""" CUDA Compute Provider Implementation This module implements the ComputeProvider interface for NVIDIA CUDA GPUs, providing optimized CUDA operations for ZK circuit acceleration. """ import ctypes import numpy as np from typing import Dict, List, Optional, Any, Tuple import os import sys import time import logging from .compute_provider import ( ComputeProvider, ComputeDevice, ComputeBackend, ComputeTask, ComputeResult ) # Try to import CUDA libraries try: import pycuda.driver as cuda import pycuda.autoinit from pycuda.compiler import SourceModule CUDA_AVAILABLE = True except ImportError: CUDA_AVAILABLE = False cuda = None SourceModule = None # Configure logging logger = logging.getLogger(__name__) class CUDADevice(ComputeDevice): """CUDA-specific device information.""" def __init__(self, device_id: int, cuda_device): """Initialize CUDA device info.""" super().__init__( device_id=device_id, name=cuda_device.name().decode('utf-8'), backend=ComputeBackend.CUDA, memory_total=cuda_device.total_memory(), memory_available=cuda_device.total_memory(), # Will be updated compute_capability=f"{cuda_device.compute_capability()[0]}.{cuda_device.compute_capability()[1]}", is_available=True ) self.cuda_device = cuda_device self._update_memory_info() def _update_memory_info(self): """Update memory information.""" try: free_mem, total_mem = cuda.mem_get_info() self.memory_available = free_mem self.memory_total = total_mem except Exception: pass def update_utilization(self): """Update device utilization.""" try: # This would require nvidia-ml-py for real utilization # For now, we'll estimate based on memory usage self._update_memory_info() used_memory = self.memory_total - self.memory_available self.utilization = (used_memory / self.memory_total) * 100 except Exception: self.utilization = 0.0 def update_temperature(self): """Update device temperature.""" try: # This would require nvidia-ml-py for real temperature # For now, we'll set a reasonable default self.temperature = 65.0 # Typical GPU temperature except Exception: self.temperature = None class CUDAComputeProvider(ComputeProvider): """CUDA implementation of ComputeProvider.""" def __init__(self, lib_path: Optional[str] = None): """ Initialize CUDA compute provider. Args: lib_path: Path to compiled CUDA library """ self.lib_path = lib_path or self._find_cuda_lib() self.lib = None self.devices = [] self.current_device_id = 0 self.context = None self.initialized = False # CUDA-specific self.cuda_contexts = {} self.cuda_modules = {} if not CUDA_AVAILABLE: logger.warning("PyCUDA not available, CUDA provider will not work") return try: if self.lib_path: self.lib = ctypes.CDLL(self.lib_path) self._setup_function_signatures() # Initialize CUDA cuda.init() self._discover_devices() logger.info(f"CUDA Compute Provider initialized with {len(self.devices)} devices") except Exception as e: logger.error(f"Failed to initialize CUDA provider: {e}") def _find_cuda_lib(self) -> str: """Find the compiled CUDA library.""" possible_paths = [ "./liboptimized_field_operations.so", "./optimized_field_operations.so", "../liboptimized_field_operations.so", "../../liboptimized_field_operations.so", "/usr/local/lib/liboptimized_field_operations.so", os.path.join(os.path.dirname(__file__), "liboptimized_field_operations.so") ] for path in possible_paths: if os.path.exists(path): return path raise FileNotFoundError("CUDA library not found") def _setup_function_signatures(self): """Setup function signatures for the CUDA library.""" if not self.lib: return # Define function signatures self.lib.field_add.argtypes = [ ctypes.POINTER(ctypes.c_uint64), # a ctypes.POINTER(ctypes.c_uint64), # b ctypes.POINTER(ctypes.c_uint64), # result ctypes.c_int # count ] self.lib.field_add.restype = ctypes.c_int self.lib.field_mul.argtypes = [ ctypes.POINTER(ctypes.c_uint64), # a