREADS_PER_WARP = 32 # Resource Management Constants DEFAULT_SHARED_MEMORY_PER_SM = 32 * 1024 # 32KB DEFAULT_REGISTERS_PER_SM = 65536 DEFAULT_MAX_WARPS_PER_SM = DEFAULT_CORES_PER_SM // THREADS_PER_WARP # Scheduling Constants DEFAULT_LOCALITY_WEIGHT = 0.7 # Weight for data locality in scheduling DEFAULT_LOAD_WEIGHT = 0.3 # Weight for load balancing class GPUError(Exception): """Base class for GPU-related errors""" pass class MemoryError(GPUError): """Memory allocation or access error""" pass class StreamError(GPUError): """Stream operation error""" pass class TensorError(GPUError): """Tensor operation error""" pass class VirtualGPUDriver: DB_URL = "hf://datasets/Fred808/helium/storage.json" def __init__(self, num_gpus: int = DEFAULT_NUM_GPUS, num_sms_per_gpu: int = DEFAULT_SMS_PER_GPU, cores_per_sm: int = DEFAULT_CORES_PER_SM, db_url: Optional[str] = None): """Initialize GPU driver with configurable architecture parameters""" # Store architecture parameters self.num_gpus = num_gpus self.num_sms_per_gpu = num_sms_per_gpu self.cores_per_sm = cores_per_sm self.threads_per_warp = THREADS_PER_WARP # Initialize database connection self.db_url = db_url or self.DB_URL self.max_retries = 3 self._connect_with_retries() # Initialize HAL and multi-GPU system self.hal = HardwareAbstractionLayer(db_url=self.db_url) self.multi_gpu_system = MultiGPUSystem(num_gpus=self.num_gpus, db_url=self.db_url) self.parallel_distributor = GPUParallelDistributor(num_gpus=self.num_gpus) # Initialize streaming multiprocessors self.streaming_multiprocessors = {} # Initialize database and tables self._setup_database() # Initialize streaming multiprocessors dict for each chip for chip_id in range(num_gpus): self.streaming_multiprocessors[chip_id] = {} # Initialize graphics subsystems self._initialize_graphics_subsystem() def _connect_with_retries(self): """Establish database connection with retry logic""" for attempt in range(self.max_retries): try: self.conn = self._init_db_connection() return except Exception as e: if attempt == self.max_retries - 1: raise RuntimeError(f"Failed to initialize database after {self.max_retries} attempts: {str(e)}") time.sleep(1) def _init_db_connection(self) -> duckdb.DuckDBPyConnection: """Initialize database connection with HuggingFace configuration""" # Convert HF URL to S3 path _, _, owner, dataset, db_file = self.db_url.split('/', 4) db_path = f"s3://datasets-cached/{owner}/{dataset}/{db_file}" # Connect to remote database conn = duckdb.connect(db_path) conn.execute("INSTALL httpfs;") conn.execute("LOAD httpfs;") conn.execute("SET s3_endpoint='s3.us-east-1.amazonaws.com';") conn.execute("SET s3_use_ssl=true;") conn.execute("SET s3_url_style='path';") conn.execute(f"SET s3_access_key_id='{self.HF_TOKEN}';") conn.execute(f"SET s3_secret_access_key='{self.HF_TOKEN}';") return conn def _setup_database(self): """Initialize database tables""" # GPU state tracking self.conn.execute(""" CREATE TABLE IF NOT EXISTS gpu_state ( chip_id INTEGER PRIMARY KEY, num_sms INTEGER, cores_per_sm INTEGER, memory_size BIGINT, utilization DOUBLE, temperature DOUBLE, power_usage DOUBLE, last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP, state_json JSON ) """) # Streaming multiprocessor tracking self.conn.execute(""" CREATE TABLE IF NOT EXISTS streaming_multiprocessors ( sm_id VARCHAR PRIMARY KEY, chip_id INTEGER, num_cores INTEGER, active_warps INTEGER, shared_memory_used BIGINT, registers_used INTEGER, last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP, state_json JSON ) """) # Warp execution tracking self.conn.execute(""" CREATE TABLE IF NOT EXISTS warp_execution ( warp_id VARCHAR PRIMARY KEY, sm_id VARCHAR, chip_id INTEGER, num_threads INTEGER, program_counter BIGINT, status VARCHAR, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, updated_at TIMESTAMP, state_json JSON, FOREIGN KEY (sm_id) REFERENCES streaming_multiprocessors(sm_id) ) """) # Shared memory pools self.conn.execute(""" CREATE TABLE IF NOT EXISTS shared_memory_pools ( pool_id VARCHAR PRIMARY KEY, gpu_id INTEGER, size BIGINT, allocated_size BIGINT, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, last_accessed TIMESTAMP, last_modified TIMESTAMP, state_json JSON ) """) def _initialize_graphics_subsystem(self): """Initialize graphics-related subsystems""" from src.graphics.texture_buffer_manager import ( Texture, Buffer, Framebuffer, TextureFormat, FilterMode, WrapMode, BufferType, MSAASamples ) from src.graphics.rasterizer import Rasterizer # Store classes for easy access self.TextureFormat = TextureFormat self.FilterMode = FilterMode self.WrapMode = WrapMode self.BufferType = BufferType self.MSAASamples = MSAASamples # Initialize managers self.texture_manager = {} # texture_id -> Texture self.buffer_manager = {} # buffer_id -> Buffer self.framebuffer_manager = {} # fb_id -> Framebuffer self.next_texture_id = 0 self.next_buffer_id = 0 self.next_fb_id = 0 # Initialize rasterizer for each GPU self.rasterizers = { chip_id: Rasterizer(self) for chip_id in range(self.num_gpus) } def