aitbc/gpu_acceleration/cuda_kernels/optimized_field_operations.cu

/**
 * 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"