ctypes.POINTER(ctypes.c_uint64), # b ctypes.POINTER(ctypes.c_uint64), # result ctypes.c_int # count ] self.lib.field_mul.restype = ctypes.c_int self.lib.field_inverse.argtypes = [ ctypes.POINTER(ctypes.c_uint64), # a ctypes.POINTER(ctypes.c_uint64), # result ctypes.c_int # count ] self.lib.field_inverse.restype = ctypes.c_int self.lib.multi_scalar_mul.argtypes = [ ctypes.POINTER(ctypes.POINTER(ctypes.c_uint64)), # scalars ctypes.POINTER(ctypes.POINTER(ctypes.c_uint64)), # points ctypes.POINTER(ctypes.c_uint64), # result ctypes.c_int, # scalar_count ctypes.c_int # point_count ] self.lib.multi_scalar_mul.restype = ctypes.c_int def _discover_devices(self): """Discover available CUDA devices.""" self.devices = [] for i in range(cuda.Device.count()): try: cuda_device = cuda.Device(i) device = CUDADevice(i, cuda_device) self.devices.append(device) except Exception as e: logger.warning(f"Failed to initialize CUDA device {i}: {e}") def initialize(self) -> bool: """Initialize the CUDA provider.""" if not CUDA_AVAILABLE: logger.error("CUDA not available") return False try: # Create context for first device if self.devices: self.current_device_id = 0 self.context = self.devices[0].cuda_device.make_context() self.cuda_contexts[0] = self.context self.initialized = True return True else: logger.error("No CUDA devices available") return False except Exception as e: logger.error(f"CUDA initialization failed: {e}") return False def shutdown(self) -> None: """Shutdown the CUDA provider.""" try: # Clean up all contexts for context in self.cuda_contexts.values(): context.pop() self.cuda_contexts.clear() # Clean up modules self.cuda_modules.clear() self.initialized = False logger.info("CUDA provider shutdown complete") except Exception as e: logger.error(f"CUDA shutdown failed: {e}") def get_available_devices(self) -> List[ComputeDevice]: """Get list of available CUDA devices.""" return self.devices def get_device_count(self) -> int: """Get number of available CUDA devices.""" return len(self.devices) def set_device(self, device_id: int) -> bool: """Set the active CUDA device.""" if device_id >= len(self.devices): return False try: # Pop current context if self.context: self.context.pop() # Set new device and create context self.current_device_id = device_id device = self.devices[device_id] if device_id not in self.cuda_contexts: self.cuda_contexts[device_id] = device.cuda_device.make_context() self.context = self.cuda_contexts[device_id] self.context.push() return True except Exception as e: logger.error(f"Failed to set CUDA device {device_id}: {e}") return False def get_device_info(self, device_id: int) -> Optional[ComputeDevice]: """Get information about a specific CUDA device.""" if device_id < len(self.devices): device = self.devices[device_id] device.update_utilization() device.update_temperature() return device return None def allocate_memory(self, size: int, device_id: Optional[int] = None) -> Any: """Allocate memory on CUDA device.""" if not self.initialized: raise RuntimeError("CUDA provider not initialized") if device_id is not None and device_id != self.current_device_id: if not self.set_device(device_id): raise RuntimeError(f"Failed to set device {device_id}") return cuda.mem_alloc(size) def free_memory(self, memory_handle: Any) -> None: """Free allocated CUDA memory.""" try: memory_handle.free() except Exception as e: logger.warning(f"Failed to free CUDA memory: {e}") def copy_to_device(self, host_data: Any, device_data: Any) -> None: """Copy data from host to CUDA device.""" if not self.initialized: raise RuntimeError("CUDA provider not initialized") cuda.memcpy_htod(device_data, host_data) def copy_to_host(self, device_data: Any, host_data: Any) -> None: """Copy data from CUDA device to host.""" if not self.initialized: raise RuntimeError("CUDA provider not initialized") cuda.memcpy_dtoh(host_data, device_data) def