Update Python version requirements and fix compatibility issues

- Bump minimum Python version from 3.11 to 3.13 across all apps
- Add Python 3.11-3.13 test matrix to CLI workflow
- Document Python 3.11+ requirement in .env.example
- Fix Starlette Broadcast removal with in-process fallback implementation
- Add _InProcessBroadcast class for tests when Starlette Broadcast is unavailable
- Refactor API key validators to read live settings instead of cached values
- Update database models with explicit

gpu_acceleration/cuda_kernels/cuda_zk_accelerator.py

@@ -0,0 +1,311 @@
+#!/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()

gpu_acceleration/cuda_kernels/field_operations.cu

@@ -0,0 +1,330 @@
+/**
+ * CUDA Kernel for ZK Circuit Field Operations
+ * 
+ * Implements GPU-accelerated field arithmetic for zero-knowledge proof generation
+ * focusing on parallel processing of large constraint systems and witness calculations.
+ */
+
+#include <cuda_runtime.h>
+#include <curand_kernel.h>
+#include <device_launch_parameters.h>
+#include <stdint.h>
+#include <stdio.h>
+
+// Custom 128-bit integer type for CUDA compatibility
+typedef unsigned long long uint128_t __attribute__((mode(TI)));
+
+// Field element structure (256-bit for bn128 curve)
+typedef struct {
+    uint64_t limbs[4];  // 4 x 64-bit limbs for 256-bit field element
+} field_element_t;
+
+// Constraint structure for parallel processing
+typedef struct {
+    field_element_t a;
+    field_element_t b;
+    field_element_t c;
+    uint8_t operation;  // 0: a + b = c, 1: a * b = c
+} constraint_t;
+
+// CUDA kernel for parallel field addition
+__global__ void field_addition_kernel(
+    const field_element_t* a,
+    const field_element_t* b,
+    field_element_t* result,
+    const uint64_t modulus[4],
+    int num_elements
+) {
+    int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    
+    if (idx < num_elements) {
+        // Perform field addition with modulus reduction
+        uint64_t carry = 0;
+        
+        for (int i = 0; i < 4; i++) {
+            uint128_t sum = (uint128_t)a[idx].limbs[i] + b[idx].limbs[i] + carry;
+            result[idx].limbs[i] = (uint64_t)sum;
+            carry = sum >> 64;
+        }
+        
+        // Modulus reduction if needed
+        uint128_t reduction = 0;
+        for (int i = 0; i < 4; i++) {
+            if (result[idx].limbs[i] >= modulus[i]) {
+                reduction = 1;
+                break;
+            }
+        }
+        
+        if (reduction) {
+            carry = 0;
+            for (int i = 0; i < 4; i++) {
+                uint128_t diff = (uint128_t)result[idx].limbs[i] - modulus[i] - carry;
+                result[idx].limbs[i] = (uint64_t)diff;
+                carry = diff >> 63; // Borrow
+            }
+        }
+    }
+}
+
+// CUDA kernel for parallel field multiplication
+__global__ void field_multiplication_kernel(
+    const field_element_t* a,
+    const field_element_t* b,
+    field_element_t* result,
+    const uint64_t modulus[4],
+    int num_elements
+) {
+    int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    
+    if (idx < num_elements) {
+        // Perform schoolbook multiplication with modulus reduction
+        uint64_t product[8] = {0};  // Intermediate product (512 bits)
+        
+        // Multiply all limbs
+        for (int i = 0; i < 4; i++) {
+            uint64_t carry = 0;
+            for (int j = 0; j < 4; j++) {
+                uint128_t partial = (uint128_t)a[idx].limbs[i] * b[idx].limbs[j] + product[i + j] + carry;
+                product[i + j] = (uint64_t)partial;
+                carry = partial >> 64;
+            }
+            product[i + 4] = carry;
+        }
+        
+        // Montgomery reduction (simplified for demonstration)
+        // In practice, would use proper Montgomery reduction algorithm
+        for (int i = 0; i < 4; i++) {
+            result[idx].limbs[i] = product[i];  // Simplified - needs proper reduction
+        }
+    }
+}
+
+// CUDA kernel for parallel constraint verification
+__global__ void constraint_verification_kernel(
+    const constraint_t* constraints,
+    const field_element_t* witness,
+    bool* results,
+    int num_constraints
+) {
+    int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    
+    if (idx < num_constraints) {
+        const constraint_t* c = &constraints[idx];
+        field_element_t computed;
+        
+        if (c->operation == 0) {
+            // Addition constraint: a + b = c
+            // Simplified field addition
+            uint64_t carry = 0;
+            for (int i = 0; i < 4; i++) {
+                uint128_t sum = (uint128_t)c->a.limbs[i] + c->b.limbs[i] + carry;
+                computed.limbs[i] = (uint64_t)sum;
+                carry = sum >> 64;
+            }
+        } else {
+            // Multiplication constraint: a * b = c
+            // Simplified field multiplication
+            computed.limbs[0] = c->a.limbs[0] * c->b.limbs[0];  // Simplified
+            computed.limbs[1] = 0;
+            computed.limbs[2] = 0;
+            computed.limbs[3] = 0;
+        }
+        
+        // Check if computed equals expected
+        bool equal = true;
+        for (int i = 0; i < 4; i++) {
+            if (computed.limbs[i] != c->c.limbs[i]) {
+                equal = false;
+                break;
+            }
+        }
+        
+        results[idx] = equal;
+    }
+}
+
+// CUDA kernel for parallel witness generation
+__global__ void witness_generation_kernel(
+    const field_element_t* inputs,
+    field_element_t* witness,
+    int num_inputs,
+    int witness_size
+) {
+    int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    
+    if (idx < num_inputs) {
+        // Copy inputs to witness
+        witness[idx] = inputs[idx];
+        
+        // Generate additional witness elements (simplified)
+        // In practice, would implement proper witness generation algorithm
+        for (int i = num_inputs; i < witness_size; i++) {
+            if (idx == 0) {  // Only first thread generates additional elements
+                // Simple linear combination (placeholder)
+                witness[i].limbs[0] = inputs[0].limbs[0] + i;
+                witness[i].limbs[1] = 0;
+                witness[i].limbs[2] = 0;
+                witness[i].limbs[3] = 0;
+            }
+        }
+    }
+}
+
+// Host wrapper functions
+extern "C" {
+
+// Initialize CUDA device and check capabilities
+cudaError_t init_cuda_device() {
+    int deviceCount = 0;
+    cudaError_t error = cudaGetDeviceCount(&deviceCount);
+    
+    if (error != cudaSuccess || deviceCount == 0) {
+        printf("No CUDA devices found\n");
+        return error;
+    }
+    
+    // Select first available device
+    error = cudaSetDevice(0);
+    if (error != cudaSuccess) {
+        printf("Failed to set CUDA device\n");
+        return error;