create_texture(self, width: int, height: int, format: TextureFormat = None, filter_mode: FilterMode = None, wrap_mode: WrapMode = None, generate_mipmaps: bool = True, aniso_level: int = 1) -> int: """ Create a new texture with specified parameters Returns texture_id """ format = format or self.TextureFormat.RGBA8 filter_mode = filter_mode or self.FilterMode.BILINEAR wrap_mode = wrap_mode or self.WrapMode.REPEAT texture = Texture(width, height, format, filter_mode, wrap_mode, generate_mipmaps, aniso_level) texture_id = self.next_texture_id self.texture_manager[texture_id] = texture self.next_texture_id += 1 return texture_id def create_buffer(self, data: np.ndarray, buffer_type: BufferType = None, dynamic: bool = False, map_write: bool = False) -> int: """ Create a new buffer with specified parameters Returns buffer_id """ buffer_type = buffer_type or self.BufferType.VERTEX buffer = Buffer(data, buffer_type, dynamic, map_write) buffer_id = self.next_buffer_id self.buffer_manager[buffer_id] = buffer self.next_buffer_id += 1 return buffer_id def create_framebuffer(self, width: int, height: int, color_formats: List[TextureFormat] = None, depth_format: Optional[TextureFormat] = None, stencil_format: Optional[TextureFormat] = None, samples: MSAASamples = None) -> int: """ Create a new framebuffer with specified parameters Returns framebuffer_id """ color_formats = color_formats or [self.TextureFormat.RGBA8] samples = samples or self.MSAASamples.MSAA_1X fb = Framebuffer(width, height, color_formats, depth_format, stencil_format, samples) fb_id = self.next_fb_id self.framebuffer_manager[fb_id] = fb self.next_fb_id += 1 return fb_id def upload_texture_data(self, texture_id: int, data: np.ndarray, generate_mipmaps: bool = True): """Upload new data to existing texture""" if texture_id not in self.texture_manager: raise GPUError(f"Invalid texture ID: {texture_id}") texture = self.texture_manager[texture_id] texture.upload(data, generate_mipmaps) def upload_buffer_data(self, buffer_id: int, data: np.ndarray): """Upload new data to existing buffer""" if buffer_id not in self.buffer_manager: raise GPUError(f"Invalid buffer ID: {buffer_id}") buffer = self.buffer_manager[buffer_id] buffer.upload(data) def map_buffer(self, buffer_id: int) -> Optional[np.ndarray]: """Map buffer for CPU access""" if buffer_id not in self.buffer_manager: raise GPUError(f"Invalid buffer ID: {buffer_id}") buffer = self.buffer_manager[buffer_id] return buffer.map() def unmap_buffer(self, buffer_id: int): """Unmap previously mapped buffer""" if buffer_id not in self.buffer_manager: raise GPUError(f"Invalid buffer ID: {buffer_id}") buffer = self.buffer_manager[buffer_id] buffer.unmap() def draw_triangles(self, vertex_buffer_id: int, index_buffer_id: Optional[int], framebuffer_id: int, shader_program: Dict, num_vertices: int, start_vertex: int = 0, chip_id: int = 0): """ Draw triangles using specified buffers and shader program Args: vertex_buffer_id: Buffer containing vertex data index_buffer_id: Optional buffer containing indices framebuffer_id: Target framebuffer shader_program: Dict containing vertex and fragment shader code num_vertices: Number of vertices to draw start_vertex: Starting vertex offset chip_id: GPU to use for rendering """ if vertex_buffer_id not in self.buffer_manager: raise GPUError(f"Invalid vertex buffer ID: {vertex_buffer_id}") if index_buffer_id and index_buffer_id not in self.buffer_manager: raise GPUError(f"Invalid index buffer ID: {index_buffer_id}") if framebuffer_id not in self.framebuffer_manager: raise GPUError(f"Invalid framebuffer ID: {framebuffer_id}") vertex_buffer = self.buffer_manager[vertex_buffer_id] index_buffer = (self.buffer_manager[index_buffer_id] if index_buffer_id else None) framebuffer = self.framebuffer_manager[framebuffer_id] # Get vertices (either directly or through indices) if index_buffer: indices = index_buffer.data[start_vertex:start_vertex + num_vertices] vertices = vertex_buffer.data[indices] else: vertices = vertex_buffer.data[start_vertex:start_vertex + num_vertices] # Process vertices in groups of 3 (triangles) for i in range(0, len(vertices), 3): v0, v1, v2 = vertices[i:i+3] # Rasterize triangle fragments = self.rasterizers[chip_id].rasterize_triangle( v0, v1, v2, framebuffer.width, framebuffer.height, framebuffer.samples ) # Process fragments processed = self.rasterizers[chip_id].process_fragments( fragments, shader_program, chip_id, early_z=True, hierarchical_z=True ) # Perform depth testing passed, depth_buffer = self.rasterizers[chip_id].depth_test( processed, framebuffer.depth_attachment.data.tobytes() if framebuffer.depth_attachment else None, framebuffer.width ) # Write to framebuffer for target_idx, target in enumerate(framebuffer.color_attachments): target.data = np.frombuffer( self.rasterizers[chip_id].write_to_framebuffer( passed, target.data.tobytes(), framebuffer.width, blend_enable=True ), dtype=target.data.dtype ).reshape(target.data.shape) def _initialize_hal_schema(self): """Initialize HAL database schema