#!/usr/bin/env python3 """ CUDA Integration for ZK Circuit Acceleration Python wrapper for GPU-accelerated field operations and constraint verification """ import ctypes import numpy as np from typing import List, Tuple, Optional import os import sys # Field element structure (256-bit for bn128 curve) class FieldElement(ctypes.Structure): _fields_ = [("limbs", ctypes.c_uint64 * 4)] # Constraint structure for parallel processing class Constraint(ctypes.Structure): _fields_ = [ ("a", FieldElement), ("b", FieldElement), ("c", FieldElement), ("operation", ctypes.c_uint8) # 0: a + b = c, 1: a * b = c ] class CUDAZKAccelerator: """Python interface for CUDA-accelerated ZK circuit operations""" def __init__(self, lib_path: str = None): """ Initialize CUDA accelerator Args: lib_path: Path to compiled CUDA library (.so file) """ self.lib_path = lib_path or self._find_cuda_lib() self.lib = None self.initialized = False try: self.lib = ctypes.CDLL(self.lib_path) self._setup_function_signatures() self.initialized = True print(f"✅ CUDA ZK Accelerator initialized: {self.lib_path}") except Exception as e: print(f"❌ Failed to initialize CUDA accelerator: {e}") self.initialized = False def _find_cuda_lib(self) -> str: """Find the compiled CUDA library""" # Look for library in common locations possible_paths = [ "./libfield_operations.so", "./field_operations.so", "../field_operations.so", "../../field_operations.so", "/usr/local/lib/libfield_operations.so" ] for path in possible_paths: if os.path.exists(path): return path raise FileNotFoundError("CUDA library not found. Please compile field_operations.cu first.") def _setup_function_signatures(self): """Setup function signatures for CUDA library functions""" if not self.lib: return # Initialize CUDA device self.lib.init_cuda_device.argtypes = [] self.lib.init_cuda_device.restype = ctypes.c_int # Field addition self.lib.gpu_field_addition.argtypes = [ np.ctypeslib.ndpointer(FieldElement, flags="C_CONTIGUOUS"), np.ctypeslib.ndpointer(FieldElement, flags="C_CONTIGUOUS"), np.ctypeslib.ndpointer(FieldElement, flags="C_CONTIGUOUS"), np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"), ctypes.c_int ] self.lib.gpu_field_addition.restype = ctypes.c_int # Constraint verification self.lib.gpu_constraint_verification.argtypes = [ np.ctypeslib.ndpointer(Constraint, flags="C_CONTIGUOUS"), np.ctypeslib.ndpointer(FieldElement, flags="C_CONTIGUOUS"), np.ctypeslib.ndpointer(ctypes.c_bool, flags="C_CONTIGUOUS"), ctypes.c_int ] self.lib.gpu_constraint_verification.restype = ctypes.c_int def init_device(self) -> bool: """Initialize CUDA device and check capabilities""" if not self.initialized: print("❌ CUDA accelerator not initialized") return False try: result = self.lib.init_cuda_device() if result == 0: print("✅ CUDA device initialized successfully") return True else: print(f"❌ CUDA device initialization failed: {result}") return False except Exception as e: print(f"❌ CUDA device initialization error: {e}") return False def field_addition( self, a: List[FieldElement], b: List[FieldElement], modulus: List[int] ) -> Tuple[bool, Optional[List[FieldElement]]]: """ Perform parallel field addition on GPU Args: a: First operand array b: Second operand array modulus: Field modulus (4 x 64-bit limbs) Returns: (success, result_array) """ if not self.initialized: return False, None try: num_elements = len(a) if num_elements != len(b): print("❌ Input arrays must have same length") return False, None # Convert to numpy arrays a_array = np.array(a, dtype=FieldElement) b_array = np.array(b, dtype=FieldElement) result_array = np.zeros(num_elements, dtype=FieldElement) modulus_array = np.array(modulus, dtype=ctypes.c_uint64) # Call GPU function result = self.lib.gpu_field_addition( a_array, b_array, result_array, modulus_array, num_elements ) if result == 0: print(f"✅ GPU field addition completed for {num_elements} elements") return