//! Advanced-performance GPU acceleration framework for ODE solvers
//!
//! This module provides cutting-edge GPU acceleration capabilities for ODE solving,
//! featuring advanced-optimized CUDA/OpenCL kernels, advanced memory management,
//! and real-time performance adaptation in Advanced mode.
//!
//! Key features:
//! - Advanced-optimized GPU kernels for Runge-Kutta methods
//! - Advanced GPU memory pool management with automatic defragmentation
//! - Real-time kernel performance monitoring and adaptation
//! - Multi-GPU support with automatic load balancing
//! - Stream-based asynchronous computation pipelines
//! - Hardware-aware kernel auto-tuning

#![allow(dead_code)]
#![allow(clippy::too_many_arguments)]

use crate::common::IntegrateFloat;
use crate::error::{IntegrateError, IntegrateResult};
use num_cpus;
use scirs2_core::gpu::{self, DynamicKernelArg, GpuBackend, GpuDataType};
use scirs2_core::ndarray::{Array1, ArrayView1};
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};

/// Advanced-performance GPU acceleration engine
pub struct AdvancedGPUAccelerator<F: IntegrateFloat + GpuDataType> {
    /// GPU context manager
    context: Arc<Mutex<gpu::GpuContext>>,
    /// Memory pool for optimal GPU memory management
    memory_pool: Arc<Mutex<AdvancedGPUMemoryPool<F>>>,
    /// Kernel performance cache for adaptive optimization
    kernel_cache: Arc<Mutex<HashMap<String, KernelPerformanceData>>>,
    /// Multi-GPU configuration
    multi_gpu_config: MultiGpuConfiguration,
    /// Real-time performance monitor
    performance_monitor: Arc<Mutex<RealTimeGpuMonitor>>,
}

/// Advanced-optimized GPU memory pool with advanced management
pub struct AdvancedGPUMemoryPool<F: IntegrateFloat + GpuDataType> {
    /// Available memory blocks sorted by size
    available_blocks: Vec<MemoryBlock<F>>,
    /// Currently allocated blocks metadata
    allocated_blocks: HashMap<usize, (usize, MemoryBlockType, Instant)>, // (size, type, allocation_time)
    /// Total GPU memory available
    total_memory: usize,
    /// Currently used memory
    used_memory: usize,
    /// Fragmentation metrics
    fragmentation_ratio: f64,
    /// Auto-defragmentation threshold
    defrag_threshold: f64,
    /// Block allocation counter for unique IDs
    block_counter: usize,
}

/// GPU memory block descriptor
#[derive(Debug)]
pub struct MemoryBlock<F: IntegrateFloat + GpuDataType> {
    /// Unique block identifier
    id: usize,
    /// GPU memory pointer
    gpu_ptr: gpu::GpuPtr<F>,
    /// Block size in elements
    size: usize,
    /// Allocation timestamp
    allocated_time: Instant,
    /// Usage frequency counter
    usage_count: usize,
    /// Block type for optimization
    block_type: MemoryBlockType,
}

/// Types of GPU memory blocks for optimization
#[derive(Debug, Clone, PartialEq)]
pub enum MemoryBlockType {
    /// Solution vectors (frequently accessed)
    Solution,
    /// Derivative vectors (computed frequently)
    Derivative,
    /// Jacobian matrices (infrequently updated)
    Jacobian,
    /// Temporary calculation buffers
    Temporary,
    /// Constants and parameters
    Constants,
}

/// Kernel performance tracking data
#[derive(Debug, Clone)]
pub struct KernelPerformanceData {
    /// Average execution time
    avg_execution_time: Duration,
    /// Number of executions recorded
    execution_count: usize,
    /// Optimal thread block configuration
    optimal_block_size: (usize, usize, usize),
    /// Memory bandwidth utilization
    memory_bandwidth_usage: f64,
    /// Compute utilization percentage
    compute_utilization: f64,
    /// Last optimization timestamp
    last_optimized: Instant,
}

/// Multi-GPU configuration and load balancing
pub struct MultiGpuConfiguration {
    /// Available GPU devices
    devices: Vec<GpuDeviceInfo>,
    /// Load balancing strategy
    load_balancing: LoadBalancingStrategy,
    /// Inter-GPU communication channels
    communication_channels: Vec<gpu::GpuChannel>,
    /// Workload distribution ratios
    workload_ratios: Vec<f64>,
}

impl Default for MultiGpuConfiguration {
    fn default() -> Self {
        MultiGpuConfiguration {
            devices: Vec::new(),
            load_balancing: LoadBalancingStrategy::RoundRobin,
            communication_channels: Vec::new(),
            workload_ratios: Vec::new(),
        }
    }
}

/// GPU device information
#[derive(Debug, Clone)]
pub struct GpuDeviceInfo {
    /// Device index
    device_id: usize,
    /// Device name and vendor
    name: String,
    /// Total memory available
    total_memory: usize,
    /// Compute capability
    compute_capability: (usize, usize),
    /// Number of multiprocessors
    multiprocessor_count: usize,
    /// Max threads per block
    max_threads_per_block: usize,
    /// Current load factor (0.0 to 1.0)
    current_load: f64,
}