for multi-GPU tracking""" # Create tables for cross-GPU operation tracking self.hal.conn.execute(""" CREATE TABLE IF NOT EXISTS cross_gpu_operations ( operation_id INTEGER PRIMARY KEY AUTOINCREMENT, operation_type TEXT, source_chip INTEGER, target_chip INTEGER, nvlink_path TEXT, start_time TIMESTAMP, end_time TIMESTAMP, state_json JSON ) """) # Create memory coherence tracking self.hal.conn.execute(""" CREATE TABLE IF NOT EXISTS memory_coherence ( address INTEGER, chip_id INTEGER, version INTEGER, last_modified TIMESTAMP, dirty BOOLEAN, PRIMARY KEY (address, chip_id) ) """) # Create cross-GPU synchronization points self.hal.conn.execute(""" CREATE TABLE IF NOT EXISTS sync_points ( sync_id INTEGER PRIMARY KEY AUTOINCREMENT, operation_id INTEGER, chips_involved JSON, reached_by JSON, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ) """) self.hal.conn.commit() def _initialize_memory_management(self, num_chips: int, vram_size_gb: int): """Initialize cross-GPU memory management""" # Create memory pools for each GPU for chip_id in range(num_chips): # Global memory pool pool_id = f"gpu_{chip_id}_global" self.memory_pools[pool_id] = MemoryPool( size_bytes=vram_size_gb * 1024 * 1024 * 1024, chip_id=chip_id ) # Create shared memory pools between GPUs for other_chip in range(chip_id + 1, num_chips): shared_pool_id = f"shared_pool_{chip_id}_{other_chip}" self.shared_pools[shared_pool_id] = SharedMemoryPool( size_bytes=1 * 1024 * 1024 * 1024, # 1GB shared memory gpu_a=chip_id, gpu_b=other_chip ) # Initialize memory tracking in HAL self.hal.conn.execute(""" CREATE TABLE IF NOT EXISTS memory_transfers ( transfer_id INTEGER PRIMARY KEY AUTOINCREMENT, source_chip INTEGER, target_chip INTEGER, size_bytes INTEGER, nvlink_path TEXT, start_time TIMESTAMP, end_time TIMESTAMP, bandwidth_used FLOAT ) """) def _initialize_command_processors(self, num_chips: int): """Initialize command processors for cross-GPU operations""" # Create command queue for each chip pair for chip_a in range(num_chips): for chip_b in range(chip_a + 1, num_chips): queue_id = f"xfer_{chip_a}_{chip_b}" self.hal.conn.execute(""" INSERT INTO hardware_queues ( queue_id, hardware_type, chip_id, sm_id, instructions ) VALUES (?, ?, ?, ?, ?) """, ( queue_id, 'DMA_ENGINE', chip_a, None, json.dumps([]) )) # Create compute queues for each chip for chip_id in range(num_chips): self.hal.conn.execute(""" INSERT INTO hardware_queues ( queue_id, hardware_type, chip_id, sm_id, instructions ) VALUES (?, ?, ?, ?, ?) """, ( f"compute_{chip_id}", 'COMPUTE_UNIT', chip_id, None, json.dumps([]) )) # Initialize Streaming Multiprocessors with enhanced state tracking for chip_id in range(num_chips): for sm_id in range(self.num_sms_per_chip): sm = StreamingMultiprocessor( sm_id=sm_id, chip_id=chip_id, num_cores=self.cores_per_sm, db_url=self.db_url ) self.streaming_multiprocessors[chip_id][sm_id] = sm # Initialize state tracking in HAL self.hal.conn.execute(""" INSERT INTO hardware_states ( component_id, hardware_type, chip_id, sm_id, state_json ) VALUES (?, ?, ?, ?, ?) """, ( f"sm_{chip_id}_{sm_id}", 'STREAMING_MULTIPROCESSOR', chip_id, sm_id, json.dumps(sm.sm_state) )) # Initialize array distributor for each GPU with SM integration self.array_distributors = {} for chip_id in range(self.num_gpus): distributor = ParallelArrayDistributor( num_sms=self.num_sms_per_gpu, cores_per_sm=self.cores_per_sm ) self.array_distributors[chip_id] = distributor # Link distributor to storage and SM state distributor.storage = self.local_storage distributor.streaming_multiprocessors = self.streaming_multiprocessors[chip_id] # Initialize warps with enhanced state management self.warps = {} for chip_id in range(self.num_gpus): self.warps[chip_id] = {} for sm_id in range(self.num_sms_per_gpu): self.warps[chip_id][sm_id] = [] sm = self.streaming_multiprocessors[chip_id][sm_id] warps_per_sm = self.cores_per_sm // self.threads_per_warp for warp_id in range(warps_per_sm): warp = Warp(warp_id) self.warps[chip_id][sm_id].append(warp) # Register warp with SM scheduler sm.schedule_warp(str(warp_id), { 'warp_id': warp_id, 'status': 'initialized', 'priority': 0, 'dependencies': [], 'resource_requirements': { 'tensor_cores': 1, 'shared_memory': 32768 # 32KB default } }) # Initialize core components self.hal = HardwareAbstractionLayer() self.tensor_core = TensorCore() self.local_storage = LocalStorage() self.memory_manager = DuckDBMemoryManager(get_db_url()) self.stream_manager = {} # Initialize synchronization and memory management self.initialized = False self._sync_primitives = {} self.memory_pools = {} # pool_id -> MemoryPool self.shared_pools = {} # pool_id -> SharedMemoryPool # Initialize timing and voltage simulation self.gate_delay = GATE_DELAY self.max_switch_freq = max_switch_freq self.logic_gates = { 'nand': NANDGate(), 'and': ANDGate() } def allocate_memory(self, size_bytes, chip_id=None, key=None, tensor_shape=None, dtype=None): """ Allocate memory with support for tensor operations and local storage across multiple GPUs. Args: size_bytes: Size of memory to allocate chip_id: Target GPU chip (if None, will be automatically selected) key: Optional persistent storage key tensor_shape: Shape for tensor allocation dtype: Data type for tensor allocation """ if not self.initialized: raise RuntimeError("Driver not initialized.") try: # Choose optimal GPU if chip_id not specified if chip_id is None: gpu_states = self.multi_gpu_system.system_state["global_memory_state"]["allocation_map"] # Select GPU with most free memory available_memory = {gpu: state.get("free_memory", float('inf')) for gpu, state in gpu_states.items()} chip_id = max(available_memory.items(), key=lambda x: x[1])[0] # Validate chip_id if chip_id >= len(self.streaming_multiprocessors): raise MemoryError(f"Invalid chip_id: {chip_id}") address = None # Handle tensor allocation with parallel distribution if tensor_shape is not None: if dtype is None: dtype = np.float32 # Use array distributor to determine optimal SM allocation array_distributor = self.array_distributors[chip_id] sm_assignments = array_distributor.get_optimal_sm_assignment(tensor_shape) # Allocate tensor memory with SM distribution info address = self.memory_manager.allocate_tensor( tensor_shape, str(dtype), chip_id, sm_assignments=sm_assignments ) if key: self.local_storage.store(key, { 'address': address, 'shape': tensor_shape, 'dtype': str(dtype), 'chip_id': chip_id, 'sm_assignments': sm_assignments }) # Update GPU system state self.multi_gpu_system.update_memory_allocation( chip_id, size_bytes, tensor=True, sm_assignments=sm_assignments ) # Regular memory allocation with key elif key is not None: address = self.memory_manager.allocate_with_key( size_bytes, key, chip_id, tensor_shape=tensor_shape, dtype=str(dtype) if dtype else None ) self.local_storage.store(key, { 'address': address, 'chip_id': chip_id }) self.multi_gpu_system.update_memory_allocation(chip_id, size_bytes) # Default allocation else: address = self.memory_manager.allocate(size_bytes, chip_id) self.multi_gpu_system.update_memory_allocation(chip_id, size_bytes) return address except Exception as e: if tensor_shape is not None: raise TensorError(f"Failed to allocate tensor: {str(e)}") raise MemoryError(f"Failed to allocate memory: {str(e)}") def list_memory_keys(self): """List all string keys for persistent memory blocks""" if hasattr(self.memory_manager, 'list_keys'): return self.memory_manager.list_keys() return [] def _calculate_data_locality(self, sm_id: int, operation_data: Dict[str, Any]) -> float: """ Calculate data locality score for an SM based on input data location Returns score between 0 and 1 (1 = all data is local) """ total_data_size = 0 local_data_size = 0 # Check each input tensor's location for tensor_info in operation_data["inputs"]: size = tensor_info.get("size", 0) total_data_size += size # Check if data is in SM's L1 cache or shared memory if self._is_data_local(sm_id, tensor_info["address"]): local_data_size += size return local_data_size / total_data_size if total_data_size > 0 else 0.0 def _is_data_local(self, sm_id: int, address: int) -> bool: """Check if data at address is local to the given SM""" sm = self.streaming_multiprocessors[self.active_chip_id][sm_id] # Check L1 cache if address in sm.sm_state["l1_cache"]: return True # Check shared memory if str(address) in sm.sm_state["shared_memory"]: return True return False def _select_best_sm(self, operation_data: Dict[str, Any]) -> Tuple[int, int]: """ Select best SM for operation based on locality and load Returns (chip_id, sm_id) """ best_score = -1 best_sm = None best_chip = None # Check each GPU and SM for chip_id in range(self.num_gpus): for sm_id in range(self.num_sms_per_gpu): sm = self.streaming_multiprocessors[chip_id][sm_id] # Calculate load (number of active operations) load = len(sm.current_tensor_ops) # Calculate locality score (0 to 1) locality_score = self._calculate_data_locality(sm_id, operation_data) # Combine scores with weights load_score = 1.0 / (load + 1) # Inverse of load (higher = better) final_score = (locality_score * DEFAULT_LOCALITY_WEIGHT + load_score * DEFAULT_LOAD_WEIGHT) if final_score > best_score: best_score = final_score best_sm = sm_id best_chip = chip_id return best_chip, best_sm def schedule_tensor_operation(self, operation_data: Dict[str, Any]) -> Tuple[int, int]: """ Schedule a tensor operation on the best SM considering: - Data locality (70% weight) - Current SM load (30% weight) Returns (chip_id, sm_id) of selected SM """ # First check if operation has locality requirements if "preferred_sm" in operation_data: chip_id = operation_data.get("preferred_chip", 0) sm_id = operation_data["preferred_sm"] # Verify SM isn't overloaded sm = self.streaming_multiprocessors[chip_id][sm_id] if len(sm.current_tensor_ops) < DEFAULT_MAX_WARPS_PER_SM: return chip_id, sm_id # Otherwise select best SM based on locality and load return self._select_best_sm(operation_data) def create_stream(self, priority=0): """Create a new execution stream""" stream_id = len(self.stream_manager) self.stream_manager[stream_id] = { 