/**
 * 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"