+    }
+    
+    // Get device properties
+    cudaDeviceProp prop;
+    error = cudaGetDeviceProperties(&prop, 0);
+    if (error == cudaSuccess) {
+        printf("CUDA Device: %s\n", prop.name);
+        printf("Compute Capability: %d.%d\n", prop.major, prop.minor);
+        printf("Global Memory: %zu MB\n", prop.totalGlobalMem / (1024 * 1024));
+        printf("Shared Memory per Block: %zu KB\n", prop.sharedMemPerBlock / 1024);
+        printf("Max Threads per Block: %d\n", prop.maxThreadsPerBlock);
+    }
+    
+    return error;
+}
+
+// Parallel field addition on GPU
+cudaError_t gpu_field_addition(
+    const field_element_t* a,
+    const field_element_t* b,
+    field_element_t* result,
+    const uint64_t modulus[4],
+    int num_elements
+) {
+    // Allocate device memory
+    field_element_t *d_a, *d_b, *d_result;
+    uint64_t *d_modulus;
+    
+    size_t field_size = num_elements * sizeof(field_element_t);
+    size_t modulus_size = 4 * sizeof(uint64_t);
+    
+    cudaError_t error = cudaMalloc(&d_a, field_size);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMalloc(&d_b, field_size);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMalloc(&d_result, field_size);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMalloc(&d_modulus, modulus_size);
+    if (error != cudaSuccess) return error;
+    
+    // Copy data to device
+    error = cudaMemcpy(d_a, a, field_size, cudaMemcpyHostToDevice);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMemcpy(d_b, b, field_size, cudaMemcpyHostToDevice);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMemcpy(d_modulus, modulus, modulus_size, cudaMemcpyHostToDevice);
+    if (error != cudaSuccess) return error;
+    
+    // Launch kernel
+    int threadsPerBlock = 256;
+    int blocksPerGrid = (num_elements + threadsPerBlock - 1) / threadsPerBlock;
+    
+    printf("Launching field addition kernel: %d blocks, %d threads per block\n", 
+           blocksPerGrid, threadsPerBlock);
+    
+    field_addition_kernel<<<blocksPerGrid, threadsPerBlock>>>(
+        d_a, d_b, d_result, d_modulus, num_elements
+    );
+    
+    // Check for kernel launch errors
+    error = cudaGetLastError();
+    if (error != cudaSuccess) return error;
+    
+    // Copy result back to host
+    error = cudaMemcpy(result, d_result, field_size, cudaMemcpyDeviceToHost);
+    
+    // Free device memory
+    cudaFree(d_a);
+    cudaFree(d_b);
+    cudaFree(d_result);
+    cudaFree(d_modulus);
+    
+    return error;
+}
+
+// Parallel constraint verification on GPU
+cudaError_t gpu_constraint_verification(
+    const constraint_t* constraints,
+    const field_element_t* witness,
+    bool* results,
+    int num_constraints
+) {
+    // Allocate device memory
+    constraint_t *d_constraints;
+    field_element_t *d_witness;
+    bool *d_results;
+    
+    size_t constraint_size = num_constraints * sizeof(constraint_t);
+    size_t witness_size = 1000 * sizeof(field_element_t);  // Assume witness size
+    size_t result_size = num_constraints * sizeof(bool);
+    
+    cudaError_t error = cudaMalloc(&d_constraints, constraint_size);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMalloc(&d_witness, witness_size);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMalloc(&d_results, result_size);
+    if (error != cudaSuccess) return error;
+    
+    // Copy data to device
+    error = cudaMemcpy(d_constraints, constraints, constraint_size, cudaMemcpyHostToDevice);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMemcpy(d_witness, witness, witness_size, cudaMemcpyHostToDevice);
+    if (error != cudaSuccess) return error;
+    
+    // Launch kernel
+    int threadsPerBlock = 256;
+    int blocksPerGrid = (num_constraints + threadsPerBlock - 1) / threadsPerBlock;
+    
+    printf("Launching constraint verification kernel: %d blocks, %d threads per block\n", 
+           blocksPerGrid, threadsPerBlock);
+    
+    constraint_verification_kernel<<<blocksPerGrid, threadsPerBlock>>>(
+        d_constraints, d_witness, d_results, num_constraints
+    );
+    
+    // Check for kernel launch errors
+    error = cudaGetLastError();
+    if (error != cudaSuccess) return error;
+    
+    // Copy result back to host
+    error = cudaMemcpy(results, d_results, result_size, cudaMemcpyDeviceToHost);
+    
+    // Free device memory
+    cudaFree(d_constraints);
+    cudaFree(d_witness);
+    cudaFree(d_results);
+    
+    return error;
+}
+
+} // extern "C"

gpu_acceleration/cuda_kernels/gpu_aware_compiler.py

@@ -0,0 +1,396 @@
+#!/usr/bin/env python3
+"""
+GPU-Aware ZK Circuit Compilation with Memory Optimization
+Implements GPU-aware compilation strategies and memory management for large circuits
+"""
+
+import os
+import json
+import time
+import hashlib
+import subprocess
+from typing import Dict, List, Optional, Tuple
+from pathlib import Path
+
+class GPUAwareCompiler:
+    """GPU-aware ZK circuit compiler with memory optimization"""
+    
+    def __init__(self, base_dir: str = None):
+        self.base_dir = Path(base_dir or "/home/oib/windsurf/aitbc/apps/zk-circuits")
+        self.cache_dir = Path("/tmp/zk_gpu_cache")
+        self.cache_dir.mkdir(exist_ok=True)
+        
+        # GPU memory configuration (RTX 4060 Ti: 16GB)
+        self.gpu_memory_config = {
+            "total_memory_mb": 16384,
+            "safe_memory_mb": 14336,  # Leave 2GB for system
+            "circuit_memory_per_constraint": 0.001,  # MB per constraint
+            "max_constraints_per_batch": 1000000  # 1M constraints per batch
+        }
+        
+        print(f"🚀 GPU-Aware Compiler initialized")
+        print(f"   Base directory: {self.base_dir}")
+        print(f"   Cache directory: {self.cache_dir}")
+        print(f"   GPU memory: {self.gpu_memory_config['total_memory_mb']}MB")
+    
+    def estimate_circuit_memory(self, circuit_path: str) -> Dict:
+        """
+        Estimate memory requirements for circuit compilation
+        
+        Args:
+            circuit_path: Path to circuit file
+            
+        Returns:
+            Memory estimation dictionary
+        """
+        circuit_file = Path(circuit_path)
+        
+        if not circuit_file.exists():
+            return {"error": "Circuit file not found"}
+        
+        # Parse circuit to estimate constraints
+        try:
+            with open(circuit_file, 'r') as f:
+                content = f.read()
+            
+            # Simple constraint estimation
+            constraint_count = content.count('<==') + content.count('===')
+            
+            # Estimate memory requirements
+            estimated_memory = constraint_count * self.gpu_memory_config["circuit_memory_per_constraint"]
+            
+            # Add overhead for compilation
+            compilation_overhead = estimated_memory * 2  # 2x for intermediate data
+            
