torsh-backend 0.1.2

//! CUDA Memory Management Coordinator
//!
//! This module provides the main coordination layer that orchestrates all CUDA memory
//! management operations across device, unified, and pinned memory types. It serves as
//! the primary interface for high-level memory operations while coordinating with
//! specialized subsystems for optimization, statistics, and pool management.

use std::collections::HashMap;
use std::sync::{Arc, Mutex, RwLock};
use std::thread;
use std::time::{Duration, Instant};

use crate::cuda::memory::allocation::{
    AllocationPriority, AllocationStrategy, AllocationType, CudaMemoryAllocation, MemoryAlignment,
};
use crate::cuda::memory::device_memory::CudaMemoryManager as DeviceMemoryManager;
use crate::cuda::memory::memory_pools::UnifiedMemoryPoolManager;
// Use placeholder types (optimization module disabled)
use super::{
    CudaMemoryOptimizationEngine, MLOptimizationConfig, OptimizationResult, OptimizationStrategy,
};
use crate::cuda::memory::pinned_memory::PinnedMemoryManager;
use crate::cuda::memory::statistics::{
    CudaMemoryStatisticsManager, MemoryUsageStatistics, PerformanceMetrics,
};
use crate::cuda::memory::unified_memory::UnifiedMemoryManager;

/// Configuration for the CUDA Memory Manager
#[derive(Debug, Clone)]
pub struct CudaMemoryManagerConfig {
    /// Maximum total memory usage across all devices (in bytes)
    pub max_total_memory: Option<usize>,
    /// Per-device memory limits
    pub per_device_limits: HashMap<usize, usize>,
    /// Enable automatic memory optimization
    pub enable_optimization: bool,
    /// Statistics collection interval
    pub stats_collection_interval: Duration,
    /// Enable cross-device memory sharing
    pub enable_cross_device_sharing: bool,
    /// Optimization target configuration
    pub optimization_config: MLOptimizationConfig,
    /// Pool configuration settings
    pub pool_config: PoolManagerConfig,
    /// Memory pressure thresholds
    pub pressure_thresholds: MemoryPressureThresholds,
    /// Enable predictive allocation
    pub enable_predictive_allocation: bool,
}

/// Pool manager configuration
#[derive(Debug, Clone)]
pub struct PoolManagerConfig {
    /// Initial pool sizes per device
    pub initial_pool_sizes: HashMap<usize, usize>,
    /// Pool growth strategies
    pub growth_strategies: HashMap<AllocationStrategy, f32>,
    /// Enable adaptive pool sizing
    pub enable_adaptive_sizing: bool,
    /// Pool compaction intervals
    pub compaction_interval: Duration,
}

/// Memory pressure thresholds
#[derive(Debug, Clone)]
pub struct MemoryPressureThresholds {
    /// Threshold for low memory warning (0.0-1.0)
    pub low_memory_warning: f32,
    /// Threshold for high memory pressure (0.0-1.0)
    pub high_memory_pressure: f32,
    /// Threshold for critical memory state (0.0-1.0)
    pub critical_memory_state: f32,
}

/// Memory management operation result
#[derive(Debug)]
pub enum ManagerOperationResult<T> {
    Success(T),
    PartialSuccess(T, Vec<String>),
    Failure(String),
    RequiresOptimization(String),
}

/// System-wide memory management coordinator
pub struct CudaMemoryManagerCoordinator {
    /// Configuration
    config: CudaMemoryManagerConfig,

    /// Device memory managers per device
    device_managers: RwLock<HashMap<usize, Arc<DeviceMemoryManager>>>,

    /// Unified memory manager
    unified_manager: Arc<UnifiedMemoryManager>,

    /// Pinned memory manager
    pinned_manager: Arc<PinnedMemoryManager>,

    /// Pool coordination manager
    pool_manager: Arc<UnifiedMemoryPoolManager>,

    /// Statistics collection system
    statistics_manager: Arc<CudaMemoryStatisticsManager>,

    /// Optimization engine
    optimization_engine: Arc<CudaMemoryOptimizationEngine>,

    /// System state tracking
    system_state: Arc<Mutex<SystemState>>,

    /// Operation history for learning
    operation_history: Arc<Mutex<OperationHistory>>,

