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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)
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