+            total_memory_mb = estimated_memory + compilation_overhead
+            
+            return {
+                "circuit_path": str(circuit_file),
+                "estimated_constraints": constraint_count,
+                "estimated_memory_mb": total_memory_mb,
+                "compilation_overhead_mb": compilation_overhead,
+                "gpu_feasible": total_memory_mb < self.gpu_memory_config["safe_memory_mb"],
+                "recommended_batch_size": min(
+                    self.gpu_memory_config["max_constraints_per_batch"],
+                    int(self.gpu_memory_config["safe_memory_mb"] / self.gpu_memory_config["circuit_memory_per_constraint"])
+                )
+            }
+            
+        except Exception as e:
+            return {"error": f"Failed to parse circuit: {e}"}
+    
+    def compile_with_gpu_optimization(self, circuit_path: str, output_dir: str = None) -> Dict:
+        """
+        Compile circuit with GPU-aware memory optimization
+        
+        Args:
+            circuit_path: Path to circuit file
+            output_dir: Output directory for compiled artifacts
+            
+        Returns:
+            Compilation results
+        """
+        start_time = time.time()
+        
+        # Estimate memory requirements
+        memory_est = self.estimate_circuit_memory(circuit_path)
+        
+        if "error" in memory_est:
+            return memory_est
+        
+        print(f"🔧 Compiling {circuit_path}")
+        print(f"   Estimated constraints: {memory_est['estimated_constraints']}")
+        print(f"   Estimated memory: {memory_est['estimated_memory_mb']:.2f}MB")
+        
+        # Check GPU feasibility
+        if not memory_est["gpu_feasible"]:
+            print("⚠️  Circuit too large for GPU, using CPU compilation")
+            return self.compile_cpu_fallback(circuit_path, output_dir)
+        
+        # Create cache key
+        cache_key = self._create_cache_key(circuit_path)
+        cache_path = self.cache_dir / f"{cache_key}.json"
+        
+        # Check cache
+        if cache_path.exists():
+            cached_result = self._load_cache(cache_path)
+            if cached_result:
+                print("✅ Using cached compilation result")
+                cached_result["cache_hit"] = True
+                cached_result["compilation_time"] = time.time() - start_time
+                return cached_result
+        
+        # Perform GPU-aware compilation
+        try:
+            result = self._compile_circuit(circuit_path, output_dir, memory_est)
+            
+            # Cache result
+            self._save_cache(cache_path, result)
+            
+            result["compilation_time"] = time.time() - start_time
+            result["cache_hit"] = False
+            
+            print(f"✅ Compilation completed in {result['compilation_time']:.3f}s")
+            
+            return result
+            
+        except Exception as e:
+            print(f"❌ Compilation failed: {e}")
+            return {"error": str(e), "compilation_time": time.time() - start_time}
+    
+    def _compile_circuit(self, circuit_path: str, output_dir: str, memory_est: Dict) -> Dict:
+        """
+        Perform actual circuit compilation with GPU optimization
+        """
+        circuit_file = Path(circuit_path)
+        circuit_name = circuit_file.stem
+        
+        # Set output directory
+        if not output_dir:
+            output_dir = self.base_dir / "build" / circuit_name
+        else:
+            output_dir = Path(output_dir)
+        
+        output_dir.mkdir(parents=True, exist_ok=True)
+        
+        # Compile with Circom
+        cmd = [
+            "circom",
+            str(circuit_file),
+            "--r1cs",
+            "--wasm",
+            "-o", str(output_dir)
+        ]
+        
+        print(f"🔄 Running: {' '.join(cmd)}")
+        
+        result = subprocess.run(
+            cmd,
+            capture_output=True,
+            text=True,
+            cwd=str(self.base_dir)
+        )
+        
+        if result.returncode != 0:
+            return {
+                "error": "Circom compilation failed",
+                "stderr": result.stderr,
+                "stdout": result.stdout
+            }
+        
+        # Check compiled artifacts
+        r1cs_path = output_dir / f"{circuit_name}.r1cs"
+        wasm_path = output_dir / f"{circuit_name}_js" / f"{circuit_name}.wasm"
+        
+        artifacts = {}
+        if r1cs_path.exists():
+            artifacts["r1cs"] = str(r1cs_path)
+            r1cs_size = r1cs_path.stat().st_size / (1024 * 1024)  # MB
+            print(f"   R1CS size: {r1cs_size:.2f}MB")
+        
+        if wasm_path.exists():
+            artifacts["wasm"] = str(wasm_path)
+            wasm_size = wasm_path.stat().st_size / (1024 * 1024)  # MB
+            print(f"   WASM size: {wasm_size:.2f}MB")
+        
+        return {
+            "success": True,
+            "circuit_name": circuit_name,
+            "output_dir": str(output_dir),
+            "artifacts": artifacts,
+            "memory_estimation": memory_est,
+            "optimization_applied": "gpu_aware_memory"
+        }
+    
+    def compile_cpu_fallback(self, circuit_path: str, output_dir: str = None) -> Dict:
+        """Fallback CPU compilation for circuits too large for GPU"""
+        print("🔄 Using CPU fallback compilation")
+        
+        # Use standard circom compilation
+        return self._compile_circuit(circuit_path, output_dir, {"gpu_feasible": False})
+    
+    def batch_compile_optimized(self, circuit_paths: List[str]) -> Dict:
+        """
+        Compile multiple circuits with GPU memory optimization
+        
+        Args:
+            circuit_paths: List of circuit file paths
+            
+        Returns:
+            Batch compilation results
+        """
+        start_time = time.time()
+        
+        print(f"🚀 Batch compiling {len(circuit_paths)} circuits")
+        
+        # Estimate total memory requirements
+        total_memory = 0
+        memory_estimates = []
+        
+        for circuit_path in circuit_paths:
+            est = self.estimate_circuit_memory(circuit_path)
+            if "error" not in est:
+                total_memory += est["estimated_memory_mb"]
+                memory_estimates.append(est)
+        
+        print(f"   Total estimated memory: {total_memory:.2f}MB")
+        
+        # Check if batch fits in GPU memory
+        if total_memory > self.gpu_memory_config["safe_memory_mb"]:
+            print("⚠️  Batch too large for GPU, using sequential compilation")
+            return self.sequential_compile(circuit_paths)
+        
+        # Parallel compilation (simplified - would use actual GPU parallelization)
+        results = []
+        for circuit_path in circuit_paths:
+            result = self.compile_with_gpu_optimization(circuit_path)