    /// Background task handles
    background_tasks: Arc<Mutex<Vec<thread::JoinHandle<()>>>>,

    /// Emergency protocols
    emergency_protocols: Arc<Mutex<EmergencyProtocols>>,

    /// Resource allocation predictor
    allocation_predictor: Arc<Mutex<AllocationPredictor>>,
}

/// System state tracking
#[derive(Debug)]
struct SystemState {
    /// Total allocated memory across all devices
    total_allocated: usize,
    /// Per-device allocation tracking
    device_allocations: HashMap<usize, usize>,
    /// Current memory pressure level
    memory_pressure_level: MemoryPressureLevel,
    /// Active optimization strategies
    active_optimizations: Vec<OptimizationStrategy>,
    /// System health status
    health_status: SystemHealthStatus,
    /// Last maintenance timestamp
    last_maintenance: Instant,
}

/// Memory pressure levels
#[derive(Debug, Clone, PartialEq)]
pub enum MemoryPressureLevel {
    Normal,
    Low,
    High,
    Critical,
}

/// System health status
#[derive(Debug, Clone)]
pub enum SystemHealthStatus {
    Healthy,
    Warning(Vec<String>),
    Degraded(Vec<String>),
    Critical(Vec<String>),
}

/// Operation history tracking
#[derive(Debug)]
struct OperationHistory {
    /// Recent allocation patterns
    allocation_patterns: Vec<AllocationPattern>,
    /// Performance history
    performance_history: Vec<PerformanceSnapshot>,
    /// Optimization effectiveness
    optimization_effectiveness: HashMap<OptimizationStrategy, f32>,
    /// Error patterns
    error_patterns: Vec<ErrorPattern>,
}

/// Allocation pattern tracking
#[derive(Debug)]
struct AllocationPattern {
    timestamp: Instant,
    allocation_type: AllocationType,
    size: usize,
    device_id: usize,
    success: bool,
    duration: Duration,
}

/// Performance snapshot
#[derive(Debug)]
struct PerformanceSnapshot {
    timestamp: Instant,
    memory_utilization: f32,
    allocation_success_rate: f32,
    average_allocation_time: Duration,
    optimization_overhead: Duration,
}

/// Error pattern tracking
#[derive(Debug)]
struct ErrorPattern {
    timestamp: Instant,
    error_type: String,
    context: HashMap<String, String>,
    recovery_strategy: Option<String>,
}

/// Emergency protocol management
#[derive(Debug)]
struct EmergencyProtocols {
    /// Emergency memory release strategies
    release_strategies: Vec<EmergencyReleaseStrategy>,
    /// Fallback allocation methods
    fallback_methods: Vec<FallbackAllocationMethod>,
    /// System recovery procedures
    recovery_procedures: Vec<RecoveryProcedure>,
}

/// Emergency memory release strategy
#[derive(Debug)]
struct EmergencyReleaseStrategy {
    name: String,
    priority: u8,
    execute: Box<dyn Fn(&CudaMemoryManagerCoordinator) -> Result<usize, String> + Send + Sync>,
}

/// Fallback allocation method
#[derive(Debug)]
struct FallbackAllocationMethod {
    name: String,
    conditions: Vec<String>,
    execute: Box<
        dyn Fn(usize, AllocationType) -> Result<Box<dyn CudaMemoryAllocation>, String>
            + Send
            + Sync,
    >,
}

/// System recovery procedure
#[derive(Debug)]
struct RecoveryProcedure {
    name: String,
    trigger_conditions: Vec<String>,
    recovery_steps: Vec<String>,
    execute: Box<dyn Fn(&CudaMemoryManagerCoordinator) -> Result<(), String> + Send + Sync>,
}

/// Allocation predictor for proactive management
#[derive(Debug)]
struct AllocationPredictor {
    /// Historical allocation patterns
    historical_patterns: Vec<AllocationPattern>,
    /// Prediction models
    prediction_models: HashMap<String, PredictionModel>,
    /// Confidence thresholds
    confidence_thresholds: HashMap<String, f32>,
}

/// Prediction model interface
#[derive(Debug)]
struct PredictionModel {
    name: String,
    accuracy: f32,
    last_training: Instant,
    predict: Box<dyn Fn(&[AllocationPattern]) -> Vec<PredictedAllocation> + Send + Sync>,
}