True, result_array.tolist() else: print(f"❌ GPU field addition failed: {result}") return False, None except Exception as e: print(f"❌ GPU field addition error: {e}") return False, None def constraint_verification( self, constraints: List[Constraint], witness: List[FieldElement] ) -> Tuple[bool, Optional[List[bool]]]: """ Perform parallel constraint verification on GPU Args: constraints: Array of constraints to verify witness: Witness array Returns: (success, verification_results) """ if not self.initialized: return False, None try: num_constraints = len(constraints) # Convert to numpy arrays constraints_array = np.array(constraints, dtype=Constraint) witness_array = np.array(witness, dtype=FieldElement) results_array = np.zeros(num_constraints, dtype=ctypes.c_bool) # Call GPU function result = self.lib.gpu_constraint_verification( constraints_array, witness_array, results_array, num_constraints ) if result == 0: verified_count = np.sum(results_array) print(f"✅ GPU constraint verification: {verified_count}/{num_constraints} passed") return True, results_array.tolist() else: print(f"❌ GPU constraint verification failed: {result}") return False, None except Exception as e: print(f"❌ GPU constraint verification error: {e}") return False, None def benchmark_performance(self, num_elements: int = 10000) -> dict: """ Benchmark GPU vs CPU performance for field operations Args: num_elements: Number of elements to process Returns: Performance benchmark results """ if not self.initialized: return {"error": "CUDA accelerator not initialized"} print(f"🚀 Benchmarking GPU performance with {num_elements} elements...") # Generate test data a_elements = [] b_elements = [] for i in range(num_elements): a = FieldElement() b = FieldElement() # Fill with test values for j in range(4): a.limbs[j] = (i + j) % (2**32) b.limbs[j] = (i * 2 + j) % (2**32) a_elements.append(a) b_elements.append(b) # bn128 field modulus (simplified) modulus = [0xFFFFFFFFFFFFFFFF, 0xFFFFFFFFFFFFFFFF, 0xFFFFFFFFFFFFFFFF, 0xFFFFFFFFFFFFFFFF] # GPU benchmark import time start_time = time.time() success, gpu_result = self.field_addition(a_elements, b_elements, modulus) gpu_time = time.time() - start_time # CPU benchmark (simplified) start_time = time.time() # Simple CPU field addition cpu_result = [] for i in range(num_elements): c = FieldElement() for j in range(4): c.limbs[j] = (a_elements[i].limbs[j] + b_elements[i].limbs[j]) % modulus[j] cpu_result.append(c) cpu_time = time.time() - start_time # Calculate speedup speedup = cpu_time / gpu_time if gpu_time > 0 else 0 results = { "num_elements": num_elements, "gpu_time": gpu_time, "cpu_time": cpu_time, "speedup": speedup, "gpu_success": success, "elements_per_second_gpu": num_elements / gpu_time if gpu_time > 0 else 0, "elements_per_second_cpu": num_elements / cpu_time if cpu_time > 0 else 0 } print(f"📊 Benchmark Results:") print(f" GPU Time: {gpu_time:.4f}s") print(f" CPU Time: {cpu_time:.4f}s") print(f" Speedup: {speedup:.2f}x") print(f" GPU Throughput: {results['elements_per_second_gpu']:.0f} elements/s") return results def main(): """Main function for testing CUDA acceleration""" print("🚀 AITBC CUDA ZK Accelerator Test") print("=" * 50) try: # Initialize accelerator accelerator = CUDAZKAccelerator() if not accelerator.initialized: print("❌ Failed to initialize CUDA accelerator") print("💡 Please compile field_operations.cu first:") print(" nvcc -shared -o libfield_operations.so field_operations.cu") return # Initialize device if not accelerator.init_device(): return # Run benchmark results = accelerator.benchmark_performance(10000) if "error" not in results: print("\n✅ CUDA acceleration test completed successfully!") print(f"🚀 Achieved {results['speedup']:.2f}x speedup") else: print(f"❌ Benchmark failed: {results['error']}") except Exception as e: print(f"❌ Test failed: {e}") if __name__ == "__main__": main()