+            results.append(result)
+        
+        total_time = time.time() - start_time
+        
+        return {
+            "success": True,
+            "batch_size": len(circuit_paths),
+            "total_time": total_time,
+            "average_time": total_time / len(circuit_paths),
+            "results": results,
+            "memory_estimates": memory_estimates
+        }
+    
+    def sequential_compile(self, circuit_paths: List[str]) -> Dict:
+        """Sequential compilation fallback"""
+        start_time = time.time()
+        results = []
+        
+        for circuit_path in circuit_paths:
+            result = self.compile_with_gpu_optimization(circuit_path)
+            results.append(result)
+        
+        total_time = time.time() - start_time
+        
+        return {
+            "success": True,
+            "batch_size": len(circuit_paths),
+            "compilation_type": "sequential",
+            "total_time": total_time,
+            "average_time": total_time / len(circuit_paths),
+            "results": results
+        }
+    
+    def _create_cache_key(self, circuit_path: str) -> str:
+        """Create cache key for circuit"""
+        circuit_file = Path(circuit_path)
+        
+        # Use file hash and modification time
+        file_hash = hashlib.sha256()
+        
+        try:
+            with open(circuit_file, 'rb') as f:
+                file_hash.update(f.read())
+            
+            # Add modification time
+            mtime = circuit_file.stat().st_mtime
+            file_hash.update(str(mtime).encode())
+            
+            return file_hash.hexdigest()[:16]
+            
+        except Exception:
+            # Fallback to filename
+            return hashlib.md5(str(circuit_path).encode()).hexdigest()[:16]
+    
+    def _load_cache(self, cache_path: Path) -> Optional[Dict]:
+        """Load cached compilation result"""
+        try:
+            with open(cache_path, 'r') as f:
+                return json.load(f)
+        except Exception:
+            return None
+    
+    def _save_cache(self, cache_path: Path, result: Dict):
+        """Save compilation result to cache"""
+        try:
+            with open(cache_path, 'w') as f:
+                json.dump(result, f, indent=2)
+        except Exception as e:
+            print(f"⚠️  Failed to save cache: {e}")
+    
+    def benchmark_compilation_performance(self, circuit_path: str, iterations: int = 5) -> Dict:
+        """
+        Benchmark compilation performance
+        
+        Args:
+            circuit_path: Path to circuit file
+            iterations: Number of iterations to run
+            
+        Returns:
+            Performance benchmark results
+        """
+        print(f"📊 Benchmarking compilation performance ({iterations} iterations)")
+        
+        times = []
+        cache_hits = 0
+        successes = 0
+        
+        for i in range(iterations):
+            print(f"   Iteration {i + 1}/{iterations}")
+            
+            start_time = time.time()
+            result = self.compile_with_gpu_optimization(circuit_path)
+            iteration_time = time.time() - start_time
+            
+            times.append(iteration_time)
+            
+            if result.get("cache_hit"):
+                cache_hits += 1
+            
+            if result.get("success"):
+                successes += 1
+        
+        avg_time = sum(times) / len(times)
+        min_time = min(times)
+        max_time = max(times)
+        
+        return {
+            "circuit_path": circuit_path,
+            "iterations": iterations,
+            "success_rate": successes / iterations,
+            "cache_hit_rate": cache_hits / iterations,
+            "average_time": avg_time,
+            "min_time": min_time,
+            "max_time": max_time,
+            "times": times
+        }
+
+def main():
+    """Main function for testing GPU-aware compilation"""
+    print("🚀 AITBC GPU-Aware ZK Circuit Compiler")
+    print("=" * 50)
+    
+    compiler = GPUAwareCompiler()
+    
+    # Test with existing circuits
+    test_circuits = [
+        "modular_ml_components.circom",
+        "ml_training_verification.circom",
+        "ml_inference_verification.circom"
+    ]
+    
+    for circuit in test_circuits:
+        circuit_path = compiler.base_dir / circuit
+        
+        if circuit_path.exists():
+            print(f"\n🔧 Testing {circuit}")
+            
+            # Estimate memory
+            memory_est = compiler.estimate_circuit_memory(str(circuit_path))
+            print(f"   Memory estimation: {memory_est}")
+            
+            # Compile
+            result = compiler.compile_with_gpu_optimization(str(circuit_path))
+            print(f"   Result: {result.get('success', False)}")
+            
+        else:
+            print(f"⚠️  Circuit not found: {circuit_path}")
+
+if __name__ == "__main__":
+    main()

gpu_acceleration/cuda_kernels/high_performance_cuda_accelerator.py

@@ -0,0 +1,453 @@
+#!/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()

gpu_acceleration/cuda_kernels/optimized_cuda_accelerator.py

@@ -0,0 +1,394 @@
+#!/usr/bin/env python3
+"""
+Optimized CUDA ZK Accelerator with Improved Performance
+Implements optimized CUDA kernels and benchmarking for better GPU utilization
+"""
+
+import ctypes
+import numpy as np
+from typing import List, Tuple, Optional
+import os
+import sys
+import time
+
+# Field element structure (256-bit for bn128 curve)
+class FieldElement(ctypes.Structure):
+    _fields_ = [("limbs", ctypes.c_uint64 * 4)]
+
+class OptimizedCUDAZKAccelerator:
+    """Optimized Python interface for CUDA-accelerated ZK circuit operations"""
+    
+    def __init__(self, lib_path: str = None):
+        """
+        Initialize optimized 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"✅ Optimized 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"""
+        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(ctypes.c_void_p, 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 benchmark_optimized_performance(self, max_elements: int = 10000000) -> dict:
+        """
+        Benchmark optimized GPU performance with varying dataset sizes
+        
+        Args:
+            max_elements: Maximum number of elements to test
+            
+        Returns:
+            Performance benchmark results
+        """
+        if not self.initialized:
+            return {"error": "CUDA accelerator not initialized"}
+        
+        print(f"🚀 Optimized GPU Performance Benchmark (up to {max_elements:,} elements)")
+        print("=" * 70)
+        
+        # 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 = []
+        
+        for size in test_sizes:
+            if size > max_elements:
+                break
+                
+            print(f"\n📊 Testing {size:,} elements...")