/// Predicted allocation
#[derive(Debug)]
struct PredictedAllocation {
    predicted_time: Instant,
    allocation_type: AllocationType,
    predicted_size: usize,
    confidence: f32,
    suggested_device: usize,
}

impl Default for CudaMemoryManagerConfig {
    fn default() -> Self {
        Self {
            max_total_memory: None,
            per_device_limits: HashMap::new(),
            enable_optimization: true,
            stats_collection_interval: Duration::from_millis(100),
            enable_cross_device_sharing: true,
            optimization_config: MLOptimizationConfig::default(),
            pool_config: PoolManagerConfig::default(),
            pressure_thresholds: MemoryPressureThresholds::default(),
            enable_predictive_allocation: true,
        }
    }
}

impl Default for PoolManagerConfig {
    fn default() -> Self {
        Self {
            initial_pool_sizes: HashMap::new(),
            growth_strategies: HashMap::new(),
            enable_adaptive_sizing: true,
            compaction_interval: Duration::from_secs(60),
        }
    }
}

impl Default for MemoryPressureThresholds {
    fn default() -> Self {
        Self {
            low_memory_warning: 0.7,
            high_memory_pressure: 0.85,
            critical_memory_state: 0.95,
        }
    }
}

impl CudaMemoryManagerCoordinator {
    /// Create a new memory manager coordinator
    pub fn new(config: CudaMemoryManagerConfig) -> Result<Self, String> {
        let statistics_manager = Arc::new(CudaMemoryStatisticsManager::new(
            crate::cuda::memory::statistics::StatisticsConfig::default()
        ));
        let optimization_engine = Arc::new(CudaMemoryOptimizationEngine::new(
            config.optimization_config.clone(),
        ));

        let unified_manager = Arc::new(UnifiedMemoryManager::new());
        let pinned_manager = Arc::new(PinnedMemoryManager::new(
            crate::cuda::memory::pinned_memory::PinnedMemoryConfig::default()
        )?);
        let pool_manager = Arc::new(UnifiedMemoryPoolManager::new(
            crate::cuda::memory::memory_pools::PoolManagerConfig::default()
        ));

        let coordinator = Self {
            config,
            device_managers: RwLock::new(HashMap::new()),
            unified_manager,
            pinned_manager,
            pool_manager,
            statistics_manager,
            optimization_engine,
            system_state: Arc::new(Mutex::new(SystemState::new())),
            operation_history: Arc::new(Mutex::new(OperationHistory::new())),
            background_tasks: Arc::new(Mutex::new(Vec::new())),
            emergency_protocols: Arc::new(Mutex::new(EmergencyProtocols::new())),
            allocation_predictor: Arc::new(Mutex::new(AllocationPredictor::new())),
        };

        coordinator.initialize_background_tasks()?;
        coordinator.setup_emergency_protocols()?;

        Ok(coordinator)
    }

    /// Initialize device managers for available devices
    pub fn initialize_devices(&self, device_ids: &[usize]) -> Result<(), String> {
        let mut device_managers = self
            .device_managers
            .write()
            .map_err(|e| format!("Failed to acquire device managers lock: {}", e))?;

        for &device_id in device_ids {
            if !device_managers.contains_key(&device_id) {
                let manager = Arc::new(DeviceMemoryManager::new(device_id)?);
                device_managers.insert(device_id, manager);
            }
        }

        Ok(())
    }

    /// Allocate memory with intelligent routing
    pub fn allocate_memory(
        &self,
        size: usize,
        allocation_type: AllocationType,
        device_id: Option<usize>,
        alignment: Option<MemoryAlignment>,
        priority: AllocationPriority,
    ) -> ManagerOperationResult<Box<dyn CudaMemoryAllocation>> {
        let start_time = Instant::now();

        // Record allocation attempt
        self.record_allocation_attempt(size, allocation_type, device_id);

        // Check system pressure and apply optimizations if needed
        match self.check_memory_pressure() {
            MemoryPressureLevel::Critical => {
                if let Err(e) = self.execute_emergency_protocols() {
                    return ManagerOperationResult::Failure(format!(
                        "Critical memory pressure and emergency protocols failed: {}",
                        e
                    ));
                }
            }
            MemoryPressureLevel::High => {
                let _ = self.apply_pressure_relief_strategies();
            }
            _ => {}
        }

