scirs2-optimize 0.4.2

//! CUDA kernels for GPU-accelerated optimization algorithms
//!
//! This module provides low-level CUDA kernel implementations for common
//! optimization operations, leveraging scirs2-core's GPU abstractions.

use super::GpuContext;
use crate::error::OptimizeError;
use scirs2_core::gpu::async_execution::GpuStream;
use scirs2_core::gpu::{GpuBuffer, GpuKernelHandle};
use std::sync::Arc;

type ScirsResult<T> = Result<T, OptimizeError>;

/// CUDA kernel for parallel function evaluation
pub struct FunctionEvaluationKernel {
    context: Arc<GpuContext>,
    kernel: GpuKernelHandle,
}

impl FunctionEvaluationKernel {
    /// Create a new function evaluation kernel
    pub fn new(context: Arc<GpuContext>) -> ScirsResult<Self> {
        let kernel_source = r#"
            __global__ void evaluate_batch(
                const double* points,
                double* results,
                int n_points,
                int n_dims,
                int function_type
            ) {
                int idx = blockIdx.x * blockDim.x + threadIdx.x;
                if (idx >= n_points) return;
                
                // Placeholder implementation - would contain actual function evaluation
                double sum = 0.0;
                for (int i = 0; i < n_dims; i++) {
                    double x = points[idx * n_dims + i];
                    sum += x * x;
                }
                results[idx] = sum;
            }
        "#;

        let kernel = context.execute(|compiler| compiler.compile(kernel_source))?;

        Ok(Self { context, kernel })
    }

    /// Evaluate a batch of points using the specified function type
    /// Note: This is a placeholder implementation that compiles correctly
    pub fn evaluate_batch(
        &self,
        points: &GpuBuffer<f64>,
        _function_type: i32,
        _stream: Option<&GpuStream>,
    ) -> ScirsResult<GpuBuffer<f64>> {
        // Placeholder implementation
        let n_points = points.len();
        let results = self.context.create_buffer::<f64>(n_points);

        // In a real implementation, we would launch the kernel here
        // For now, just return a buffer of zeros
        Ok(results)
    }
}

/// CUDA kernel for gradient computation using finite differences
pub struct GradientKernel {
    context: Arc<GpuContext>,
    kernel: GpuKernelHandle,
}

impl GradientKernel {
    /// Create a new gradient computation kernel
    pub fn new(context: Arc<GpuContext>) -> ScirsResult<Self> {
        let kernel_source = r#"
            __global__ void compute_gradient_finite_diff(
                const double* points,
                const double* function_values,
                double* gradients,
                int n_points,
                int n_dims,
                int function_type,
                double h
            ) {
                int idx = blockIdx.x * blockDim.x + threadIdx.x;
                if (idx >= n_points) return;
                
                // Placeholder implementation
                for (int i = 0; i < n_dims; i++) {
                    gradients[idx * n_dims + i] = 0.0;
                }
            }
        "#;

        let kernel = context.execute(|compiler| compiler.compile(kernel_source))?;

        Ok(Self { context, kernel })
    }

    /// Compute gradients for a batch of points using finite differences
    /// Note: This is a placeholder implementation that compiles correctly
    pub fn compute_gradients(
        &self,
        points: &GpuBuffer<f64>,
        _function_values: &GpuBuffer<f64>,
        _function_type: i32,
        _h: f64,
        _stream: Option<&GpuStream>,
    ) -> ScirsResult<GpuBuffer<f64>> {
        // Placeholder implementation
        let n_points = points.len();
        let gradients = self.context.create_buffer::<f64>(n_points);

        // In a real implementation, we would launch the kernel here
        // For now, just return a buffer of zeros
        Ok(gradients)
    }
}

/// CUDA kernel for particle swarm optimization updates
pub struct ParticleSwarmKernel {
    context: Arc<GpuContext>,
    kernel: GpuKernelHandle,
}

impl ParticleSwarmKernel {
    /// Create a new particle swarm kernel
    pub fn new(context: Arc<GpuContext>) -> ScirsResult<Self> {
        let kernel_source = r#"
            __global__ void update_particles(
                double* positions,
                double* velocities,
                const double* personal_best,
                const double* global_best,
                int n_particles,
                int n_dims,
                double w,
                double c1,
                double c2
            ) {
                int idx = blockIdx.x * blockDim.x + threadIdx.x;
                if (idx >= n_particles) return;
                
                // Placeholder implementation
                for (int i = 0; i < n_dims; i++) {
                    velocities[idx * n_dims + i] *= 0.9;
                    positions[idx * n_dims + i] += velocities[idx * n_dims + i];
                }
            }
        "#;

        let kernel = context.execute(|compiler| compiler.compile(kernel_source))?;

        Ok(Self { context, kernel })
    }

    /// Update particle positions and velocities
    /// Note: This is a placeholder implementation that compiles correctly
    pub fn update_particles(
        &self,
        positions: &mut GpuBuffer<f64>,
        velocities: &mut GpuBuffer<f64>,
        _personal_best: &GpuBuffer<f64>,
        _global_best: &GpuBuffer<f64>,
        _w: f64,
        _c1: f64,
        _c2: f64,
        _stream: Option<&GpuStream>,
    ) -> ScirsResult<()> {
        // Placeholder implementation
        let _ = (positions, velocities);
        Ok(())
    }
}

/// CUDA kernel for differential evolution mutations
pub struct DifferentialEvolutionKernel {
    context: Arc<GpuContext>,
    kernel: GpuKernelHandle,
}

impl DifferentialEvolutionKernel {
    /// Create a new differential evolution kernel
    pub fn new(context: Arc<GpuContext>) -> ScirsResult<Self> {
        let kernel_source = r#"
            __global__ void mutate_population(
                const double* population,
                double* mutant_vectors,
                int* indices,
                int n_population,
                int n_dims,
                double F
            ) {
                int idx = blockIdx.x * blockDim.x + threadIdx.x;
                if (idx >= n_population) return;
                
                // Placeholder implementation
                for (int i = 0; i < n_dims; i++) {
                    mutant_vectors[idx * n_dims + i] = population[idx * n_dims + i];
                }
            }
        "#;

        let kernel = context.execute(|compiler| compiler.compile(kernel_source))?;

        Ok(Self { context, kernel })
    }

    /// Generate mutant vectors for differential evolution
    /// Note: This is a placeholder implementation that compiles correctly
    pub fn generate_mutants(
        &self,
        population: &GpuBuffer<f64>,
        _indices: &GpuBuffer<i32>,
        _f: f64,
        _stream: Option<&GpuStream>,
    ) -> ScirsResult<GpuBuffer<f64>> {
        // Placeholder implementation
        let n_points = population.len();
        let mutants = self.context.create_buffer::<f64>(n_points);

        // In a real implementation, we would launch the kernel here
        // For now, just return a buffer of zeros
        Ok(mutants)
    }
}

#[allow(dead_code)]
pub fn placeholder() {
    // Placeholder function to prevent unused module warnings
}