+            
+            # Generate optimized test data
+            a_elements, b_elements = self._generate_test_data(size)
+            
+            # bn128 field modulus (simplified)
+            modulus = [0xFFFFFFFFFFFFFFFF, 0xFFFFFFFFFFFFFFFF, 0xFFFFFFFFFFFFFFFF, 0xFFFFFFFFFFFFFFFF]
+            
+            # GPU benchmark with multiple runs
+            gpu_times = []
+            for run in range(3):  # 3 runs for consistency
+                start_time = time.time()
+                success, gpu_result = self.field_addition_optimized(a_elements, b_elements, modulus)
+                gpu_time = time.time() - start_time
+                
+                if success:
+                    gpu_times.append(gpu_time)
+            
+            if not gpu_times:
+                print(f"   ❌ GPU failed for {size:,} elements")
+                continue
+            
+            # Average GPU time
+            avg_gpu_time = sum(gpu_times) / len(gpu_times)
+            
+            # CPU benchmark
+            start_time = time.time()
+            cpu_result = self._cpu_field_addition(a_elements, b_elements, modulus)
+            cpu_time = time.time() - start_time
+            
+            # Calculate speedup
+            speedup = cpu_time / avg_gpu_time if avg_gpu_time > 0 else 0
+            
+            result = {
+                "elements": size,
+                "gpu_time": avg_gpu_time,
+                "cpu_time": cpu_time,
+                "speedup": speedup,
+                "gpu_throughput": size / avg_gpu_time if avg_gpu_time > 0 else 0,
+                "cpu_throughput": size / cpu_time if cpu_time > 0 else 0,
+                "gpu_success": True
+            }
+            
+            results.append(result)
+            
+            print(f"   GPU Time: {avg_gpu_time:.4f}s")
+            print(f"   CPU Time: {cpu_time:.4f}s")
+            print(f"   Speedup: {speedup:.2f}x")
+            print(f"   GPU Throughput: {result['gpu_throughput']:.0f} elements/s")
+        
+        # Find optimal performance point
+        best_speedup = max(results, key=lambda x: x["speedup"]) if results else None
+        best_throughput = max(results, key=lambda x: x["gpu_throughput"]) if results else None
+        
+        summary = {
+            "test_sizes": test_sizes[:len(results)],
+            "results": results,
+            "best_speedup": best_speedup,
+            "best_throughput": best_throughput,
+            "gpu_device": "NVIDIA GeForce RTX 4060 Ti"
+        }
+        
+        print(f"\n🎯 Performance Summary:")
+        if best_speedup:
+            print(f"   Best Speedup: {best_speedup['speedup']:.2f}x at {best_speedup['elements']:,} elements")
+        if best_throughput:
+            print(f"   Best Throughput: {best_throughput['gpu_throughput']:.0f} elements/s at {best_throughput['elements']:,} elements")
+        
+        return summary
+    
+    def field_addition_optimized(
+        self, 
+        a: List[FieldElement], 
+        b: List[FieldElement], 
+        modulus: List[int]
+    ) -> Tuple[bool, Optional[List[FieldElement]]]:
+        """
+        Perform optimized 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 with optimal memory layout
+            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:
+                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 _generate_test_data(self, num_elements: int) -> Tuple[List[FieldElement], List[FieldElement]]:
+        """Generate optimized test data for benchmarking"""
+        a_elements = []
+        b_elements = []
+        
+        # Use numpy for faster generation
+        a_data = np.random.randint(0, 2**32, size=(num_elements, 4), dtype=np.uint64)
+        b_data = np.random.randint(0, 2**32, size=(num_elements, 4), dtype=np.uint64)
+        
+        for i in range(num_elements):
+            a = FieldElement()
+            b = FieldElement()
+            
+            for j in range(4):
+                a.limbs[j] = a_data[i, j]
+                b.limbs[j] = b_data[i, j]
+            
+            a_elements.append(a)
+            b_elements.append(b)
+        
+        return a_elements, b_elements
+    
+    def _cpu_field_addition(self, a_elements: List[FieldElement], b_elements: List[FieldElement], modulus: List[int]) -> List[FieldElement]:
+        """Optimized CPU field addition for benchmarking"""
+        num_elements = len(a_elements)
+        result = []
+        
+        # Use numpy for vectorized operations where possible
+        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]
+            result.append(c)
+        
+        return result
+    
+    def analyze_performance_bottlenecks(self) -> dict:
+        """Analyze potential performance bottlenecks in GPU operations"""
+        print("🔍 Analyzing GPU Performance Bottlenecks...")
+        
+        analysis = {
+            "memory_bandwidth": self._test_memory_bandwidth(),
+            "compute_utilization": self._test_compute_utilization(),
+            "data_transfer": self._test_data_transfer(),
+            "kernel_launch": self._test_kernel_launch_overhead()
+        }
+        
+        print("\n📊 Performance Analysis Results:")
+        for key, value in analysis.items():
+            print(f"   {key}: {value}")
+        
+        return analysis
+    
+    def _test_memory_bandwidth(self) -> str:
+        """Test GPU memory bandwidth"""
+        # Simple memory bandwidth test
+        try:
+            size = 1000000  # 1M elements
+            a_elements, b_elements = self._generate_test_data(size)
+            
+            start_time = time.time()
+            success, _ = self.field_addition_optimized(a_elements, b_elements, 
+                                                      [0xFFFFFFFFFFFFFFFF] * 4)
+            test_time = time.time() - start_time
+            
+            if success:
+                bandwidth = (size * 4 * 8 * 3) / (test_time * 1e9)  # GB/s (3 arrays, 4 limbs, 8 bytes)
+                return f"{bandwidth:.2f} GB/s"
+            else:
+                return "Test failed"
+        except Exception as e:
+            return f"Error: {e}"
+    
+    def _test_compute_utilization(self) -> str:
+        """Test GPU compute utilization"""
+        return "Compute utilization test - requires profiling tools"
+    
+    def _test_data_transfer(self) -> str:
+        """Test data transfer overhead"""
+        try:
+            size = 100000
+            a_elements, _ = self._generate_test_data(size)
+            
+            # Test data transfer time
+            start_time = time.time()
+            a_array = np.array(a_elements, dtype=FieldElement)
+            transfer_time = time.time() - start_time
+            
+            return f"{transfer_time:.4f}s for {size:,} elements"
+        except Exception as e:
+            return f"Error: {e}"
+    
+    def _test_kernel_launch_overhead(self) -> str:
+        """Test kernel launch overhead"""
+        try:
+            size = 1000  # Small dataset to isolate launch overhead
+            a_elements, b_elements = self._generate_test_data(size)
+            
+            start_time = time.time()
+            success, _ = self.field_addition_optimized(a_elements, b_elements, 
+                                                      [0xFFFFFFFFFFFFFFFF] * 4)
+            total_time = time.time() - start_time
+            
+            if success:
+                return f"{total_time:.4f}s total (includes launch overhead)"
+            else:
+                return "Test failed"
+        except Exception as e:
+            return f"Error: {e}"
+
+def main():
+    """Main function for testing optimized CUDA acceleration"""
+    print("🚀 AITBC Optimized CUDA ZK Accelerator Test")
+    print("=" * 50)
+    
+    try:
+        # Initialize accelerator
+        accelerator = OptimizedCUDAZKAccelerator()
+        
+        if not accelerator.initialized:
+            print("❌ Failed to initialize CUDA accelerator")
+            return
+        
+        # Initialize device
+        if not accelerator.init_device():
+            return
+        
+        # Run optimized benchmark
+        results = accelerator.benchmark_optimized_performance(10000000)
+        
+        # Analyze performance bottlenecks
+        bottleneck_analysis = accelerator.analyze_performance_bottlenecks()
+        
+        print("\n✅ Optimized CUDA acceleration test completed!")
