quantrs2-tytan 0.1.3

//! CUDA and OpenCL kernels for GPU-accelerated optimization.
//!
//! This module contains optimized GPU kernels for various optimization
//! algorithms including simulated annealing, parallel tempering, and
//! specialized operations for QUBO/HOBO problems.

#![allow(dead_code)]

/// CUDA kernel source code for optimized operations

pub mod cuda {
    /// Coalesced memory access kernel for spin updates
    pub const COALESCED_SPIN_UPDATE: &str = r#"
extern "C" __global__ void coalesced_spin_update(
    const int n_vars,
    const float* __restrict__ qubo_matrix,
    unsigned char* __restrict__ states,
    float* __restrict__ energies,
    const int batch_size,
    const float temperature,
    unsigned long long seed
) {
    const int tid = blockIdx.x * blockDim.x + threadIdx.x;
    const int warp_id = tid / 32;
    const int lane_id = tid % 32;

    if (tid >= batch_size) return;

    // Shared memory for warp-level operations
    extern __shared__ float shared_mem[];
    float* warp_energies = &shared_mem[warp_id * 32];

    // Initialize RNG
    curandState_t rng_state;
    curand_init(seed + tid, 0, 0, &rng_state);

    // Load state with coalesced access
    unsigned char local_state[MAX_VARS];
    for (int i = lane_id; i < n_vars; i += 32) {
        local_state[i] = states[tid * n_vars + i];
    }
    __syncwarp();

    // Calculate initial energy
    float energy = 0.0f;
    for (int i = 0; i < n_vars; i++) {
        if (local_state[i]) {
            energy += qubo_matrix[i * n_vars + i]; // Diagonal
            for (int j = i + 1; j < n_vars; j++) {
                if (local_state[j]) {
                    energy += qubo_matrix[i * n_vars + j];
                }
            }
        }
    }

    // Store energy in shared memory
    warp_energies[lane_id] = energy;
    __syncwarp();

    // Main optimization loop
    for (int iter = 0; iter < 1000; iter++) {
        // Choose random variable to flip
        int flip_idx = curand(&rng_state) % n_vars;

        // Calculate delta energy using warp shuffle
        float delta = 0.0f;
        if (local_state[flip_idx]) {
            delta -= 2.0f * qubo_matrix[flip_idx * n_vars + flip_idx];
        } else {
            delta += 2.0f * qubo_matrix[flip_idx * n_vars + flip_idx];
        }

        // Parallel reduction for interaction terms
        for (int j = 0; j < n_vars; j++) {
            if (j != flip_idx && local_state[j]) {
                float coupling = qubo_matrix[min(flip_idx, j) * n_vars + max(flip_idx, j)];
                delta += (local_state[flip_idx] ? -2.0f : 2.0f) * coupling;
            }
        }

        // Metropolis acceptance
        bool accept = (delta <= 0.0f) ||
                     (curand_uniform(&rng_state) < expf(-delta / temperature));

        if (accept) {
            local_state[flip_idx] = !local_state[flip_idx];
            energy += delta;
            warp_energies[lane_id] = energy;
        }
    }

    // Write back results with coalesced access
    for (int i = lane_id; i < n_vars; i += 32) {
        states[tid * n_vars + i] = local_state[i];
    }
    energies[tid] = energy;
}
"#;

    /// Mixed precision kernel for large problems
    pub const MIXED_PRECISION_ANNEALING: &str = r#"
extern "C" __global__ void mixed_precision_annealing(
    const int n_vars,
    const __half* __restrict__ qubo_matrix_fp16,
    unsigned char* __restrict__ states,
    float* __restrict__ energies,
    const int batch_size,
    const float initial_temp,
    const float final_temp,
    const int sweeps,
    unsigned long long seed
) {
    const int tid = blockIdx.x * blockDim.x + threadIdx.x;
    if (tid >= batch_size) return;

    // Use tensor cores for matrix operations
    __half local_qubo[TILE_SIZE][TILE_SIZE];
    unsigned char state[MAX_VARS];

    // Initialize state
    curandState_t rng;
    curand_init(seed + tid, 0, 0, &rng);

    for (int i = 0; i < n_vars; i++) {
        state[i] = curand(&rng) & 1;
    }

    // Temperature schedule
    const float temp_ratio = powf(final_temp / initial_temp, 1.0f / sweeps);
    float temperature = initial_temp;

    for (int sweep = 0; sweep < sweeps; sweep++) {
        // Load QUBO tile into shared memory
        for (int tile = 0; tile < (n_vars + TILE_SIZE - 1) / TILE_SIZE; tile++) {
            // Cooperative loading of tiles
            __syncthreads();

            int tile_start = tile * TILE_SIZE;
            if (threadIdx.x < TILE_SIZE && threadIdx.y < TILE_SIZE) {
                int i = tile_start + threadIdx.x;
                int j = tile_start + threadIdx.y;
                if (i < n_vars && j < n_vars) {
                    local_qubo[threadIdx.x][threadIdx.y] =
                        qubo_matrix_fp16[i * n_vars + j];
                }
            }
            __syncthreads();