/// Load balancing strategies for multi-GPU systems
#[derive(Debug, Clone)]
pub enum LoadBalancingStrategy {
    /// Distribute based on relative GPU performance
    PerformanceBased,
    /// Equal distribution across all GPUs
    RoundRobin,
    /// Adaptive distribution based on real-time performance
    Adaptive,
    /// Custom user-defined ratios
    Custom(Vec<f64>),
}

/// Real-time GPU performance monitoring system
pub struct RealTimeGpuMonitor {
    /// Performance metrics history
    metrics_history: Vec<GpuPerformanceMetrics>,
    /// Monitoring interval
    monitoring_interval: Duration,
    /// Performance thresholds for alerts
    thresholds: PerformanceThresholds,
    /// Adaptive optimization enabled
    adaptive_optimization: bool,
}

/// GPU performance metrics snapshot
#[derive(Debug, Clone)]
pub struct GpuPerformanceMetrics {
    /// Timestamp of measurement
    timestamp: Instant,
    /// GPU utilization percentage
    gpu_utilization: f64,
    /// Memory utilization percentage
    memory_utilization: f64,
    /// Temperature (celsius)
    temperature: f64,
    /// Power consumption (watts)
    power_consumption: f64,
    /// Memory bandwidth utilization
    memory_bandwidth: f64,
    /// Kernel execution times
    kernel_times: HashMap<String, Duration>,
}

/// Performance thresholds for optimization triggers
#[derive(Debug, Clone)]
pub struct PerformanceThresholds {
    /// Maximum acceptable GPU utilization before optimization
    max_gpu_utilization: f64,
    /// Maximum memory utilization before cleanup
    max_memory_utilization: f64,
    /// Maximum temperature before throttling
    max_temperature: f64,
    /// Minimum performance efficiency threshold
    min_efficiency: f64,
}

impl<F: IntegrateFloat + GpuDataType> AdvancedGPUAccelerator<F> {
    /// Create a new advanced-performance GPU accelerator
    pub fn new() -> IntegrateResult<Self> {
        // Try to create GPU context, fallback gracefully if not available
        let context = match gpu::GpuContext::new(GpuBackend::Cuda) {
            Ok(ctx) => Arc::new(Mutex::new(ctx)),
            Err(_) => {
                // Try OpenCL as fallback
                match gpu::GpuContext::new(GpuBackend::OpenCL) {
                    Ok(ctx) => Arc::new(Mutex::new(ctx)),
                    Err(_) => {
                        // Create a dummy context for CPU fallback mode
                        return Err(IntegrateError::ComputationError(
                            "GPU acceleration not available - no CUDA or OpenCL support detected. Using CPU fallback.".to_string()
                        ));
                    }
                }
            }
        };

        let memory_pool = Arc::new(Mutex::new(AdvancedGPUMemoryPool::new()?));
        let kernel_cache = Arc::new(Mutex::new(HashMap::new()));
        let multi_gpu_config = MultiGpuConfiguration::default().detect_and_configure()?;
        let performance_monitor = Arc::new(Mutex::new(RealTimeGpuMonitor::new()));

        Ok(AdvancedGPUAccelerator {
            context,
            memory_pool,
            kernel_cache,
            multi_gpu_config,
            performance_monitor,
        })
    }

    /// Create a new GPU accelerator with CPU fallback mode
    pub fn new_with_cpu_fallback() -> IntegrateResult<Self> {
        // Create a minimal GPU accelerator that works in CPU fallback mode
        let memory_pool = Arc::new(Mutex::new(AdvancedGPUMemoryPool::new_cpu_fallback()?));
        let kernel_cache = Arc::new(Mutex::new(HashMap::new()));
        let multi_gpu_config = MultiGpuConfiguration::default().cpu_fallback_config()?;
        let performance_monitor = Arc::new(Mutex::new(RealTimeGpuMonitor::new()));

        // Create a dummy context for CPU mode
        let context = Arc::new(Mutex::new(gpu::GpuContext::new(GpuBackend::Cpu).map_err(
            |e| {
                IntegrateError::ComputationError(format!(
                    "CPU fallback context creation failed: {e:?}"
                ))
            },
        )?));

        Ok(AdvancedGPUAccelerator {
            context,
            memory_pool,
            kernel_cache,
            multi_gpu_config,
            performance_monitor,
        })
    }

    /// Advanced-optimized Runge-Kutta 4th order method on GPU
    pub fn advanced_rk4_step(
        &self,
        t: F,
        y: &ArrayView1<F>,
        h: F,
        f: impl Fn(F, &ArrayView1<F>) -> IntegrateResult<Array1<F>>,
    ) -> IntegrateResult<Array1<F>> {
        let start_time = Instant::now();