execute_kernel( self, kernel_name: str, grid_size: Tuple[int, int, int], block_size: Tuple[int, int, int], args: List[Any], shared_memory: int = 0 ) -> bool: """Execute a CUDA kernel.""" if not self.initialized: return False try: # This would require loading compiled CUDA kernels # For now, we'll use the library functions if available if self.lib and hasattr(self.lib, kernel_name): # Convert args to ctypes c_args = [] for arg in args: if isinstance(arg, np.ndarray): c_args.append(arg.ctypes.data_as(ctypes.POINTER(ctypes.c_uint64))) else: c_args.append(arg) result = getattr(self.lib, kernel_name)(*c_args) return result == 0 # Assuming 0 means success # Fallback: try to use PyCUDA if kernel is loaded if kernel_name in self.cuda_modules: kernel = self.cuda_modules[kernel_name].get_function(kernel_name) kernel(*args, grid=grid_size, block=block_size, shared=shared_memory) return True return False except Exception as e: logger.error(f"Kernel execution failed: {e}") return False def synchronize(self) -> None: """Synchronize CUDA operations.""" if self.initialized: cuda.Context.synchronize() def get_memory_info(self, device_id: Optional[int] = None) -> Tuple[int, int]: """Get CUDA memory information.""" if device_id is not None and device_id != self.current_device_id: if not self.set_device(device_id): return (0, 0) try: free_mem, total_mem = cuda.mem_get_info() return (free_mem, total_mem) except Exception: return (0, 0) def get_utilization(self, device_id: Optional[int] = None) -> float: """Get CUDA device utilization.""" device = self.get_device_info(device_id or self.current_device_id) return device.utilization if device else 0.0 def get_temperature(self, device_id: Optional[int] = None) -> Optional[float]: """Get CUDA device temperature.""" device = self.get_device_info(device_id or self.current_device_id) return device.temperature if device else None # ZK-specific operations def zk_field_add(self, a: np.ndarray, b: np.ndarray, result: np.ndarray) -> bool: """Perform field addition using CUDA.""" if not self.lib or not self.initialized: return False try: # Allocate device memory a_dev = cuda.mem_alloc(a.nbytes) b_dev = cuda.mem_alloc(b.nbytes) result_dev = cuda.mem_alloc(result.nbytes) # Copy data to device cuda.memcpy_htod(a_dev, a) cuda.memcpy_htod(b_dev, b) # Execute kernel success = self.lib.field_add( a_dev, b_dev, result_dev, len(a) ) == 0 if success: # Copy result back cuda.memcpy_dtoh(result, result_dev) # Clean up a_dev.free() b_dev.free() result_dev.free() return success except Exception as e: logger.error(f"CUDA field add failed: {e}") return False def zk_field_mul(self, a: np.ndarray, b: np.ndarray, result: np.ndarray) -> bool: """Perform field multiplication using CUDA.""" if not self.lib or not self.initialized: return False try: # Allocate device memory a_dev = cuda.mem_alloc(a.nbytes) b_dev = cuda.mem_alloc(b.nbytes) result_dev = cuda.mem_alloc(result.nbytes) # Copy data to device cuda.memcpy_htod(a_dev, a) cuda.memcpy_htod(b_dev, b) # Execute kernel success = self.lib.field_mul( a_dev, b_dev, result_dev, len(a) ) == 0 if success: # Copy result back cuda.memcpy_dtoh(result, result_dev) # Clean up a_dev.free() b_dev.free() result_dev.free() return success except Exception as e: logger.error(f"CUDA field mul failed: {e}") return False def zk_field_inverse(self, a: np.ndarray, result: np.ndarray) -> bool: """Perform field inversion using CUDA.""" if not self.lib or not self.initialized: return False try: # Allocate device memory a_dev = cuda.mem_alloc(a.nbytes) result_dev = cuda.mem_alloc(result.nbytes) # Copy data to device cuda.memcpy_htod(a_dev, a) # Execute kernel success = self.lib.field_inverse( a_dev, result_dev, len(a) ) == 0 if success: # Copy result back cuda.memcpy_dtoh(result, result_dev) # Clean up a_dev.free() result_dev.free() return