#!/usr/bin/env python3 """ High-Performance CUDA ZK Accelerator with Optimized Kernels Implements optimized CUDA kernels with memory coalescing, vectorization, and shared memory """ import ctypes import numpy as np from typing import List, Tuple, Optional import os import sys import time # Optimized field element structure for flat array access class OptimizedFieldElement(ctypes.Structure): _fields_ = [("limbs", ctypes.c_uint64 * 4)] class HighPerformanceCUDAZKAccelerator: """High-performance Python interface for optimized CUDA ZK operations""" def __init__(self, lib_path: str = None): """ Initialize high-performance CUDA accelerator Args: lib_path: Path to compiled optimized CUDA library (.so file) """ self.lib_path = lib_path or self._find_optimized_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"✅ High-Performance CUDA ZK Accelerator initialized: {self.lib_path}") except Exception as e: print(f"❌ Failed to initialize CUDA accelerator: {e}") self.initialized = False def _find_optimized_cuda_lib(self) -> str: """Find the compiled optimized 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" ] for path in possible_paths: if os.path.exists(path): return path raise FileNotFoundError("Optimized CUDA library not found. Please compile optimized_field_operations.cu first.") def _setup_function_signatures(self): """Setup function signatures for optimized CUDA library functions""" if not self.lib: return # Initialize optimized CUDA device self.lib.init_optimized_cuda_device.argtypes = [] self.lib.init_optimized_cuda_device.restype = ctypes.c_int # Optimized field addition with flat arrays self.lib.gpu_optimized_field_addition.argtypes = [ np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"), np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"), np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"), np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"), ctypes.c_int ] self.lib.gpu_optimized_field_addition.restype = ctypes.c_int # Vectorized field addition self.lib.gpu_vectorized_field_addition.argtypes = [ np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"), # field_vector_t np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"), np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"), np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"), ctypes.c_int ] self.lib.gpu_vectorized_field_addition.restype = ctypes.c_int # Shared memory field addition self.lib.gpu_shared_memory_field_addition.argtypes = [ np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"), np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"), np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"), np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"), ctypes.c_int ] self.lib.gpu_shared_memory_field_addition.restype = ctypes.c_int def init_device(self) -> bool: """Initialize optimized CUDA device and check capabilities""" if not self.initialized: print("❌ CUDA accelerator not initialized") return False try: result = self.lib.init_optimized_cuda_device() if result == 0: print("✅ Optimized 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 benchmark_optimized_kernels(self, max_elements: int = 10000000) -> dict: """ Benchmark all optimized CUDA kernels and compare performance Args: max_elements: Maximum number of elements to test Returns: Comprehensive performance benchmark results """ if not self.initialized: return {"error": "CUDA accelerator not initialized"} print(f"🚀 High-Performance CUDA Kernel Benchmark (up to {max_elements:,} elements)") print("=" * 80) # Test different dataset sizes test_sizes = [ 1000, # 1K elements 10000, # 10K elements 100000, # 100K elements 1000000, # 1M elements 5000000, # 5M elements 10000000, # 10M elements ] results = { "test_sizes": [], "optimized_flat": [], "vectorized": [], "shared_memory": [], "cpu_baseline": [], "performance_summary": {} } for size in test_sizes: if size > max_elements: break print(f"\n📊 Benchmarking {size:,} elements...") # Generate test data as flat arrays for optimal memory access a_flat, b_flat = self._generate_flat_test_data(size) # bn128 field modulus (simplified) modulus = [0xFFFFFFFFFFFFFFFF, 0xFFFFFFFFFFFFFFFF, 0xFFFFFFFFFFFFFFFF, 0xFFFFFFFFFFFFFFFF] # Benchmark optimized flat array kernel flat_result = self._benchmark_optimized_flat_kernel(a_flat, b_flat, modulus, size) # Benchmark vectorized kernel vec_result = self._benchmark_vectorized_kernel(a_flat, b_flat, modulus, size) # Benchmark shared memory kernel shared_result = self._benchmark_shared_memory_kernel(a_flat, b_flat, modulus, size) # Benchmark CPU baseline cpu_result = self._benchmark_cpu_baseline(a_flat, b_flat, modulus, size) # Store results results["test_sizes"].append(size) results["optimized_flat"].append(flat_result) results["vectorized"].append(vec_result) results["shared_memory"].append(shared_result) results["cpu_baseline"].append(cpu_result) # Print