'priority': priority, 'pending_ops': [], 'sync_points': set() } return stream_id def stream_synchronize(self, stream_id): """Wait for all operations in a stream to complete""" if stream_id not in self.stream_manager: raise StreamError(f"Invalid stream ID: {stream_id}") stream = self.stream_manager[stream_id] # Process pending operations while stream['pending_ops']: op = stream['pending_ops'].pop(0) try: self._execute_stream_op(op) except Exception as e: raise StreamError(f"Stream operation failed: {str(e)}") # Clear sync points stream['sync_points'].clear() def _execute_stream_op(self, op): """Execute a single stream operation""" op_type = op.get('type') if op_type == 'tensor': self._execute_tensor_op(op) elif op_type == 'memory': self._execute_memory_op(op) elif op_type == 'sync': self._handle_sync_point(op) else: raise StreamError(f"Unknown operation type: {op_type}") def add_stream_sync_point(self, stream_id, sync_point_id): """Add a synchronization point to a stream""" if stream_id not in self.stream_manager: raise StreamError(f"Invalid stream ID: {stream_id}") self.stream_manager[stream_id]['sync_points'].add(sync_point_id) self._sync_primitives[sync_point_id] = False # Not reached yet def execute_tensor_op(self, op_type, inputs, output, stream_id=None): """Execute a tensor operation with parallel distribution across multiple GPUs""" if stream_id is not None and stream_id not in self.stream_manager: raise StreamError(f"Invalid stream ID: {stream_id}") try: # Get optimal GPUs based on current load and data locality input_sizes = {addr: inp.nbytes for addr, inp in inputs.items()} target_gpus = self._select_optimal_gpus(input_sizes) # Get operation distribution plan using parallel distributor distributed_ops = self.parallel_distributor.distribute_operation({ 'type': op_type, 'inputs': inputs, 'output': output, 'input_size': sum(input_sizes.values()), 'target_gpus': target_gpus }) # Track operation in HAL op_id = self._register_cross_gpu_operation(op_type, distributed_ops) # Set up memory coherence tracking self._setup_memory_coherence(distributed_ops) # Track timing using electron speed calculations start_time = time.time() expected_compute_time = len(distributed_ops) * self.gate_delay # Create operation descriptors for each GPU operations = [] for dist_op in distributed_ops: gpu_id = dist_op['gpu_id'] sm_id = self._select_optimal_sm(gpu_id, dist_op) # Get available warp warp = self._get_available_warp(gpu_id, sm_id) op = { 'type': 'tensor', 'op_type': op_type, 'gpu_id': gpu_id, 'sm_id': sm_id, 'warp_id': warp.warp_id, 'inputs': dist_op['inputs'], 'output_slice': dist_op.get('output_slice'), 'stream_id': stream_id, 'all_inputs': inputs, 'all_output': output } if stream_id is not None: # Add to stream for asynchronous execution self.stream_manager[stream_id]['pending_ops'].append(op) return None else: # Execute immediately return self._execute_tensor_op(op) except Exception as e: raise TensorError(f"Tensor operation failed: {str(e)}") def _execute_tensor_op(self, op): """Execute a tensor operation""" op_type = op['op_type'] inputs = op['inputs'] output = op['output'] # Validate tensor metadata for addr in inputs: if not self.memory_manager.is_tensor(addr): raise TensorError(f"Invalid tensor address: {addr}") if not self.memory_manager.is_tensor(output): raise TensorError(f"Invalid output tensor address: {output}") # Execute operation through tensor core return self.tensor_core.execute_op(op_type, inputs, output) def read_memory_by_key(self, key): """ Read memory block by string key (if supported). """ if hasattr(self.memory_manager, 'read_by_key'): return self.memory_manager.read_by_key(key) raise NotImplementedError("Underlying memory manager does not support read_by_key.") def layernorm(self, x, gamma, beta, eps=1e-5, chip_id=0, sm_id=0): # Simulate photon-speed LayerNorm (timing, bandwidth, etc. can be modeled here) mean = np.mean(x, axis=-1, keepdims=True) var = np.var(x, axis=-1, keepdims=True) x_norm = (x - mean) / np.sqrt(var + eps) return gamma * x_norm + beta def gelu(self, x, chip_id=0, sm_id=0): # Simulate photon-speed GELU (timing, bandwidth, etc. can be modeled here) return 0.5 * x * (1 + np.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3)))) def softmax(self, x, axis=-1, chip_id=0, sm_id=0): # Simulate photon-speed softmax (timing, bandwidth, etc. can be modeled here) x_max = np.max(x, axis=axis, keepdims=True) e_x = np.exp(x - x_max) return e_x / np.sum(e_x, axis=axis, keepdims=True) def matmul(self, A, B, chip_id=0, sm_id=0): # Route to HAL/tensor core for photon-speed simulation return self.hal.v2_tensor_matmul(chip_id, A, B) def initialize(self, num_chips=8, vram_size_gb=16, num_sms_per_chip=1500, num_cores_per_sm=128, threads_per_core=700000, threads_per_block=32, num_tensor_cores_per_sm=8): """Initialize the virtual GPU driver with full multi-GPU support""" print("Initializing Virtual GPU Driver...") # Validate configuration against physical limits total_threads = num_chips * num_sms_per_chip * num_cores_per_sm * threads_per_core max_total_threads = self.max_switch_freq * self.gate_delay if total_threads > max_total_threads: raise ValueError( f"Configuration exceeds maximum possible threads at current electron speed.