+        
+        if results.get("best_speedup"):
+            print(f"🚀 Best performance: {results['best_speedup']['speedup']:.2f}x speedup")
+        
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+
+if __name__ == "__main__":
+    main()

gpu_acceleration/cuda_kernels/optimized_field_operations.cu

@@ -0,0 +1,517 @@
+/**
+ * Optimized CUDA Kernels for ZK Circuit Field Operations
+ * 
+ * Implements high-performance GPU-accelerated field arithmetic with optimized memory access
+ * patterns, vectorized operations, and improved data transfer efficiency.
+ */
+
+#include <cuda_runtime.h>
+#include <curand_kernel.h>
+#include <device_launch_parameters.h>
+#include <stdint.h>
+#include <stdio.h>
+
+// Custom 128-bit integer type for CUDA compatibility
+typedef unsigned long long uint128_t __attribute__((mode(TI)));
+
+// Optimized field element structure using flat arrays for better memory coalescing
+typedef struct {
+    uint64_t limbs[4];  // 4 x 64-bit limbs for 256-bit field element
+} field_element_t;
+
+// Vectorized field element for improved memory bandwidth
+typedef uint4 field_vector_t;  // 128-bit vector (4 x 32-bit)
+
+// Optimized constraint structure
+typedef struct {
+    uint64_t a[4];
+    uint64_t b[4];
+    uint64_t c[4];
+    uint8_t operation;  // 0: a + b = c, 1: a * b = c
+} optimized_constraint_t;
+
+// Optimized kernel for parallel field addition with coalesced memory access
+__global__ void optimized_field_addition_kernel(
+    const uint64_t* __restrict__ a_flat,
+    const uint64_t* __restrict__ b_flat,
+    uint64_t* __restrict__ result_flat,
+    const uint64_t* __restrict__ modulus,
+    int num_elements
+) {
+    // Calculate global thread ID
+    int tid = blockIdx.x * blockDim.x + threadIdx.x;
+    int stride = blockDim.x * gridDim.x;
+    
+    // Process multiple elements per thread for better utilization
+    for (int elem = tid; elem < num_elements; elem += stride) {
+        int base_idx = elem * 4;  // 4 limbs per element
+        
+        // Perform field addition with carry propagation
+        uint64_t carry = 0;
+        
+        // Unrolled loop for better performance
+        #pragma unroll
+        for (int i = 0; i < 4; i++) {
+            uint128_t sum = (uint128_t)a_flat[base_idx + i] + b_flat[base_idx + i] + carry;
+            result_flat[base_idx + i] = (uint64_t)sum;
+            carry = sum >> 64;
+        }
+        
+        // Simplified modulus reduction (for demonstration)
+        // In practice, would implement proper bn128 field reduction
+        if (carry > 0) {
+            #pragma unroll
+            for (int i = 0; i < 4; i++) {
+                uint128_t diff = (uint128_t)result_flat[base_idx + i] - modulus[i] - carry;
+                result_flat[base_idx + i] = (uint64_t)diff;
+                carry = diff >> 63; // Borrow
+            }
+        }
+    }
+}
+
+// Vectorized field addition kernel using uint4 for better memory bandwidth
+__global__ void vectorized_field_addition_kernel(
+    const field_vector_t* __restrict__ a_vec,
+    const field_vector_t* __restrict__ b_vec,
+    field_vector_t* __restrict__ result_vec,
+    const uint64_t* __restrict__ modulus,
+    int num_vectors
+) {
+    int tid = blockIdx.x * blockDim.x + threadIdx.x;
+    int stride = blockDim.x * gridDim.x;
+    
+    for (int vec = tid; vec < num_vectors; vec += stride) {
+        // Load vectors
+        field_vector_t a = a_vec[vec];
+        field_vector_t b = b_vec[vec];
+        
+        // Perform vectorized addition
+        field_vector_t result;
+        uint64_t carry = 0;
+        
+        // Component-wise addition with carry
+        uint128_t sum0 = (uint128_t)a.x + b.x + carry;
+        result.x = (uint64_t)sum0;
+        carry = sum0 >> 64;
+        
+        uint128_t sum1 = (uint128_t)a.y + b.y + carry;
+        result.y = (uint64_t)sum1;
+        carry = sum1 >> 64;
+        
+        uint128_t sum2 = (uint128_t)a.z + b.z + carry;
+        result.z = (uint64_t)sum2;
+        carry = sum2 >> 64;
+        
+        uint128_t sum3 = (uint128_t)a.w + b.w + carry;
+        result.w = (uint64_t)sum3;
+        
+        // Store result
+        result_vec[vec] = result;
+    }
+}
+
+// Shared memory optimized kernel for large datasets
+__global__ void shared_memory_field_addition_kernel(
+    const uint64_t* __restrict__ a_flat,
+    const uint64_t* __restrict__ b_flat,
+    uint64_t* __restrict__ result_flat,
+    const uint64_t* __restrict__ modulus,
+    int num_elements
+) {
+    // Shared memory for tile processing
+    __shared__ uint64_t tile_a[256 * 4];  // 256 threads, 4 limbs each
+    __shared__ uint64_t tile_b[256 * 4];
+    __shared__ uint64_t tile_result[256 * 4];
+    
+    int tid = threadIdx.x;
+    int elements_per_tile = blockDim.x;
+    int tile_idx = blockIdx.x;
+    int elem_in_tile = tid;
+    
+    // Load data into shared memory
+    if (tile_idx * elements_per_tile + elem_in_tile < num_elements) {
+        int global_idx = (tile_idx * elements_per_tile + elem_in_tile) * 4;
+        
+        // Coalesced global memory access
+        #pragma unroll
+        for (int i = 0; i < 4; i++) {
+            tile_a[tid * 4 + i] = a_flat[global_idx + i];
+            tile_b[tid * 4 + i] = b_flat[global_idx + i];
+        }
+    }
+    
+    __syncthreads();
+    
+    // Process in shared memory
+    if (tile_idx * elements_per_tile + elem_in_tile < num_elements) {
+        uint64_t carry = 0;
+        
+        #pragma unroll
+        for (int i = 0; i < 4; i++) {
+            uint128_t sum = (uint128_t)tile_a[tid * 4 + i] + tile_b[tid * 4 + i] + carry;
+            tile_result[tid * 4 + i] = (uint64_t)sum;
+            carry = sum >> 64;
+        }
+        
+        // Simplified modulus reduction
+        if (carry > 0) {
+            #pragma unroll
+            for (int i = 0; i < 4; i++) {
+                uint128_t diff = (uint128_t)tile_result[tid * 4 + i] - modulus[i] - carry;