comparison print(f" Optimized Flat: {flat_result['time']:.4f}s, {flat_result['throughput']:.0f} elem/s") print(f" Vectorized: {vec_result['time']:.4f}s, {vec_result['throughput']:.0f} elem/s") print(f" Shared Memory: {shared_result['time']:.4f}s, {shared_result['throughput']:.0f} elem/s") print(f" CPU Baseline: {cpu_result['time']:.4f}s, {cpu_result['throughput']:.0f} elem/s") # Calculate speedups flat_speedup = cpu_result['time'] / flat_result['time'] if flat_result['time'] > 0 else 0 vec_speedup = cpu_result['time'] / vec_result['time'] if vec_result['time'] > 0 else 0 shared_speedup = cpu_result['time'] / shared_result['time'] if shared_result['time'] > 0 else 0 print(f" Speedups - Flat: {flat_speedup:.2f}x, Vec: {vec_speedup:.2f}x, Shared: {shared_speedup:.2f}x") # Calculate performance summary results["performance_summary"] = self._calculate_performance_summary(results) # Print final summary self._print_performance_summary(results["performance_summary"]) return results def _benchmark_optimized_flat_kernel(self, a_flat: np.ndarray, b_flat: np.ndarray, modulus: List[int], num_elements: int) -> dict: """Benchmark optimized flat array kernel""" try: result_flat = np.zeros_like(a_flat) modulus_array = np.array(modulus, dtype=np.uint64) # Multiple runs for consistency times = [] for run in range(3): start_time = time.time() success = self.lib.gpu_optimized_field_addition( a_flat, b_flat, result_flat, modulus_array, num_elements ) run_time = time.time() - start_time if success == 0: # Success times.append(run_time) if not times: return {"time": float('inf'), "throughput": 0, "success": False} avg_time = sum(times) / len(times) throughput = num_elements / avg_time if avg_time > 0 else 0 return {"time": avg_time, "throughput": throughput, "success": True} except Exception as e: print(f" ❌ Optimized flat kernel error: {e}") return {"time": float('inf'), "throughput": 0, "success": False} def _benchmark_vectorized_kernel(self, a_flat: np.ndarray, b_flat: np.ndarray, modulus: List[int], num_elements: int) -> dict: """Benchmark vectorized kernel""" try: # Convert flat arrays to vectorized format (uint4) # For simplicity, we'll reuse the flat array kernel as vectorized # In practice, would convert to proper vector format result_flat = np.zeros_like(a_flat) modulus_array = np.array(modulus, dtype=np.uint64) times = [] for run in range(3): start_time = time.time() success = self.lib.gpu_vectorized_field_addition( a_flat, b_flat, result_flat, modulus_array, num_elements ) run_time = time.time() - start_time if success == 0: times.append(run_time) if not times: return {"time": float('inf'), "throughput": 0, "success": False} avg_time = sum(times) / len(times) throughput = num_elements / avg_time if avg_time > 0 else 0 return {"time": avg_time, "throughput": throughput, "success": True} except Exception as e: print(f" ❌ Vectorized kernel error: {e}") return {"time": float('inf'), "throughput": 0, "success": False} def _benchmark_shared_memory_kernel(self, a_flat: np.ndarray, b_flat: np.ndarray, modulus: List[int], num_elements: int) -> dict: """Benchmark shared memory kernel""" try: result_flat = np.zeros_like(a_flat) modulus_array = np.array(modulus, dtype=np.uint64) times = [] for run in range(3): start_time = time.time() success = self.lib.gpu_shared_memory_field_addition( a_flat, b_flat, result_flat, modulus_array, num_elements ) run_time = time.time() - start_time if success == 0: times.append(run_time) if not times: return {"time": float('inf'), "throughput": 0, "success": False} avg_time = sum(times) / len(times) throughput = num_elements / avg_time if avg_time > 0 else 0 return {"time": avg_time, "throughput": throughput, "success": True} except Exception as e: print(f" ❌ Shared memory kernel error: {e}") return {"time": float('inf'), "throughput": 0, "success": False} def _benchmark_cpu_baseline(self, a_flat: np.ndarray, b_flat: np.ndarray, modulus: List[int], num_elements: int) -> dict: """Benchmark CPU baseline for comparison""" try: start_time = time.time() # Simple CPU field addition result_flat = np.zeros_like(a_flat) for i in range(num_elements): base_idx = i * 4 for j in range(4): result_flat[base_idx + j] = (a_flat[base_idx + j] + b_flat[base_idx + j]) % modulus[j] cpu_time = time.time() - start_time throughput = num_elements / cpu_time if cpu_time > 0 else 0 return {"time": cpu_time, "throughput": throughput, "success": True} except Exception as e: print(f" ❌ CPU baseline error: {e}") return {"time": float('inf'), "throughput": 0, "success": False} def _generate_flat_test_data(self, num_elements: int) -> Tuple[np.ndarray, np.ndarray]: """Generate flat array test data for optimal memory access""" # Generate flat arrays (num_elements * 4 limbs) flat_size = num_elements * 4 # Use numpy for fast generation a_flat = np.random.randint(0, 2**32, size=flat_size, dtype=np.uint64) b_flat = np.random.randint(0, 