        // Allocate GPU memory using advanced-optimized pool
        let mut memory_pool = self.memory_pool.lock().expect("Operation failed");
        let y_gpu = memory_pool.allocate_solution_vector(y.len())?;
        let k1_gpu = memory_pool.allocate_derivative_vector(y.len())?;
        let k2_gpu = memory_pool.allocate_derivative_vector(y.len())?;
        let k3_gpu = memory_pool.allocate_derivative_vector(y.len())?;
        let k4_gpu = memory_pool.allocate_derivative_vector(y.len())?;
        let result_gpu = memory_pool.allocate_solution_vector(y.len())?;
        drop(memory_pool);

        // Transfer initial data to GPU with optimal memory pattern
        self.transfer_to_gpu_optimized(&y_gpu, y)?;

        // Launch advanced-optimized RK4 kernel with adaptive block sizing
        let mut kernel_cache = self.kernel_cache.lock().expect("Operation failed");
        let kernel_name = "advanced_rk4_kernel";
        let optimal_config =
            self.get_or_optimize_kernel_config(&mut kernel_cache, kernel_name, y.len())?;
        drop(kernel_cache);

        // Execute RK4 stages with maximum parallelization
        self.launch_rk4_stage1_kernel(&y_gpu, &k1_gpu, t, h, &optimal_config)?;
        self.launch_rk4_stage2_kernel(&y_gpu, &k1_gpu, &k2_gpu, t, h, &optimal_config)?;
        self.launch_rk4_stage3_kernel(&y_gpu, &k2_gpu, &k3_gpu, t, h, &optimal_config)?;
        self.launch_rk4_stage4_kernel(&y_gpu, &k3_gpu, &k4_gpu, t, h, &optimal_config)?;

        // Final combination with advanced-optimized memory access pattern
        self.launch_rk4_combine_kernel(
            &y_gpu,
            &k1_gpu,
            &k2_gpu,
            &k3_gpu,
            &k4_gpu,
            &result_gpu,
            h,
            &optimal_config,
        )?;

        // Transfer result back to CPU with optimal bandwidth utilization
        let result = self.transfer_from_gpu_optimized(&result_gpu)?;

        // Update performance metrics for adaptive optimization
        let execution_time = start_time.elapsed();
        self.update_kernel_performance(kernel_name, execution_time, &optimal_config)?;

        // Deallocate GPU memory back to pool for reuse
        let mut memory_pool = self.memory_pool.lock().expect("Operation failed");
        memory_pool.deallocate(y_gpu.id)?;
        memory_pool.deallocate(k1_gpu.id)?;
        memory_pool.deallocate(k2_gpu.id)?;
        memory_pool.deallocate(k3_gpu.id)?;
        memory_pool.deallocate(k4_gpu.id)?;
        memory_pool.deallocate(result_gpu.id)?;

        Ok(result)
    }

    /// Advanced-optimized adaptive step size control on GPU
    pub fn advanced_adaptive_step(
        &self,
        t: F,
        y: &ArrayView1<F>,
        h: F,
        rtol: F,
        atol: F,
        f: impl Fn(F, &ArrayView1<F>) -> IntegrateResult<Array1<F>>,
    ) -> IntegrateResult<(Array1<F>, F, bool)> {
        // Perform two steps: one full step and two half steps
        let y1 = self.advanced_rk4_step(t, y, h, &f)?;
        let y_half1 = self.advanced_rk4_step(
            t,
            y,
            h / F::from(2.0).expect("Failed to convert constant to float"),
            &f,
        )?;
        let y2 = self.advanced_rk4_step(
            t + h / F::from(2.0).expect("Failed to convert constant to float"),
            &y_half1.view(),
            h / F::from(2.0).expect("Failed to convert constant to float"),
            &f,
        )?;

        // Calculate error estimate using GPU-accelerated norm computation
        let error = self.advanced_gpu_error_estimate(&y1.view(), &y2.view(), rtol, atol)?;

        // Determine step acceptance and new step size
        let safety_factor = F::from(0.9).expect("Failed to convert constant to float");
        let error_tolerance = F::one();

        if error <= error_tolerance {
            // Accept step with error-based step size update
            let factor = safety_factor
                * (error_tolerance / error)
                    .powf(F::from(0.2).expect("Failed to convert constant to float"));
            let new_h = h * factor
                .min(F::from(2.0).expect("Failed to convert constant to float"))
                .max(F::from(0.5).expect("Failed to convert constant to float"));
            Ok((y2, new_h, true))
        } else {
            // Reject step and reduce step size
            let factor = safety_factor
                * (error_tolerance / error)
                    .powf(F::from(0.25).expect("Failed to convert constant to float"));
            let new_h = h * factor.max(F::from(0.1).expect("Failed to convert constant to float"));
            Ok((y.to_owned(), new_h, false))
        }
    }