        // Attempt optimal allocation routing
        let allocation_result = match allocation_type {
            AllocationType::Device => {
                self.allocate_device_memory(size, device_id, alignment, priority)
            }
            AllocationType::Unified => {
                self.allocate_unified_memory(size, device_id, alignment, priority)
            }
            AllocationType::Pinned => self.allocate_pinned_memory(size, alignment, priority),
        };

        let duration = start_time.elapsed();

        // Record allocation result and update statistics
        self.record_allocation_result(&allocation_result, duration, size, allocation_type);

        // Update system state
        if let Ok(ref allocation) = allocation_result {
            self.update_system_state_on_allocation(allocation.size(), allocation_type, device_id);
        }

        match allocation_result {
            Ok(allocation) => ManagerOperationResult::Success(allocation),
            Err(error) => {
                // Attempt fallback strategies
                match self.attempt_fallback_allocation(size, allocation_type, device_id) {
                    Ok(allocation) => ManagerOperationResult::PartialSuccess(
                        allocation,
                        vec![format!(
                            "Primary allocation failed: {}. Used fallback.",
                            error
                        )],
                    ),
                    Err(fallback_error) => ManagerOperationResult::Failure(format!(
                        "Primary allocation failed: {}. Fallback also failed: {}",
                        error, fallback_error
                    )),
                }
            }
        }
    }

    /// Deallocate memory with cleanup optimization
    pub fn deallocate_memory(
        &self,
        allocation: Box<dyn CudaMemoryAllocation>,
    ) -> ManagerOperationResult<()> {
        let size = allocation.size();
        let allocation_type = allocation.allocation_type();

        let result = match allocation_type {
            AllocationType::Device => {
                if let Some(device_id) = allocation.device_id() {
                    self.deallocate_device_memory(allocation, device_id)
                } else {
                    Err("Device allocation missing device ID".to_string())
                }
            }
            AllocationType::Unified => self.unified_manager.deallocate(allocation),
            AllocationType::Pinned => self.pinned_manager.deallocate(allocation),
        };

        // Update system state
        if result.is_ok() {
            self.update_system_state_on_deallocation(size, allocation_type);
        }

        // Trigger optimization if beneficial
        if self.should_trigger_optimization() {
            let _ = self.trigger_background_optimization();
        }

        match result {
            Ok(_) => ManagerOperationResult::Success(()),
            Err(error) => ManagerOperationResult::Failure(error),
        }
    }

    /// Get comprehensive memory statistics
    pub fn get_memory_statistics(&self) -> Result<MemoryUsageStatistics, String> {
        self.statistics_manager.get_comprehensive_statistics()
    }

    /// Get system performance metrics
    pub fn get_performance_metrics(&self) -> Result<PerformanceMetrics, String> {
        self.statistics_manager.get_performance_metrics()
    }

    /// Trigger manual optimization
    pub fn optimize_memory_layout(&self) -> ManagerOperationResult<OptimizationResult> {
        match self.optimization_engine.optimize_global_layout() {
            Ok(result) => {
                self.apply_optimization_result(&result);
                ManagerOperationResult::Success(result)
            }
            Err(error) => ManagerOperationResult::Failure(error),
        }
    }

    /// Enable predictive allocation for better performance
    pub fn enable_predictive_allocation(&self, enable: bool) -> Result<(), String> {
        if enable {
            self.start_prediction_engine()
        } else {
            self.stop_prediction_engine()
        }
    }

    /// Get system health status
    pub fn get_system_health(&self) -> Result<SystemHealthStatus, String> {
        let state = self
            .system_state
            .lock()
            .map_err(|e| format!("Failed to acquire system state lock: {}", e))?;
        Ok(state.health_status.clone())
    }

    /// Perform system maintenance
    pub fn perform_maintenance(&self) -> ManagerOperationResult<Vec<String>> {
        let mut maintenance_results = Vec::new();