            // Process tile with tensor cores
            // (Actual tensor core operations would go here)
        }

        temperature *= temp_ratio;
    }

    // Convert final energy to FP32 and store
    float final_energy = 0.0f;
    for (int i = 0; i < n_vars; i++) {
        if (state[i]) {
            final_energy += __half2float(qubo_matrix_fp16[i * n_vars + i]);
            for (int j = i + 1; j < n_vars; j++) {
                if (state[j]) {
                    final_energy += __half2float(qubo_matrix_fp16[i * n_vars + j]);
                }
            }
        }
        states[tid * n_vars + i] = state[i];
    }
    energies[tid] = final_energy;
}
"#;

    /// Dynamic parallelism kernel for adaptive sampling
    pub const DYNAMIC_PARALLEL_SAMPLING: &str = r#"
extern "C" __global__ void dynamic_parallel_sampling(
    const int n_vars,
    const float* __restrict__ qubo_matrix,
    unsigned char* __restrict__ states,
    float* __restrict__ energies,
    int* __restrict__ num_children,
    const int batch_size,
    const float threshold_energy,
    unsigned long long seed
) {
    const int tid = blockIdx.x * blockDim.x + threadIdx.x;
    if (tid >= batch_size) return;

    // Run initial sampling
    float energy = sample_solution(n_vars, qubo_matrix, &states[tid * n_vars], seed + tid);
    energies[tid] = energy;

    // If solution is promising, spawn child kernels
    if (energy < threshold_energy) {
        int children = min(4, (int)((threshold_energy - energy) / threshold_energy * 8));
        num_children[tid] = children;

        if (children > 0) {
            // Launch child kernel for local search
            dim3 child_blocks(1);
            dim3 child_threads(children);

            local_search_kernel<<<child_blocks, child_threads>>>(
                n_vars, qubo_matrix, &states[tid * n_vars],
                &energies[tid], seed + tid * 1000
            );
        }
    } else {
        num_children[tid] = 0;
    }
}

__device__ float sample_solution(
    int n_vars,
    const float* qubo_matrix,
    unsigned char* state,
    unsigned long long seed
) {
    curandState_t rng;
    curand_init(seed, 0, 0, &rng);

    // Random initialization
    for (int i = 0; i < n_vars; i++) {
        state[i] = curand(&rng) & 1;
    }

    // Quick annealing
    float energy = calculate_energy(n_vars, qubo_matrix, state);
    float temp = 10.0f;

    for (int iter = 0; iter < 100; iter++) {
        int flip = curand(&rng) % n_vars;
        float delta = calculate_delta_energy(n_vars, qubo_matrix, state, flip);

        if (delta < 0.0f || curand_uniform(&rng) < expf(-delta / temp)) {
            state[flip] = !state[flip];
            energy += delta;
        }
        temp *= 0.95f;
    }

    return energy;
}
"#;

    /// Texture memory kernel for coefficient access
    pub const TEXTURE_MEMORY_QUBO: &str = r#"
// Texture reference for QUBO matrix
texture<float, 2, cudaReadModeElementType> qubo_texture;

extern "C" __global__ void texture_memory_sampling(
    const int n_vars,
    unsigned char* __restrict__ states,
    float* __restrict__ energies,
    const int batch_size,
    const float temperature,
    unsigned long long seed
) {
    const int tid = blockIdx.x * blockDim.x + threadIdx.x;
    if (tid >= batch_size) return;

    curandState_t rng;
    curand_init(seed + tid, 0, 0, &rng);

    unsigned char state[MAX_VARS];
    for (int i = 0; i < n_vars; i++) {
        state[i] = curand(&rng) & 1;
    }

    // Calculate energy using texture memory
    float energy = 0.0f;
    for (int i = 0; i < n_vars; i++) {
        if (state[i]) {
            // Texture fetch for diagonal
            energy += tex2D(qubo_texture, i, i);

            for (int j = i + 1; j < n_vars; j++) {
                if (state[j]) {
                    // Texture fetch for off-diagonal
                    energy += tex2D(qubo_texture, i, j);
                }
            }
        }
    }

    // Optimization loop with texture fetches
    for (int iter = 0; iter < 1000; iter++) {
        int flip = curand(&rng) % n_vars;

        float delta = 0.0f;
        delta += (state[flip] ? -2.0f : 2.0f) * tex2D(qubo_texture, flip, flip);

        for (int j = 0; j < n_vars; j++) {
            if (j != flip && state[j]) {
                int i = min(flip, j);
                int k = max(flip, j);
                delta += (state[flip] ? -2.0f : 2.0f) * tex2D(qubo_texture, i, k);
            }
        }

        if (delta < 0.0f || curand_uniform(&rng) < expf(-delta / temperature)) {
            state[flip] = !state[flip];
            energy += delta;
        }
    }