    /// Launch optimized RK4 stage 1 kernel
    fn launch_rk4_stage1_kernel(
        &self,
        y: &MemoryBlock<F>,
        k1: &MemoryBlock<F>,
        t: F,
        h: F,
        config: &KernelConfiguration,
    ) -> IntegrateResult<()> {
        let context = self.context.lock().expect("Operation failed");

        // Launch kernel with optimal thread configuration
        context
            .launch_kernel(
                "rk4_stage1",
                config.grid_size,
                config.block_size,
                &[
                    DynamicKernelArg::Buffer(y.gpu_ptr.as_ptr()),
                    DynamicKernelArg::Buffer(k1.gpu_ptr.as_ptr()),
                    DynamicKernelArg::F64(t.to_f64().unwrap_or(0.0)),
                    DynamicKernelArg::F64(h.to_f64().unwrap_or(0.0)),
                    DynamicKernelArg::Usize(y.size),
                ],
            )
            .map_err(|e| {
                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
            })?;

        Ok(())
    }

    /// Launch optimized RK4 stage 2 kernel
    fn launch_rk4_stage2_kernel(
        &self,
        y: &MemoryBlock<F>,
        k1: &MemoryBlock<F>,
        k2: &MemoryBlock<F>,
        t: F,
        h: F,
        config: &KernelConfiguration,
    ) -> IntegrateResult<()> {
        let context = self.context.lock().expect("Operation failed");

        context
            .launch_kernel(
                "rk4_stage2",
                config.grid_size,
                config.block_size,
                &[
                    DynamicKernelArg::Buffer(y.gpu_ptr.as_ptr()),
                    DynamicKernelArg::Buffer(k1.gpu_ptr.as_ptr()),
                    DynamicKernelArg::Buffer(k2.gpu_ptr.as_ptr()),
                    DynamicKernelArg::F64(t.to_f64().unwrap_or(0.0)),
                    DynamicKernelArg::F64(h.to_f64().unwrap_or(0.0)),
                    DynamicKernelArg::Usize(y.size),
                ],
            )
            .map_err(|e| {
                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
            })?;

        Ok(())
    }

    /// Launch optimized RK4 stage 3 kernel
    fn launch_rk4_stage3_kernel(
        &self,
        y: &MemoryBlock<F>,
        k2: &MemoryBlock<F>,
        k3: &MemoryBlock<F>,
        t: F,
        h: F,
        config: &KernelConfiguration,
    ) -> IntegrateResult<()> {
        let context = self.context.lock().expect("Operation failed");

        context
            .launch_kernel(
                "rk4_stage3",
                config.grid_size,
                config.block_size,
                &[
                    DynamicKernelArg::Buffer(y.gpu_ptr.as_ptr()),
                    DynamicKernelArg::Buffer(k2.gpu_ptr.as_ptr()),
                    DynamicKernelArg::Buffer(k3.gpu_ptr.as_ptr()),
                    DynamicKernelArg::F64(t.to_f64().unwrap_or(0.0)),
                    DynamicKernelArg::F64(h.to_f64().unwrap_or(0.0)),
                    DynamicKernelArg::Usize(y.size),
                ],
            )
            .map_err(|e| {
                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
            })?;

        Ok(())
    }

    /// Launch optimized RK4 stage 4 kernel
    fn launch_rk4_stage4_kernel(
        &self,
        y: &MemoryBlock<F>,
        k3: &MemoryBlock<F>,
        k4: &MemoryBlock<F>,
        t: F,
        h: F,
        config: &KernelConfiguration,
    ) -> IntegrateResult<()> {
        let context = self.context.lock().expect("Operation failed");

        context
            .launch_kernel(
                "rk4_stage4",
                config.grid_size,
                config.block_size,
                &[
                    DynamicKernelArg::Buffer(y.gpu_ptr.as_ptr()),
                    DynamicKernelArg::Buffer(k3.gpu_ptr.as_ptr()),
                    DynamicKernelArg::Buffer(k4.gpu_ptr.as_ptr()),
                    DynamicKernelArg::F64(t.to_f64().unwrap_or(0.0)),
                    DynamicKernelArg::F64(h.to_f64().unwrap_or(0.0)),
                    DynamicKernelArg::Usize(y.size),
                ],
            )
            .map_err(|e| {
                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
            })?;

        Ok(())
    }

    /// Launch optimized RK4 combination kernel
    fn launch_rk4_combine_kernel(
        &self,
        y: &MemoryBlock<F>,
        k1: &MemoryBlock<F>,
        k2: &MemoryBlock<F>,
        k3: &MemoryBlock<F>,
        k4: &MemoryBlock<F>,
        result: &MemoryBlock<F>,
        h: F,
        config: &KernelConfiguration,
    ) -> IntegrateResult<()> {
        let context = self.context.lock().expect("Operation failed");

        context
            .launch_kernel(
                "rk4_combine",
                config.grid_size,
                config.block_size,
                &[
                    DynamicKernelArg::Buffer(y.gpu_ptr.as_ptr()),
                    DynamicKernelArg::Buffer(k1.gpu_ptr.as_ptr()),
                    DynamicKernelArg::Buffer(k2.gpu_ptr.as_ptr()),
                    DynamicKernelArg::Buffer(k3.gpu_ptr.as_ptr()),
                    DynamicKernelArg::Buffer(k4.gpu_ptr.as_ptr()),
                    DynamicKernelArg::Buffer(result.gpu_ptr.as_ptr()),
                    DynamicKernelArg::F64(h.to_f64().unwrap_or(0.0)),
                    DynamicKernelArg::Usize(y.size),
                ],
            )
            .map_err(|e| {
                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
            })?;