success except Exception as e: logger.error(f"CUDA field inverse failed: {e}") return False def zk_multi_scalar_mul( self, scalars: List[np.ndarray], points: List[np.ndarray], result: np.ndarray ) -> bool: """Perform multi-scalar multiplication using CUDA.""" if not self.lib or not self.initialized: return False try: # This is a simplified implementation # In practice, this would require more complex memory management scalar_count = len(scalars) point_count = len(points) # Allocate device memory for all scalars and points scalar_ptrs = [] point_ptrs = [] for scalar in scalars: scalar_dev = cuda.mem_alloc(scalar.nbytes) cuda.memcpy_htod(scalar_dev, scalar) scalar_ptrs.append(ctypes.c_void_p(int(scalar_dev))) for point in points: point_dev = cuda.mem_alloc(point.nbytes) cuda.memcpy_htod(point_dev, point) point_ptrs.append(ctypes.c_void_p(int(point_dev))) result_dev = cuda.mem_alloc(result.nbytes) # Execute kernel success = self.lib.multi_scalar_mul( (ctypes.POINTER(ctypes.c_void64) * scalar_count)(*scalar_ptrs), (ctypes.POINTER(ctypes.c_void64) * point_count)(*point_ptrs), result_dev, scalar_count, point_count ) == 0 if success: # Copy result back cuda.memcpy_dtoh(result, result_dev) # Clean up for scalar_dev in [ptr for ptr in scalar_ptrs]: cuda.mem_free(ptr) for point_dev in [ptr for ptr in point_ptrs]: cuda.mem_free(ptr) result_dev.free() return success except Exception as e: logger.error(f"CUDA multi-scalar mul failed: {e}") return False def zk_pairing(self, p1: np.ndarray, p2: np.ndarray, result: np.ndarray) -> bool: """Perform pairing operation using CUDA.""" # This would require a specific pairing implementation # For now, return False as not implemented logger.warning("CUDA pairing operation not implemented") return False # Performance and monitoring def benchmark_operation(self, operation: str, iterations: int = 100) -> Dict[str, float]: """Benchmark a CUDA operation.""" if not self.initialized: return {"error": "CUDA provider not initialized"} try: # Create test data test_size = 1024 a = np.random.randint(0, 2**32, size=test_size, dtype=np.uint64) b = np.random.randint(0, 2**32, size=test_size, dtype=np.uint64) result = np.zeros_like(a) # Warm up if operation == "add": self.zk_field_add(a, b, result) elif operation == "mul": self.zk_field_mul(a, b, result) # Benchmark start_time = time.time() for _ in range(iterations): if operation == "add": self.zk_field_add(a, b, result) elif operation == "mul": self.zk_field_mul(a, b, result) end_time = time.time() total_time = end_time - start_time avg_time = total_time / iterations ops_per_second = iterations / total_time return { "total_time": total_time, "average_time": avg_time, "operations_per_second": ops_per_second, "iterations": iterations } except Exception as e: return {"error": str(e)} def get_performance_metrics(self) -> Dict[str, Any]: """Get CUDA performance metrics.""" if not self.initialized: return {"error": "CUDA provider not initialized"} try: free_mem, total_mem = self.get_memory_info() utilization = self.get_utilization() temperature = self.get_temperature() return { "backend": "cuda", "device_count": len(self.devices), "current_device": self.current_device_id, "memory": { "free": free_mem, "total": total_mem, "used": total_mem - free_mem, "utilization": ((total_mem - free_mem) / total_mem) * 100 }, "utilization": utilization, "temperature": temperature, "devices": [ { "id": device.device_id, "name": device.name, "memory_total": device.memory_total, "compute_capability": device.compute_capability, "utilization": device.utilization, "temperature": device.temperature } for device in self.devices ] } except Exception as e: return {"error": str(e)} # Register the CUDA provider from .compute_provider import ComputeProviderFactory ComputeProviderFactory.register_provider(ComputeBackend.CUDA, CUDAComputeProvider)