+                tile_result[tid * 4 + i] = (uint64_t)diff;
+                carry = diff >> 63;
+            }
+        }
+    }
+    
+    __syncthreads();
+    
+    // Write back to global memory
+    if (tile_idx * elements_per_tile + elem_in_tile < num_elements) {
+        int global_idx = (tile_idx * elements_per_tile + elem_in_tile) * 4;
+        
+        // Coalesced global memory write
+        #pragma unroll
+        for (int i = 0; i < 4; i++) {
+            result_flat[global_idx + i] = tile_result[tid * 4 + i];
+        }
+    }
+}
+
+// Optimized constraint verification kernel
+__global__ void optimized_constraint_verification_kernel(
+    const optimized_constraint_t* __restrict__ constraints,
+    const uint64_t* __restrict__ witness_flat,
+    bool* __restrict__ results,
+    int num_constraints
+) {
+    int tid = blockIdx.x * blockDim.x + threadIdx.x;
+    int stride = blockDim.x * gridDim.x;
+    
+    for (int constraint_idx = tid; constraint_idx < num_constraints; constraint_idx += stride) {
+        const optimized_constraint_t* c = &constraints[constraint_idx];
+        
+        bool constraint_satisfied = true;
+        
+        if (c->operation == 0) {
+            // Addition constraint: a + b = c
+            uint64_t computed[4];
+            uint64_t carry = 0;
+            
+            #pragma unroll
+            for (int i = 0; i < 4; i++) {
+                uint128_t sum = (uint128_t)c->a[i] + c->b[i] + carry;
+                computed[i] = (uint64_t)sum;
+                carry = sum >> 64;
+            }
+            
+            // Check if computed equals expected
+            #pragma unroll
+            for (int i = 0; i < 4; i++) {
+                if (computed[i] != c->c[i]) {
+                    constraint_satisfied = false;
+                    break;
+                }
+            }
+        } else {
+            // Multiplication constraint: a * b = c (simplified)
+            // In practice, would implement proper field multiplication
+            constraint_satisfied = (c->a[0] * c->b[0]) == c->c[0];  // Simplified check
+        }
+        
+        results[constraint_idx] = constraint_satisfied;
+    }
+}
+
+// Stream-optimized kernel for overlapping computation and transfer
+__global__ void stream_optimized_field_kernel(
+    const uint64_t* __restrict__ a_flat,
+    const uint64_t* __restrict__ b_flat,
+    uint64_t* __restrict__ result_flat,
+    const uint64_t* __restrict__ modulus,
+    int num_elements,
+    int stream_id
+) {
+    int tid = blockIdx.x * blockDim.x + threadIdx.x;
+    int stride = blockDim.x * gridDim.x;
+    
+    // Each stream processes a chunk of the data
+    int elements_per_stream = (num_elements + 3) / 4;  // 4 streams
+    int start_elem = stream_id * elements_per_stream;
+    int end_elem = min(start_elem + elements_per_stream, num_elements);
+    
+    for (int elem = start_elem + tid; elem < end_elem; elem += stride) {
+        int base_idx = elem * 4;
+        
+        uint64_t carry = 0;
+        
+        #pragma unroll
+        for (int i = 0; i < 4; i++) {
+            uint128_t sum = (uint128_t)a_flat[base_idx + i] + b_flat[base_idx + i] + carry;
+            result_flat[base_idx + i] = (uint64_t)sum;
+            carry = sum >> 64;
+        }
+    }
+}
+
+// Host wrapper functions for optimized operations
+extern "C" {
+
+// Initialize CUDA device with optimization info
+cudaError_t init_optimized_cuda_device() {
+    int deviceCount = 0;
+    cudaError_t error = cudaGetDeviceCount(&deviceCount);
+    
+    if (error != cudaSuccess || deviceCount == 0) {
+        printf("No CUDA devices found\n");
+        return error;
+    }
+    
+    // Select best device
+    int best_device = 0;
+    size_t max_memory = 0;
+    
+    for (int i = 0; i < deviceCount; i++) {
+        cudaDeviceProp prop;
+        error = cudaGetDeviceProperties(&prop, i);
+        if (error == cudaSuccess && prop.totalGlobalMem > max_memory) {
+            max_memory = prop.totalGlobalMem;
+            best_device = i;
+        }
+    }
+    
+    error = cudaSetDevice(best_device);
+    if (error != cudaSuccess) {
+        printf("Failed to set CUDA device\n");
+        return error;
+    }
+    
+    // Get device properties
+    cudaDeviceProp prop;
+    error = cudaGetDeviceProperties(&prop, best_device);
+    if (error == cudaSuccess) {
+        printf("✅ Optimized CUDA Device: %s\n", prop.name);
+        printf("   Compute Capability: %d.%d\n", prop.major, prop.minor);
+        printf("   Global Memory: %zu MB\n", prop.totalGlobalMem / (1024 * 1024));
+        printf("   Shared Memory per Block: %zu KB\n", prop.sharedMemPerBlock / 1024);
+        printf("   Max Threads per Block: %d\n", prop.maxThreadsPerBlock);
+        printf("   Warp Size: %d\n", prop.warpSize);
+        printf("   Max Grid Size: [%d, %d, %d]\n", 
+               prop.maxGridSize[0], prop.maxGridSize[1], prop.maxGridSize[2]);
+    }
+    
+    return error;
+}
+
+// Optimized field addition with flat arrays
+cudaError_t gpu_optimized_field_addition(
+    const uint64_t* a_flat,
+    const uint64_t* b_flat,
+    uint64_t* result_flat,
+    const uint64_t* modulus,
+    int num_elements
+) {
+    // Allocate device memory
+    uint64_t *d_a, *d_b, *d_result, *d_modulus;
+    
+    size_t flat_size = num_elements * 4 * sizeof(uint64_t);  // 4 limbs per element
+    size_t modulus_size = 4 * sizeof(uint64_t);
+    
+    cudaError_t error = cudaMalloc(&d_a, flat_size);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMalloc(&d_b, flat_size);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMalloc(&d_result, flat_size);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMalloc(&d_modulus, modulus_size);
+    if (error != cudaSuccess) return error;
+    
+    // Copy data to device with optimized transfer
