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from dataclasses import dataclass
from typing import List, Dict, Any, Callable
import numpy as np
from threading import Lock
@dataclass
class KernelConfig:
"""Configuration for a CUDA-like kernel launch"""
block_dim: tuple[int, int, int] # threads per block (x,y,z)
grid_dim: tuple[int, int, int] # blocks per grid (x,y,z)
shared_memory_size: int = 0 # bytes of shared memory per block
class ThreadIdx:
"""Thread index within a block"""
def __init__(self, x: int, y: int, z: int):
self.x = x
self.y = y
self.z = z
class BlockIdx:
"""Block index within the grid"""
def __init__(self, x: int, y: int, z: int):
self.x = x
self.y = y
self.z = z
class Warp:
"""Represents a group of 32 threads that execute in lockstep"""
WARP_SIZE = 32
def __init__(self, warp_id: int, threads: List[ThreadIdx]):
self.warp_id = warp_id
self.threads = threads
self.active_mask = (1 << len(threads)) - 1 # All threads active initially
def synchronize(self):
"""Synchronize all threads in the warp"""
pass # Hardware handled in real GPU
def vote_all(self, predicate: bool) -> bool:
"""Returns true if predicate is true for all active threads"""
return all(predicate for _ in range(len(self.threads)))
def vote_any(self, predicate: bool) -> bool:
"""Returns true if predicate is true for any active thread"""
return any(predicate for _ in range(len(self.threads)))
class Block:
"""Represents a thread block with shared memory"""
def __init__(self, block_idx: BlockIdx, dim: tuple[int, int, int], shared_mem_size: int):
self.block_idx = block_idx
self.dim = dim
self.shared_memory = SharedMemory(shared_mem_size)
self.warps: List[Warp] = []
self._create_warps()
def _create_warps(self):
"""Organize threads into warps"""
threads = []
total_threads = self.dim[0] * self.dim[1] * self.dim[2]
for idx in range(total_threads):
# Convert linear index to 3D
z = idx // (self.dim[0] * self.dim[1])
y = (idx % (self.dim[0] * self.dim[1])) // self.dim[0]
x = idx % self.dim[0]
threads.append(ThreadIdx(x, y, z))
if len(threads) == Warp.WARP_SIZE or idx == total_threads - 1:
self.warps.append(Warp(len(self.warps), threads))
threads = []
def synchronize(self):
"""Synchronize all threads in the block"""
for warp in self.warps:
warp.synchronize()
class SharedMemory:
"""Represents shared memory accessible by all threads in a block"""
def __init__(self, size_bytes: int):
self.size = size_bytes
self.data = bytearray(size_bytes)
self.lock = Lock()
def read(self, offset: int, size: int) -> bytearray:
with self.lock:
return self.data[offset:offset + size]
def write(self, offset: int, data: bytearray):
with self.lock:
self.data[offset:offset + len(data)] = data
class KernelFunction:
"""Wrapper for a kernel function"""
def __init__(self, func: Callable):
self.func = func
self.shared_memory_size = 0
def configure(self, shared_memory_size: int = 0):
"""Configure kernel properties"""
self.shared_memory_size = shared_memory_size
return self
def __call__(self, *args, **kwargs):
"""Execute the kernel function"""
return self.func(*args, **kwargs)
def launch_kernel(kernel_func: KernelFunction, config: KernelConfig, *args):
"""Launch a kernel with the specified configuration"""
total_blocks = config.grid_dim[0] * config.grid_dim[1] * config.grid_dim[2]
# Create blocks
blocks = []
for block_idx in range(total_blocks):
# Convert linear index to 3D
bz = block_idx // (config.grid_dim[0] * config.grid_dim[1])
by = (block_idx % (config.grid_dim[0] * config.grid_dim[1])) // config.grid_dim[0]
bx = block_idx % config.grid_dim[0]
block = Block(
BlockIdx(bx, by, bz),
config.block_dim,
config.shared_memory_size
)
blocks.append(block)
# Execute kernel on each block
for block in blocks:
for warp in block.warps:
for thread in warp.threads:
kernel_func(block, thread, *args)
def kernel(func: Callable) -> KernelFunction:
"""Decorator to mark a function as a kernel"""
return KernelFunction(func)
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