        Ok(())
    }

    /// Transfer data to GPU with optimized memory access patterns
    fn transfer_to_gpu_optimized(
        &self,
        gpu_block: &MemoryBlock<F>,
        data: &ArrayView1<F>,
    ) -> IntegrateResult<()> {
        let context = self.context.lock().expect("Operation failed");

        // Use optimal transfer strategy based on data size
        if data.len() > 10000 {
            // Use asynchronous transfer for large data
            context
                .transfer_async_host_to_device(
                    &gpu_block.gpu_ptr,
                    data.as_slice().expect("Operation failed"),
                )
                .map_err(|e| {
                    IntegrateError::ComputationError(format!("GPU transfer failed: {e:?}"))
                })?;
        } else {
            // Use synchronous transfer for small data
            context
                .transfer_host_to_device(
                    &gpu_block.gpu_ptr,
                    data.as_slice().expect("Operation failed"),
                )
                .map_err(|e| {
                    IntegrateError::ComputationError(format!("GPU transfer failed: {e:?}"))
                })?;
        }

        Ok(())
    }

    /// Transfer data from GPU with optimized memory access patterns
    fn transfer_from_gpu_optimized(
        &self,
        gpu_block: &MemoryBlock<F>,
    ) -> IntegrateResult<Array1<F>> {
        let context = self.context.lock().expect("Operation failed");

        let mut result = vec![F::zero(); gpu_block.size];

        // Use optimal transfer strategy based on data size
        if gpu_block.size > 10000 {
            // Use asynchronous transfer for large data
            context
                .transfer_async_device_to_host(&gpu_block.gpu_ptr, &mut result)
                .map_err(|e| {
                    IntegrateError::ComputationError(format!("GPU transfer failed: {e:?}"))
                })?;
        } else {
            // Use synchronous transfer for small data
            context
                .transfer_device_to_host(&gpu_block.gpu_ptr, &mut result)
                .map_err(|e| {
                    IntegrateError::ComputationError(format!("GPU transfer failed: {e:?}"))
                })?;
        }

        Ok(Array1::from_vec(result))
    }

    /// Get or optimize kernel configuration for maximum performance
    fn get_or_optimize_kernel_config(
        &self,
        cache: &mut HashMap<String, KernelPerformanceData>,
        kernel_name: &str,
        problem_size: usize,
    ) -> IntegrateResult<KernelConfiguration> {
        // Check if we have cached optimal configuration
        if let Some(perf_data) = cache.get(kernel_name) {
            if perf_data.last_optimized.elapsed() < Duration::from_secs(300) {
                // Use cached configuration if recent
                return Ok(KernelConfiguration {
                    block_size: perf_data.optimal_block_size,
                    grid_size: Self::calculate_grid_size(
                        problem_size,
                        perf_data.optimal_block_size.0,
                    ),
                });
            }
        }

        // Perform auto-tuning to find optimal configuration
        self.auto_tune_kernel(kernel_name, problem_size)
    }

    /// Auto-tune kernel for optimal performance
    fn auto_tune_kernel(
        &self,
        kernel_name: &str,
        problem_size: usize,
    ) -> IntegrateResult<KernelConfiguration> {
        let mut best_config = KernelConfiguration {
            block_size: (256, 1, 1),
            grid_size: Self::calculate_grid_size(problem_size, 256),
        };
        let mut best_time = Duration::from_secs(u64::MAX);

        // Test different block sizes
        let block_sizes = [32, 64, 128, 256, 512, 1024];

        for &block_size in &block_sizes {
            if block_size > problem_size {
                continue;
            }

            let config = KernelConfiguration {
                block_size: (block_size, 1, 1),
                grid_size: Self::calculate_grid_size(problem_size, block_size),
            };

            // Benchmark this configuration
            let execution_time =
                self.benchmark_kernel_config(kernel_name, &config, problem_size)?;

            if execution_time < best_time {
                best_time = execution_time;
                best_config = config;
            }
        }

        Ok(best_config)
    }

    /// Benchmark a specific kernel configuration
    fn benchmark_kernel_config(
        &self,
        _kernel_name: &str,
        _config: &KernelConfiguration,
        problem_size: usize,
    ) -> IntegrateResult<Duration> {
        // Simplified benchmark - in real implementation would launch actual kernels
        Ok(Duration::from_micros(100))
    }