+    error = cudaMemcpy(d_a, a_flat, flat_size, cudaMemcpyHostToDevice);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMemcpy(d_b, b_flat, flat_size, cudaMemcpyHostToDevice);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMemcpy(d_modulus, modulus, modulus_size, cudaMemcpyHostToDevice);
+    if (error != cudaSuccess) return error;
+    
+    // Launch optimized kernel
+    int threadsPerBlock = 256;  // Optimal for most GPUs
+    int blocksPerGrid = (num_elements + threadsPerBlock - 1) / threadsPerBlock;
+    
+    // Ensure we have enough blocks for good GPU utilization
+    blocksPerGrid = max(blocksPerGrid, 32);  // Minimum blocks for good occupancy
+    
+    printf("🚀 Launching optimized field addition kernel:\n");
+    printf("   Elements: %d\n", num_elements);
+    printf("   Blocks: %d\n", blocksPerGrid);
+    printf("   Threads per Block: %d\n", threadsPerBlock);
+    printf("   Total Threads: %d\n", blocksPerGrid * threadsPerBlock);
+    
+    // Use optimized kernel
+    optimized_field_addition_kernel<<<blocksPerGrid, threadsPerBlock>>>(
+        d_a, d_b, d_result, d_modulus, num_elements
+    );
+    
+    // Check for kernel launch errors
+    error = cudaGetLastError();
+    if (error != cudaSuccess) return error;
+    
+    // Synchronize to ensure kernel completion
+    error = cudaDeviceSynchronize();
+    if (error != cudaSuccess) return error;
+    
+    // Copy result back to host
+    error = cudaMemcpy(result_flat, d_result, flat_size, cudaMemcpyDeviceToHost);
+    
+    // Free device memory
+    cudaFree(d_a);
+    cudaFree(d_b);
+    cudaFree(d_result);
+    cudaFree(d_modulus);
+    
+    return error;
+}
+
+// Vectorized field addition for better memory bandwidth
+cudaError_t gpu_vectorized_field_addition(
+    const field_vector_t* a_vec,
+    const field_vector_t* b_vec,
+    field_vector_t* result_vec,
+    const uint64_t* modulus,
+    int num_elements
+) {
+    // Allocate device memory
+    field_vector_t *d_a, *d_b, *d_result;
+    uint64_t *d_modulus;
+    
+    size_t vec_size = num_elements * sizeof(field_vector_t);
+    size_t modulus_size = 4 * sizeof(uint64_t);
+    
+    cudaError_t error = cudaMalloc(&d_a, vec_size);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMalloc(&d_b, vec_size);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMalloc(&d_result, vec_size);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMalloc(&d_modulus, modulus_size);
+    if (error != cudaSuccess) return error;
+    
+    // Copy data to device
+    error = cudaMemcpy(d_a, a_vec, vec_size, cudaMemcpyHostToDevice);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMemcpy(d_b, b_vec, vec_size, cudaMemcpyHostToDevice);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMemcpy(d_modulus, modulus, modulus_size, cudaMemcpyHostToDevice);
+    if (error != cudaSuccess) return error;
+    
+    // Launch vectorized kernel
+    int threadsPerBlock = 256;
+    int blocksPerGrid = (num_elements + threadsPerBlock - 1) / threadsPerBlock;
+    blocksPerGrid = max(blocksPerGrid, 32);
+    
+    printf("🚀 Launching vectorized field addition kernel:\n");
+    printf("   Elements: %d\n", num_elements);
+    printf("   Blocks: %d\n", blocksPerGrid);
+    printf("   Threads per Block: %d\n", threadsPerBlock);
+    
+    vectorized_field_addition_kernel<<<blocksPerGrid, threadsPerBlock>>>(
+        d_a, d_b, d_result, d_modulus, num_elements
+    );
+    
+    error = cudaGetLastError();
+    if (error != cudaSuccess) return error;
+    
+    error = cudaDeviceSynchronize();
+    if (error != cudaSuccess) return error;
+    
+    // Copy result back
+    error = cudaMemcpy(result_vec, d_result, vec_size, cudaMemcpyDeviceToHost);
+    
+    // Free device memory
+    cudaFree(d_a);
+    cudaFree(d_b);
+    cudaFree(d_result);
+    cudaFree(d_modulus);
+    
+    return error;
+}
+
+// Shared memory optimized field addition
+cudaError_t gpu_shared_memory_field_addition(
+    const uint64_t* a_flat,
+    const uint64_t* b_flat,
+    uint64_t* result_flat,
+    const uint64_t* modulus,
+    int num_elements
+) {
+    // Similar to optimized version but uses shared memory
+    uint64_t *d_a, *d_b, *d_result, *d_modulus;
+    
+    size_t flat_size = num_elements * 4 * sizeof(uint64_t);
+    size_t modulus_size = 4 * sizeof(uint64_t);
+    
+    cudaError_t error = cudaMalloc(&d_a, flat_size);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMalloc(&d_b, flat_size);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMalloc(&d_result, flat_size);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMalloc(&d_modulus, modulus_size);
+    if (error != cudaSuccess) return error;
+    
+    // Copy data
+    error = cudaMemcpy(d_a, a_flat, flat_size, cudaMemcpyHostToDevice);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMemcpy(d_b, b_flat, flat_size, cudaMemcpyHostToDevice);
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMemcpy(d_modulus, modulus, modulus_size, cudaMemcpyHostToDevice);
+    if (error != cudaSuccess) return error;
+    
+    // Launch shared memory kernel
+    int threadsPerBlock = 256;  // Matches shared memory tile size
+    int blocksPerGrid = (num_elements + threadsPerBlock - 1) / threadsPerBlock;
+    blocksPerGrid = max(blocksPerGrid, 32);
+    
+    printf("🚀 Launching shared memory field addition kernel:\n");
+    printf("   Elements: %d\n", num_elements);
+    printf("   Blocks: %d\n", blocksPerGrid);
+    printf("   Threads per Block: %d\n", threadsPerBlock);
+    
+    shared_memory_field_addition_kernel<<<blocksPerGrid, threadsPerBlock>>>(
+        d_a, d_b, d_result, d_modulus, num_elements
+    );
+    
+    error = cudaGetLastError();
+    if (error != cudaSuccess) return error;
+    
+    error = cudaDeviceSynchronize();
+    if (error != cudaSuccess) return error;
+    
+    error = cudaMemcpy(result_flat, d_result, flat_size, cudaMemcpyDeviceToHost);
+    
+    // Free device memory
+    cudaFree(d_a);
+    cudaFree(d_b);
+    cudaFree(d_result);
+    cudaFree(d_modulus);
+    
+    return error;
+}
+
+} // extern "C"