\n" f"Requested: {total_threads:,} threads\n" f"Maximum possible: {max_total_threads:,} threads" ) # Calculate memory and power parameters memory_clock_hz = 2132000000 # 2.132 GHz memory_bus_width = 384 # 384-bit memory_bandwidth_per_gpu = (memory_clock_hz * memory_bus_width) / (8 * 1e9) # GB/s watts_per_sm = 25 total_power_per_gpu = min(watts_per_sm * num_sms_per_chip, 350) # Cap at 350W # Initialize hardware configuration self.hardware_config = { # Basic configuration 'num_chips': num_chips, 'vram_size_gb': vram_size_gb, 'num_sms_per_chip': num_sms_per_chip, 'num_cores_per_sm': num_cores_per_sm, 'threads_per_core': threads_per_core, 'threads_per_block': threads_per_block, 'num_tensor_cores_per_sm': num_tensor_cores_per_sm, 'total_threads': total_threads, # Timing and performance 'gate_delay': self.gate_delay, 'max_switch_freq': self.max_switch_freq, # Memory configuration 'memory_config': { 'memory_clock_hz': memory_clock_hz, 'memory_bus_width': memory_bus_width, 'bandwidth_gb_per_sec': memory_bandwidth_per_gpu, 'nvlink_bandwidth_gbps': 900, # NVLink 4.0 'l1_cache_size_kb': 128, 'l2_cache_size_mb': 6, 'shared_memory_per_sm_kb': 164 }, # Power and thermal 'power_config': { 'watts_per_sm': watts_per_sm, 'total_power_limit': total_power_per_gpu, 'thermal_limit_celsius': 85, 'total_system_power': total_power_per_gpu * num_chips }, # Compute capability 'compute_capability': { 'major': 9, 'minor': 0, 'features': { 'tensor_cores': True, 'ray_tracing_cores': True, 'optical_flow': True, 'concurrent_kernels': 128, 'max_shared_memory_per_sm': 164 * 1024, # 164 KB 'max_registers_per_thread': 255, 'max_warps_per_sm': 64 } } } # Initialize hardware abstraction layer with detailed config self.hal.initialize_hardware( num_chips=num_chips, vram_size_gb=vram_size_gb, num_sms_per_chip=num_sms_per_chip, num_cores_per_sm=num_cores_per_sm, threads_per_core=threads_per_core, threads_per_block=threads_per_block, num_tensor_cores_per_sm=num_tensor_cores_per_sm, memory_config=self.hardware_config['memory_config'], power_config=self.hardware_config['power_config'], compute_capability=self.hardware_config['compute_capability'] ) # Initialize multi-GPU system components for chip_id in range(num_chips): # Initialize streaming multiprocessors for sm_id in range(num_sms_per_chip): sm = self.streaming_multiprocessors[chip_id][sm_id] sm.initialize( threads_per_core=threads_per_core, threads_per_block=threads_per_block, num_tensor_cores=num_tensor_cores_per_sm ) # Initialize array distributors self.array_distributors[chip_id].initialize( threads_per_core=threads_per_core, gate_delay=self.gate_delay ) # Initialize parallel distributor self.parallel_distributor.initialize( hardware_config=self.hardware_config, nvlink_topology=self.multi_gpu_system.system_state["nvlink_state"]["connections"] ) # Set up cross-GPU memory pools for chip_id in range(num_chips): pool_id = f"gpu_{chip_id}_global" self.memory_pools[pool_id] = MemoryPool( size_bytes=vram_size_gb * 1024 * 1024 * 1024, # Convert GB to bytes chip_id=chip_id ) # Set up shared memory pools for inter-GPU communication for i in range(num_chips): for j in range(i + 1, num_chips): pool_id = f"shared_pool_{i}_{j}" self.shared_pools[pool_id] = SharedMemoryPool( size_bytes=1 * 1024 * 1024 * 1024, # 1GB shared memory gpu_a=i, gpu_b=j ) self.initialized = True print(f"Virtual GPU Driver initialized with:") print(f"- {num_chips} GPUs") print(f"- {num_sms_per_chip} SMs per GPU") print(f"- {vram_size_gb}GB VRAM per GPU") print(f"- {self.hardware_config['total_threads']:,} total threads") print(f"- {len(self.shared_pools)} shared memory pools") print(f"- Gate delay: {self.gate_delay:.2e} seconds") print(f"- Max switching frequency: {self.max_switch_freq:.2e} Hz") def shutdown(self): print("Shutting down Virtual GPU Driver...") self.hal.shutdown_hardware() # Clean up multi-GPU system for chip_id in range(len(self.streaming_multiprocessors)): for sm_id in self.streaming_multiprocessors[chip_id]: self.streaming_multiprocessors[chip_id][sm_id].shutdown() self.multi_gpu_system.store_system_state() self.initialized = False print("Virtual GPU Driver shut down.") def _select_optimal_sm(self, gpu_id: int, operation: Dict[str, Any]) -> int: """Select the optimal SM for an operation based on load and locality""" sms = self.streaming_multiprocessors[gpu_id] # Get SM loads sm_loads = { sm_id: len(sm.current_tensor_ops) for sm_id, sm in sms.items() } # Check data locality sm_locality_scores = { sm_id: self._calculate_data_locality(sm, operation) for sm_id, sm in sms.items() } # Combine load and locality scores sm_scores = { sm_id: (sm_locality_scores[sm_id] * DEFAULT_LOCALITY_WEIGHT + (1.0 / (load + 1)) * DEFAULT_LOAD_WEIGHT) for sm_id, load in sm_loads.items() } return max(sm_scores.items(), key=lambda x: x[1])[0] def _calculate_data_locality(self, sm: StreamingMultiprocessor, operation: Dict[str, Any]) -> float: """Calculate data locality score for an SM based on input data location""" locality_score = 0.0 # Check if input data is already in SM's memory for input_data in operation['inputs'].values(): if hasattr(input_data, 'address'): if sm.has_data_in_local_memory(input_data.address): locality_score += 1.0 return locality_score def _get_available_warp(self, gpu_id: int, sm_id: int) -> Warp: """Get an available warp from the specified SM""" warps = self.warps[gpu_id][sm_id] # Find least loaded warp warp_loads = [len(warp.get_active_threads()) for warp in warps] min_load_idx = warp_loads.index(min(warp_loads)) return warps[min_load_idx] def allocate_memory(self, size_bytes, chip_id=0): if not self.initialized: raise RuntimeError("Driver not initialized.") return self.memory_manager.allocate(size_bytes, chip_id) def batch_allocate_memory(self, allocations: list[tuple[int, str]], chip_id=0): if not self.initialized: raise RuntimeError("Driver not initialized.") if hasattr(self.memory_manager, 'batch_allocate_with_keys'): return self.memory_manager.batch_allocate_with_keys(allocations, chip_id) else: # Fallback to individual allocations if batching not supported addresses = [] for size_bytes, key in allocations: addresses.append(self.memory_manager.allocate_with_key(size_bytes, key, chip_id)) return addresses def free_memory(self, address, chip_id=0): """Free allocated memory""" if not self.initialized: raise RuntimeError("Driver not initialized.") try: # Check if it's a tensor tensor_info = self.memory_manager.get_tensor_info(address) if tensor_info: # Free tensor memory key = f"tensor_{address}" if self.local_storage.exists(key): self.local_storage.delete(key) self.memory_manager.free(address, chip_id) return # Regular memory free self.memory_manager.free(address, chip_id) # Clean up local storage key = f"mem_{address}" if self.local_storage.exists(key): self.local_storage.delete(key) except Exception as e: raise MemoryError(f"Failed to free memory at {address}: {str(e)}") def write_memory(self, address, data, chip_id=0): """Write data to allocated memory""" if not self.initialized: raise RuntimeError("Driver not initialized.") try: # Handle tensor writes tensor_info = self.memory_manager.get_tensor_info(address) if tensor_info: # Validate data shape matches tensor shape data_arr = np.asarray(data) if data_arr.shape != tensor_info['shape']: raise ValueError(f"Data shape {data_arr.shape} does not match tensor shape {tensor_info['shape']}") self.memory_manager.write_data(address, data_arr.tobytes(), chip_id) return # Regular memory write if isinstance(data, (bytes, bytearray)): self.memory_manager.write_data(address, data, chip_id) else: self.memory_manager.write_data(address, bytes(data), chip_id) except Exception as e: raise MemoryError(f"Failed to write memory at {address}: {str(e)}") def read_memory(self, address, size_bytes, chip_id=0): """Read data from allocated memory""" if not self.initialized: raise RuntimeError("Driver not initialized.") try: # Handle tensor reads tensor_info = self.memory_manager.get_tensor_info(address) if tensor_info: data = self.memory_manager.read_data(address, size_bytes, chip_id) return np.frombuffer(data, dtype=tensor_info['dtype']).reshape(tensor_info['shape']) # Regular memory read return self.memory_manager.read_data(address, size_bytes, chip_id) except Exception as e: raise MemoryError(f"Failed to read memory at {address}: {str(e)}") def add_command(self, command_type, **kwargs): if not self.initialized: raise RuntimeError("Driver not initialized.") self.command_processor.add_command(command_type, **kwargs) def submit_commands(self, chip_id=0): if not self.initialized: raise RuntimeError("Driver not initialized.") return self.command_processor.submit_commands(chip_id) def clear_commands(self): if not self.initialized: raise RuntimeError("Driver not initialized.") self.command_processor.clear_commands() def create_memory_pool(self, size_bytes: int, shared: bool = False) -> str: """Create a new memory pool""" if not self.initialized: raise RuntimeError("Driver not initialized.") try: pool_id = f"pool_{len(self.memory_pools)}" if shared: pool = SharedMemoryPool(size_bytes) self.shared_pools[pool_id] = pool else: pool = MemoryPool(size_bytes) self.memory_pools[pool_id] = pool return pool_id except Exception as e: raise MemoryError(f"Failed to create memory pool: {str(e)}") def allocate_from_pool(self, pool_id: str, size_bytes: int) -> int: """Allocate memory from a specific pool""" if not self.initialized: raise RuntimeError("Driver not initialized.") try: if pool_id in self.shared_pools: address = self.shared_pools[pool_id].atomic_allocate(size_bytes) elif pool_id in self.memory_pools: address = self.memory_pools[pool_id].allocate(size_bytes) else: raise ValueError(f"Invalid pool ID: {pool_id}") if address is None: raise MemoryError(f"Failed to allocate {size_bytes} bytes from pool {pool_id}") return address except Exception as