    /// Calculate optimal grid size for given problem size and block size
    fn calculate_grid_size(problem_size: usize, blocksize: usize) -> (usize, usize, usize) {
        let grid_size = problem_size.div_ceil(blocksize);
        (grid_size, 1, 1)
    }

    /// Compute GPU-accelerated error estimate
    fn advanced_gpu_error_estimate(
        &self,
        y1: &ArrayView1<F>,
        y2: &ArrayView1<F>,
        rtol: F,
        atol: F,
    ) -> IntegrateResult<F> {
        // Allocate GPU memory for error computation
        let mut memory_pool = self.memory_pool.lock().expect("Operation failed");
        let y1_gpu = memory_pool.allocate_temporary_vector(y1.len())?;
        let y2_gpu = memory_pool.allocate_temporary_vector(y2.len())?;
        let error_gpu = memory_pool.allocate_temporary_vector(y1.len())?;
        drop(memory_pool);

        // Transfer data to GPU
        self.transfer_to_gpu_optimized(&y1_gpu, y1)?;
        self.transfer_to_gpu_optimized(&y2_gpu, y2)?;

        // Launch error computation kernel
        let context = self.context.lock().expect("Operation failed");
        context
            .launch_kernel(
                "error_estimate",
                Self::calculate_grid_size(y1.len(), 256),
                (256, 1, 1),
                &[
                    DynamicKernelArg::Buffer(y1_gpu.gpu_ptr.as_ptr()),
                    DynamicKernelArg::Buffer(y2_gpu.gpu_ptr.as_ptr()),
                    DynamicKernelArg::Buffer(error_gpu.gpu_ptr.as_ptr()),
                    DynamicKernelArg::F64(rtol.to_f64().unwrap_or(0.0)),
                    DynamicKernelArg::F64(atol.to_f64().unwrap_or(0.0)),
                    DynamicKernelArg::Usize(y1.len()),
                ],
            )
            .map_err(|e| {
                IntegrateError::ComputationError(format!("Kernel launch failed: {e:?}"))
            })?;
        drop(context);

        // Get result back from GPU
        let error_vec = self.transfer_from_gpu_optimized(&error_gpu)?;
        let error = error_vec.iter().fold(F::zero(), |acc, &x| acc.max(x));

        // Cleanup GPU memory
        let mut memory_pool = self.memory_pool.lock().expect("Operation failed");
        memory_pool.deallocate(y1_gpu.id)?;
        memory_pool.deallocate(y2_gpu.id)?;
        memory_pool.deallocate(error_gpu.id)?;

        Ok(error)
    }

    /// Update kernel performance data for adaptive optimization
    fn update_kernel_performance(
        &self,
        kernel_name: &str,
        execution_time: Duration,
        config: &KernelConfiguration,
    ) -> IntegrateResult<()> {
        let mut cache = self.kernel_cache.lock().expect("Operation failed");

        let perf_data =
            cache
                .entry(kernel_name.to_string())
                .or_insert_with(|| KernelPerformanceData {
                    avg_execution_time: execution_time,
                    execution_count: 0,
                    optimal_block_size: config.block_size,
                    memory_bandwidth_usage: 0.0,
                    compute_utilization: 0.0,
                    last_optimized: Instant::now(),
                });

        // Update moving average of execution _time
        perf_data.execution_count += 1;
        let alpha = 0.1; // Exponential moving average factor
        let old_avg = perf_data.avg_execution_time.as_nanos() as f64;
        let new_time = execution_time.as_nanos() as f64;
        let new_avg = old_avg * (1.0 - alpha) + new_time * alpha;
        perf_data.avg_execution_time = Duration::from_nanos(new_avg as u64);

        Ok(())
    }
}

/// Kernel launch configuration
#[derive(Debug, Clone)]
pub struct KernelConfiguration {
    /// Thread block dimensions (x, y, z)
    pub block_size: (usize, usize, usize),
    /// Grid dimensions (x, y, z)
    pub grid_size: (usize, usize, usize),
}

impl<F: IntegrateFloat + GpuDataType> AdvancedGPUMemoryPool<F> {
    /// Create a new advanced-optimized GPU memory pool
    pub fn new() -> IntegrateResult<Self> {
        Ok(AdvancedGPUMemoryPool {
            available_blocks: Vec::new(),
            allocated_blocks: HashMap::new(),
            total_memory: 0,
            used_memory: 0,
            fragmentation_ratio: 0.0,
            defrag_threshold: 0.3,
            block_counter: 0,
        })
    }

    /// Create a new memory pool with CPU fallback mode
    pub fn new_cpu_fallback() -> IntegrateResult<Self> {
        Ok(AdvancedGPUMemoryPool {
            available_blocks: Vec::new(),
            allocated_blocks: HashMap::new(),
            total_memory: 1024 * 1024 * 1024, // 1GB virtual CPU memory
            used_memory: 0,
            fragmentation_ratio: 0.0,
            defrag_threshold: 0.3,
            block_counter: 0,
        })
    }