2**32, size=flat_size, dtype=np.uint64) return a_flat, b_flat def _calculate_performance_summary(self, results: dict) -> dict: """Calculate performance summary statistics""" summary = {} # Find best performing kernel for each size best_speedups = [] best_throughputs = [] for i, size in enumerate(results["test_sizes"]): cpu_time = results["cpu_baseline"][i]["time"] # Calculate speedups flat_speedup = cpu_time / results["optimized_flat"][i]["time"] if results["optimized_flat"][i]["time"] > 0 else 0 vec_speedup = cpu_time / results["vectorized"][i]["time"] if results["vectorized"][i]["time"] > 0 else 0 shared_speedup = cpu_time / results["shared_memory"][i]["time"] if results["shared_memory"][i]["time"] > 0 else 0 best_speedup = max(flat_speedup, vec_speedup, shared_speedup) best_speedups.append(best_speedup) # Find best throughput best_throughput = max( results["optimized_flat"][i]["throughput"], results["vectorized"][i]["throughput"], results["shared_memory"][i]["throughput"] ) best_throughputs.append(best_throughput) if best_speedups: summary["best_speedup"] = max(best_speedups) summary["average_speedup"] = sum(best_speedups) / len(best_speedups) summary["best_speedup_size"] = results["test_sizes"][best_speedups.index(max(best_speedups))] if best_throughputs: summary["best_throughput"] = max(best_throughputs) summary["average_throughput"] = sum(best_throughputs) / len(best_throughputs) summary["best_throughput_size"] = results["test_sizes"][best_throughputs.index(max(best_throughputs))] return summary def _print_performance_summary(self, summary: dict): """Print comprehensive performance summary""" print(f"\n🎯 High-Performance CUDA Summary:") print("=" * 50) if "best_speedup" in summary: print(f" Best Speedup: {summary['best_speedup']:.2f}x at {summary.get('best_speedup_size', 'N/A'):,} elements") print(f" Average Speedup: {summary['average_speedup']:.2f}x across all tests") if "best_throughput" in summary: print(f" Best Throughput: {summary['best_throughput']:.0f} elements/s at {summary.get('best_throughput_size', 'N/A'):,} elements") print(f" Average Throughput: {summary['average_throughput']:.0f} elements/s") # Performance classification if summary.get("best_speedup", 0) > 5: print(" 🚀 Performance: EXCELLENT - Significant GPU acceleration achieved") elif summary.get("best_speedup", 0) > 2: print(" ✅ Performance: GOOD - Measurable GPU acceleration achieved") elif summary.get("best_speedup", 0) > 1: print(" ⚠️ Performance: MODERATE - Limited GPU acceleration") else: print(" ❌ Performance: POOR - No significant GPU acceleration") def analyze_memory_bandwidth(self, num_elements: int = 1000000) -> dict: """Analyze memory bandwidth performance""" print(f"🔍 Analyzing Memory Bandwidth Performance ({num_elements:,} elements)...") a_flat, b_flat = self._generate_flat_test_data(num_elements) modulus = [0xFFFFFFFFFFFFFFFF] * 4 # Test different kernels flat_result = self._benchmark_optimized_flat_kernel(a_flat, b_flat, modulus, num_elements) vec_result = self._benchmark_vectorized_kernel(a_flat, b_flat, modulus, num_elements) shared_result = self._benchmark_shared_memory_kernel(a_flat, b_flat, modulus, num_elements) # Calculate theoretical bandwidth data_size = num_elements * 4 * 8 * 3 # 3 arrays, 4 limbs, 8 bytes analysis = { "data_size_gb": data_size / (1024**3), "flat_bandwidth_gb_s": data_size / (flat_result['time'] * 1024**3) if flat_result['time'] > 0 else 0, "vectorized_bandwidth_gb_s": data_size / (vec_result['time'] * 1024**3) if vec_result['time'] > 0 else 0, "shared_bandwidth_gb_s": data_size / (shared_result['time'] * 1024**3) if shared_result['time'] > 0 else 0, } print(f" Data Size: {analysis['data_size_gb']:.2f} GB") print(f" Flat Kernel: {analysis['flat_bandwidth_gb_s']:.2f} GB/s") print(f" Vectorized Kernel: {analysis['vectorized_bandwidth_gb_s']:.2f} GB/s") print(f" Shared Memory Kernel: {analysis['shared_bandwidth_gb_s']:.2f} GB/s") return analysis def main(): """Main function for testing high-performance CUDA acceleration""" print("🚀 AITBC High-Performance CUDA ZK Accelerator Test") print("=" * 60) try: # Initialize high-performance accelerator accelerator = HighPerformanceCUDAZKAccelerator() if not accelerator.initialized: print("❌ Failed to initialize CUDA accelerator") return # Initialize device if not accelerator.init_device(): return # Run comprehensive benchmark results = accelerator.benchmark_optimized_kernels(10000000) # Analyze memory bandwidth bandwidth_analysis = accelerator.analyze_memory_bandwidth(1000000) print("\n✅ High-Performance CUDA acceleration test completed!") if results.get("performance_summary", {}).get("best_speedup", 0) > 1: print(f"🚀 Optimization successful: {results['performance_summary']['best_speedup']:.2f}x speedup achieved") else: print("⚠️ Further optimization needed") except Exception as e: print(f"❌ Test failed: {e}") if __name__ == "__main__": main()