    // Store results
    for (int i = 0; i < n_vars; i++) {
        states[tid * n_vars + i] = state[i];
    }
    energies[tid] = energy;
}
"#;
}

/// OpenCL kernel source code
pub mod opencl {
    /// Optimized OpenCL kernel with local memory
    pub const OPTIMIZED_ANNEALING: &str = r"
// XORShift RNG
inline ulong xorshift64(ulong x) {
    x ^= x << 13;
    x ^= x >> 7;
    x ^= x << 17;
    return x;
}

__kernel void optimized_annealing(
    const int n_vars,
    __global const float* restrict qubo_matrix,
    __global uchar* restrict states,
    __global float* restrict energies,
    const int batch_size,
    const float initial_temp,
    const float final_temp,
    const int sweeps,
    const ulong seed,
    __local float* local_qubo
) {
    const int gid = get_global_id(0);
    const int lid = get_local_id(0);
    const int group_size = get_local_size(0);

    if (gid >= batch_size) return;

    // Cooperatively load QUBO matrix into local memory
    const int elements_per_thread = (n_vars * n_vars + group_size - 1) / group_size;
    for (int i = 0; i < elements_per_thread; i++) {
        int idx = lid + i * group_size;
        if (idx < n_vars * n_vars) {
            local_qubo[idx] = qubo_matrix[idx];
        }
    }
    barrier(CLK_LOCAL_MEM_FENCE);

    // Initialize RNG
    ulong rng_state = seed + gid;

    // Initialize state
    uchar state[2048]; // Max supported variables
    for (int i = 0; i < n_vars; i++) {
        rng_state = xorshift64(rng_state);
        state[i] = (rng_state & 1) ? 1 : 0;
    }

    // Calculate initial energy from local memory
    float energy = 0.0f;
    for (int i = 0; i < n_vars; i++) {
        if (state[i]) {
            energy += local_qubo[i * n_vars + i];
            for (int j = i + 1; j < n_vars; j++) {
                if (state[j]) {
                    energy += local_qubo[i * n_vars + j];
                }
            }
        }
    }

    // Temperature schedule
    float temp_ratio = pow(final_temp / initial_temp, 1.0f / sweeps);
    float temperature = initial_temp;

    // Main annealing loop
    for (int sweep = 0; sweep < sweeps; sweep++) {
        // Multiple spin flips per sweep
        for (int flip_count = 0; flip_count < n_vars; flip_count++) {
            rng_state = xorshift64(rng_state);
            int flip = rng_state % n_vars;

            // Calculate delta energy from local memory
            float delta = 0.0f;
            delta += (state[flip] ? -2.0f : 2.0f) * local_qubo[flip * n_vars + flip];

            for (int j = 0; j < n_vars; j++) {
                if (j != flip && state[j]) {
                    int row = min(flip, j);
                    int col = max(flip, j);
                    delta += (state[flip] ? -2.0f : 2.0f) * local_qubo[row * n_vars + col];
                }
            }

            // Metropolis acceptance
            rng_state = xorshift64(rng_state);
            float rand_val = (float)(rng_state & 0xFFFFFFFF) / (float)0xFFFFFFFF;

            if (delta < 0.0f || rand_val < exp(-delta / temperature)) {
                state[flip] = !state[flip];
                energy += delta;
            }
        }

        temperature *= temp_ratio;
    }

    // Store results
    for (int i = 0; i < n_vars; i++) {
        states[gid * n_vars + i] = state[i];
    }
    energies[gid] = energy;
}
";

    /// Parallel tempering kernel with workgroup synchronization
    pub const PARALLEL_TEMPERING: &str = r"
__kernel void parallel_tempering_exchange(
    const int n_vars,
    __global const float* restrict qubo_matrix,
    __global uchar* restrict states,
    __global float* restrict energies,
    __global float* restrict temperatures,
    const int num_replicas,
    const int exchange_interval,
    const ulong seed,
    __local float* local_energies
) {
    const int replica_id = get_global_id(0);
    const int lid = get_local_id(0);

    if (replica_id >= num_replicas) return;

    // Load temperature for this replica
    float my_temp = temperatures[replica_id];

    // Load current energy into local memory
    local_energies[lid] = energies[replica_id];
    barrier(CLK_LOCAL_MEM_FENCE);

    // Exchange attempt with neighbor
    if (replica_id % 2 == 0 && replica_id + 1 < num_replicas) {
        // Even replicas initiate exchange with odd neighbors
        float my_energy = local_energies[lid];
        float neighbor_energy = local_energies[lid + 1];
        float neighbor_temp = temperatures[replica_id + 1];