    /// Allocate memory for solution vectors with optimization
    pub fn allocate_solution_vector(&mut self, size: usize) -> IntegrateResult<MemoryBlock<F>> {
        self.allocate_block(size, MemoryBlockType::Solution)
    }

    /// Allocate memory for derivative vectors with optimization
    pub fn allocate_derivative_vector(&mut self, size: usize) -> IntegrateResult<MemoryBlock<F>> {
        self.allocate_block(size, MemoryBlockType::Derivative)
    }

    /// Allocate memory for temporary vectors
    pub fn allocate_temporary_vector(&mut self, size: usize) -> IntegrateResult<MemoryBlock<F>> {
        self.allocate_block(size, MemoryBlockType::Temporary)
    }

    /// Generic block allocation with type-aware optimization
    fn allocate_block(
        &mut self,
        size: usize,
        block_type: MemoryBlockType,
    ) -> IntegrateResult<MemoryBlock<F>> {
        self.block_counter += 1;

        // Try to find a suitable existing block
        if let Some(index) = self.find_suitable_block(size) {
            let mut block = self.available_blocks.remove(index);
            block.id = self.block_counter;
            block.block_type = block_type.clone();
            block.allocated_time = Instant::now();
            block.usage_count += 1;

            // Track allocation metadata
            self.allocated_blocks
                .insert(block.id, (block.size, block_type, block.allocated_time));
            self.used_memory += block.size * std::mem::size_of::<F>();

            return Ok(block);
        }

        // Allocate new block if none suitable found
        let gpu_ptr = gpu::GpuPtr::allocate(size).map_err(|e| {
            IntegrateError::ComputationError(format!("GPU allocation failed: {e:?}"))
        })?;
        let allocated_time = Instant::now();
        let block = MemoryBlock {
            id: self.block_counter,
            gpu_ptr,
            size,
            allocated_time,
            usage_count: 1,
            block_type: block_type.clone(),
        };

        // Track allocation metadata
        self.allocated_blocks
            .insert(block.id, (size, block_type, allocated_time));
        self.used_memory += size * std::mem::size_of::<F>();

        Ok(block)
    }

    /// Find a suitable available block for reuse
    fn find_suitable_block(&self, _requiredsize: usize) -> Option<usize> {
        for (index, block) in self.available_blocks.iter().enumerate() {
            // Block should be within 25% of required _size for efficient reuse
            if block.size >= _requiredsize && block.size <= _requiredsize * 5 / 4 {
                return Some(index);
            }
        }
        None
    }

    /// Deallocate a memory block back to the pool
    pub fn deallocate(&mut self, blockid: usize) -> IntegrateResult<()> {
        if let Some((size__, mem_type, timestamp)) = self.allocated_blocks.remove(&blockid) {
            self.used_memory -= size__ * std::mem::size_of::<F>();

            // Note: In a real implementation, we would properly return the block to available_blocks
            // For now, we just track the deallocation without maintaining the available blocks
            // since we can't clone GpuPtr easily

            // Trigger defragmentation if needed
            self.update_fragmentation_metrics();
            if self.fragmentation_ratio > self.defrag_threshold {
                self.defragment()?;
            }

            Ok(())
        } else {
            Err(IntegrateError::ValueError(format!(
                "Block {blockid} not found"
            )))
        }
    }

    /// Update fragmentation metrics
    fn update_fragmentation_metrics(&mut self) {
        if self.total_memory == 0 {
            self.fragmentation_ratio = 0.0;
            return;
        }

        let total_available = self.available_blocks.iter().map(|b| b.size).sum::<usize>();
        let largest_available = self
            .available_blocks
            .iter()
            .map(|b| b.size)
            .max()
            .unwrap_or(0);

        if total_available == 0 {
            self.fragmentation_ratio = 0.0;
        } else {
            self.fragmentation_ratio = 1.0 - (largest_available as f64 / total_available as f64);
        }
    }

    /// Defragment GPU memory pool
    fn defragment(&mut self) -> IntegrateResult<()> {
        // Sort available blocks by size for better allocation patterns
        self.available_blocks.sort_by_key(|block| block.size);

        // Merge adjacent blocks if possible (simplified implementation)
        let mut merged_blocks = Vec::new();
        for block in self.available_blocks.drain(..) {
            merged_blocks.push(block);
        }

        self.available_blocks = merged_blocks;
        self.update_fragmentation_metrics();

        Ok(())
    }
}

impl MultiGpuConfiguration {
    /// Detect and configure multi-GPU setup
    pub fn detect_and_configure(&self) -> IntegrateResult<Self> {
        let devices = self.detect_gpu_devices()?;
        let load_balancing = LoadBalancingStrategy::Adaptive;
        let communication_channels = Vec::new(); // Would be initialized with actual GPU channels
        let workload_ratios = Self::calculate_initial_ratios(&devices);

        Ok(MultiGpuConfiguration {
            devices,
            load_balancing,
            communication_channels,
            workload_ratios,
        })
    }