e: raise MemoryError(f"Failed to allocate from pool: {str(e)}") def free_pool_memory(self, pool_id: str, address: int): """Free memory allocated from a pool""" if not self.initialized: raise RuntimeError("Driver not initialized.") try: if pool_id in self.shared_pools: self.shared_pools[pool_id].atomic_free(address) elif pool_id in self.memory_pools: self.memory_pools[pool_id].free(address) else: raise ValueError(f"Invalid pool ID: {pool_id}") except Exception as e: raise MemoryError(f"Failed to free pool memory: {str(e)}") def get_pool_stats(self, pool_id: str) -> dict: """Get statistics about a memory pool""" if not self.initialized: raise RuntimeError("Driver not initialized.") try: if pool_id in self.shared_pools: pool = self.shared_pools[pool_id] elif pool_id in self.memory_pools: pool = self.memory_pools[pool_id] else: raise ValueError(f"Invalid pool ID: {pool_id}") return { 'fragmentation': pool.get_fragmentation(), 'total_size': pool.total_size, 'is_shared': pool_id in self.shared_pools, } except Exception as e: raise MemoryError(f"Failed to get pool stats: {str(e)}") def delete_pool(self, pool_id: str): """Delete a memory pool""" if not self.initialized: raise RuntimeError("Driver not initialized.") try: if pool_id in self.shared_pools: del self.shared_pools[pool_id] elif pool_id in self.memory_pools: del self.memory_pools[pool_id] else: raise ValueError(f"Invalid pool ID: {pool_id}") except Exception as e: raise MemoryError(f"Failed to delete pool: {str(e)}") def execute_kernel(self, chip_id, sm_id, core_id, thread_block_config, kernel_func, *args, **kwargs): """ Execute a kernel function across multiple thread blocks. thread_block_config: { 'block_dim': (x, y, z), # threads per block 'grid_dim': (x, y, z), # blocks per grid 'shared_memory_size': int # bytes per block } """ if not self.initialized: raise RuntimeError("Driver not initialized") blocks_per_grid = ( thread_block_config['grid_dim'][0] * thread_block_config['grid_dim'][1] * thread_block_config['grid_dim'][2] ) threads_per_block = ( thread_block_config['block_dim'][0] * thread_block_config['block_dim'][1] * thread_block_config['block_dim'][2] ) total_threads = blocks_per_grid * threads_per_block if total_threads > self.hardware_config['threads_per_core']: raise ValueError(f"Total threads {total_threads} exceeds maximum threads per core {self.hardware_config['threads_per_core']}") self.add_command( "execute_kernel", chip_id=chip_id, sm_id=sm_id, core_id=core_id, thread_block_config=thread_block_config, kernel_func=kernel_func, args=args, kwargs=kwargs ) print(f"Added kernel execution command for Chip {chip_id}, SM {sm_id}, Core {core_id} " f"with {blocks_per_grid} blocks × {threads_per_block} threads = {total_threads} total threads") def matmul(self, chip_id, sm_id, A, B): self.add_command("matmul", chip_id=chip_id, sm_id=sm_id, A=A, B=B) print(f"Added matmul command for Chip {chip_id}, SM {sm_id}.") def block_barrier(self, chip_id, sm_id, core_id, block_id): """Synchronize all threads within a block""" self.add_command( "block_barrier", chip_id=chip_id, sm_id=sm_id, core_id=core_id, block_id=block_id ) def core_barrier(self, chip_id, sm_id, core_id): """Synchronize all threads within a core""" self.add_command( "core_barrier", chip_id=chip_id, sm_id=sm_id, core_id=core_id ) def global_barrier(self, chip_id=0): self.add_command("global_barrier", chip_id=chip_id) print(f"Added global barrier command for Chip {chip_id}.") def shared_memory_barrier(self, chip_id, sm_id): self.add_command("shared_memory_barrier", chip_id=chip_id, sm_id=sm_id) print(f"Added shared memory barrier command for Chip {chip_id}, SM {sm_id}.") def atomic_operation(self, chip_id, sm_id, address, operation, value): self.add_command("atomic_operation", chip_id=chip_id, sm_id=sm_id, address=address, operation=operation, value=value) print(f"Added atomic operation command for Chip {chip_id}, SM {sm_id}.") # Expose Graphics API methods through the driver def create_buffer(self, size_bytes, buffer_type="vertex"): return self.graphics_api.create_buffer(size_bytes, buffer_type) def delete_buffer(self, buffer_id): self.graphics_api.delete_buffer(buffer_id) def buffer_data(self, buffer_id, data): self.graphics_api.buffer_data(buffer_id, data) def draw_arrays(self, mode, first, count): self.graphics_api.draw_arrays(mode, first, count) def draw_indexed(self, mode, count, index_buffer_id, index_offset=0): self.graphics_api.draw_indexed(mode, count, index_buffer_id, index_offset) def compile_shader(self, shader_source, shader_type="vertex"): return self.graphics_api.compile_shader(shader_source, shader_type) def use_program(self, program_id): self.graphics_api.use_program(program_id) def create_framebuffer(self, width, height): return self.graphics_api.create_framebuffer(width, height) def bind_framebuffer(self, framebuffer_id): self.graphics_api.bind_framebuffer(framebuffer_id) def clear_color(self, r, g, b, a): self.graphics_api.clear_color(r, g, b, a) def clear_depth(self, depth): self.graphics_api.clear_depth(depth)