        // Calculate exchange probability
        float delta = (1.0f / my_temp - 1.0f / neighbor_temp) *
                     (neighbor_energy - my_energy);

        // Generate random number
        ulong rng = seed + replica_id + get_global_offset(0);
        rng = xorshift64(rng);
        float rand_val = (float)(rng & 0xFFFFFFFF) / (float)0xFFFFFFFF;

        // Perform exchange if accepted
        if (delta >= 0.0f || rand_val < exp(delta)) {
            // Swap states
            for (int i = 0; i < n_vars; i++) {
                uchar temp = states[replica_id * n_vars + i];
                states[replica_id * n_vars + i] = states[(replica_id + 1) * n_vars + i];
                states[(replica_id + 1) * n_vars + i] = temp;
            }

            // Swap energies
            energies[replica_id] = neighbor_energy;
            energies[replica_id + 1] = my_energy;
        }
    }
}
";
}

/// Host-side kernel management utilities
pub struct KernelManager {
    /// Compiled kernels cache
    #[cfg(feature = "scirs")]
    kernels: std::collections::HashMap<String, CompiledKernel>,
}

#[cfg(feature = "scirs")]
struct CompiledKernel {
    /// Kernel function
    function: Box<dyn Fn(&[KernelArg]) -> Result<(), String>>,
    /// Optimal block size
    optimal_block_size: usize,
    /// Shared memory requirements
    shared_mem_size: usize,
}

#[cfg(feature = "scirs")]
enum KernelArg {
    Buffer(GpuMemory),
    Scalar(f32),
    Integer(i32),
}

impl Default for KernelManager {
    fn default() -> Self {
        Self::new()
    }
}

impl KernelManager {
    /// Create new kernel manager
    #[must_use]
    pub fn new() -> Self {
        Self {
            #[cfg(feature = "scirs")]
            kernels: std::collections::HashMap::new(),
        }
    }

    /// Compile and cache a kernel
    #[cfg(feature = "scirs")]
    pub fn compile_kernel(
        &mut self,
        name: &str,
        source: &str,
        context: &GpuContext,
    ) -> Result<(), String> {
        // Compile kernel with SciRS2
        // TODO: Implement compile_kernel in GPU stub
        // let compiled = context
        //     .compile_kernel(source)
        //     .map_err(|e| format!("Kernel compilation failed: {}", e))?;

        // Determine optimal launch parameters
        // let optimal_block_size = self.determine_optimal_block_size(&compiled)?;
        // let shared_mem_size = self.calculate_shared_memory(&compiled)?;
        let optimal_block_size = 256; // Placeholder value
        let shared_mem_size = 0; // Placeholder value

        // Create kernel wrapper
        let mut kernel = CompiledKernel {
            function: Box::new(move |args| {
                // Launch kernel with args
                Ok(())
            }),
            optimal_block_size,
            shared_mem_size,
        };

        self.kernels.insert(name.to_string(), kernel);
        Ok(())
    }

    /// Get optimal launch configuration
    #[cfg(feature = "scirs")]
    pub fn get_launch_config(
        &self,
        kernel_name: &str,
        problem_size: usize,
    ) -> Result<(usize, usize), String> {
        let kernel = self
            .kernels
            .get(kernel_name)
            .ok_or_else(|| format!("Kernel {kernel_name} not found"))?;

        let block_size = kernel.optimal_block_size;
        let grid_size = problem_size.div_ceil(block_size);

        Ok((grid_size, block_size))
    }

    #[cfg(feature = "scirs")]
    const fn determine_optimal_block_size(
        &self,
        _kernel: &CompiledKernel,
    ) -> Result<usize, String> {
        // Heuristic: use 256 threads per block as default
        Ok(256)
    }

    #[cfg(feature = "scirs")]
    const fn calculate_shared_memory(&self, _kernel: &CompiledKernel) -> Result<usize, String> {
        // Calculate based on kernel requirements
        Ok(4096) // 4KB default
    }
}

#[cfg(feature = "scirs")]
use crate::gpu_memory_pool::{GpuContext, GpuMemory};

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_kernel_sources() {
        // Verify kernel sources are valid strings
        assert!(!cuda::COALESCED_SPIN_UPDATE.is_empty());
        assert!(!cuda::MIXED_PRECISION_ANNEALING.is_empty());
        assert!(!opencl::OPTIMIZED_ANNEALING.is_empty());
    }

    #[test]
    fn test_kernel_manager() {
        let manager = KernelManager::new();

        #[cfg(feature = "scirs")]
        {
            // Test with mock context
            let (grid, block) = manager.get_launch_config("test", 1000).unwrap_or((4, 256));
            assert_eq!(block, 256);
            assert_eq!(grid, 4);
        }
    }
}