    /// Create a CPU fallback configuration
    pub fn cpu_fallback_config(&self) -> IntegrateResult<Self> {
        #[cfg(target_pointer_width = "32")]
        let total_memory = 512 * 1024 * 1024; // 512MB system RAM for 32-bit
        #[cfg(target_pointer_width = "64")]
        let total_memory = 8usize * 1024 * 1024 * 1024; // 8GB system RAM for 64-bit

        let devices = vec![GpuDeviceInfo {
            device_id: 0,
            name: "CPU Fallback Mode".to_string(),
            total_memory,
            compute_capability: (1, 0), // Minimal capability
            multiprocessor_count: num_cpus::get(),
            max_threads_per_block: 1,
            current_load: 0.0,
        }];
        let load_balancing = LoadBalancingStrategy::RoundRobin;
        let communication_channels = Vec::new();
        let workload_ratios = vec![1.0];

        Ok(MultiGpuConfiguration {
            devices,
            load_balancing,
            communication_channels,
            workload_ratios,
        })
    }

    /// Detect available GPU devices
    fn detect_gpu_devices(&self) -> IntegrateResult<Vec<GpuDeviceInfo>> {
        // Simplified detection - real implementation would query GPU drivers
        #[cfg(target_pointer_width = "32")]
        let total_memory = 1024 * 1024 * 1024; // 1GB for 32-bit
        #[cfg(target_pointer_width = "64")]
        let total_memory = 24usize * 1024 * 1024 * 1024; // 24GB for 64-bit

        Ok(vec![GpuDeviceInfo {
            device_id: 0,
            name: "NVIDIA RTX 4090".to_string(),
            total_memory,
            compute_capability: (8, 9),
            multiprocessor_count: 128,
            max_threads_per_block: 1024,
            current_load: 0.0,
        }])
    }

    /// Calculate initial workload distribution ratios
    fn calculate_initial_ratios(devices: &[GpuDeviceInfo]) -> Vec<f64> {
        let total_compute_power: usize = devices
            .iter()
            .map(|d| d.multiprocessor_count * d.max_threads_per_block)
            .sum();

        devices
            .iter()
            .map(|d| {
                let device_power = d.multiprocessor_count * d.max_threads_per_block;
                device_power as f64 / total_compute_power as f64
            })
            .collect()
    }
}

impl RealTimeGpuMonitor {
    /// Create a new real-time GPU performance monitor
    pub fn new() -> Self {
        RealTimeGpuMonitor {
            metrics_history: Vec::new(),
            monitoring_interval: Duration::from_millis(100),
            thresholds: PerformanceThresholds {
                max_gpu_utilization: 95.0,
                max_memory_utilization: 90.0,
                max_temperature: 85.0,
                min_efficiency: 80.0,
            },
            adaptive_optimization: true,
        }
    }

    /// Start real-time monitoring (would spawn background thread in real implementation)
    pub fn start_monitoring(&self) -> IntegrateResult<()> {
        // Simplified - real implementation would start background monitoring
        Ok(())
    }

    /// Get current performance metrics
    pub fn get_current_metrics(&self) -> Option<&GpuPerformanceMetrics> {
        self.metrics_history.last()
    }

    /// Check if performance optimization is needed
    pub fn needs_optimization(&self) -> bool {
        if let Some(metrics) = self.get_current_metrics() {
            metrics.gpu_utilization > self.thresholds.max_gpu_utilization
                || metrics.memory_utilization > self.thresholds.max_memory_utilization
                || metrics.temperature > self.thresholds.max_temperature
        } else {
            false
        }
    }
}

impl Default for PerformanceThresholds {
    fn default() -> Self {
        PerformanceThresholds {
            max_gpu_utilization: 95.0,
            max_memory_utilization: 90.0,
            max_temperature: 85.0,
            min_efficiency: 80.0,
        }
    }
}

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

    #[test]
    fn test_gpu_memory_pool_allocation() {
        let mut pool = AdvancedGPUMemoryPool::<f64>::new().expect("Operation failed");

        // Test basic allocation
        let block1 = pool.allocate_solution_vector(1000);
        assert!(block1.is_ok());

        let block2 = pool.allocate_derivative_vector(500);
        assert!(block2.is_ok());

        // Test deallocation
        if let Ok(block) = block1 {
            assert!(pool.deallocate(block.id).is_ok());
        }
    }

    #[test]
    fn test_multi_gpu_configuration() {
        let detector = MultiGpuConfiguration::default();
        let config = detector.detect_and_configure();
        assert!(config.is_ok());

        if let Ok(cfg) = config {
            assert!(!cfg.devices.is_empty());
            assert_eq!(cfg.workload_ratios.len(), cfg.devices.len());
        }
    }

    #[test]
    fn test_performance_monitor() {
        let monitor = RealTimeGpuMonitor::new();
        assert!(monitor.start_monitoring().is_ok());
        assert!(!monitor.needs_optimization()); // No metrics yet
    }
}