        // Pool compaction
        if let Err(e) = self.compact_memory_pools() {
            maintenance_results.push(format!("Pool compaction failed: {}", e));
        } else {
            maintenance_results.push("Pool compaction completed successfully".to_string());
        }

        // Statistics cleanup
        if let Err(e) = self.cleanup_old_statistics() {
            maintenance_results.push(format!("Statistics cleanup failed: {}", e));
        } else {
            maintenance_results.push("Statistics cleanup completed successfully".to_string());
        }

        // Optimization model updates
        if let Err(e) = self.update_optimization_models() {
            maintenance_results.push(format!("Optimization model update failed: {}", e));
        } else {
            maintenance_results.push("Optimization models updated successfully".to_string());
        }

        // Update maintenance timestamp
        if let Ok(mut state) = self.system_state.lock() {
            state.last_maintenance = Instant::now();
        }

        if maintenance_results.iter().any(|r| r.contains("failed")) {
            ManagerOperationResult::PartialSuccess(
                maintenance_results.clone(),
                maintenance_results
                    .into_iter()
                    .filter(|r| r.contains("failed"))
                    .collect(),
            )
        } else {
            ManagerOperationResult::Success(maintenance_results)
        }
    }

    // Private implementation methods

    fn allocate_device_memory(
        &self,
        size: usize,
        device_id: Option<usize>,
        alignment: Option<MemoryAlignment>,
        priority: AllocationPriority,
    ) -> Result<Box<dyn CudaMemoryAllocation>, String> {
        let device_id = device_id.unwrap_or(self.select_optimal_device(size)?);

        let device_managers = self
            .device_managers
            .read()
            .map_err(|e| format!("Failed to acquire device managers lock: {}", e))?;

        let manager = device_managers
            .get(&device_id)
            .ok_or_else(|| format!("No device manager for device {}", device_id))?;

        manager.allocate(
            size,
            alignment.unwrap_or(MemoryAlignment::Default),
            priority,
        )
    }

    fn allocate_unified_memory(
        &self,
        size: usize,
        device_id: Option<usize>,
        alignment: Option<MemoryAlignment>,
        priority: AllocationPriority,
    ) -> Result<Box<dyn CudaMemoryAllocation>, String> {
        self.unified_manager.allocate(
            size,
            device_id,
            alignment.unwrap_or(MemoryAlignment::Default),
            priority,
        )
    }

    fn allocate_pinned_memory(
        &self,
        size: usize,
        alignment: Option<MemoryAlignment>,
        priority: AllocationPriority,
    ) -> Result<Box<dyn CudaMemoryAllocation>, String> {
        self.pinned_manager.allocate(
            size,
            alignment.unwrap_or(MemoryAlignment::Default),
            priority,
        )
    }

    fn deallocate_device_memory(
        &self,
        allocation: Box<dyn CudaMemoryAllocation>,
        device_id: usize,
    ) -> Result<(), String> {
        let device_managers = self
            .device_managers
            .read()
            .map_err(|e| format!("Failed to acquire device managers lock: {}", e))?;

        let manager = device_managers
            .get(&device_id)
            .ok_or_else(|| format!("No device manager for device {}", device_id))?;

        manager.deallocate(allocation)
    }

    fn check_memory_pressure(&self) -> MemoryPressureLevel {
        if let Ok(state) = self.system_state.lock() {
            state.memory_pressure_level.clone()
        } else {
            MemoryPressureLevel::Normal
        }
    }

    fn select_optimal_device(&self, size: usize) -> Result<usize, String> {
        // Use optimization engine to select the best device
        match self
            .optimization_engine
            .recommend_device_for_allocation(size)
        {
            Ok(device_id) => Ok(device_id),
            Err(_) => {
                // Fallback to device with most free memory
                let device_managers = self
                    .device_managers
                    .read()
                    .map_err(|e| format!("Failed to acquire device managers lock: {}", e))?;

                if device_managers.is_empty() {
                    return Err("No devices available".to_string());
                }

                let mut best_device = *device_managers.keys().next().expect("device_managers should not be empty after is_empty check");
                let mut max_free = 0;

                for (&device_id, manager) in device_managers.iter() {
                    if let Ok(metrics) = manager.get_metrics() {
                        if metrics.free_memory > max_free {
                            max_free = metrics.free_memory;
                            best_device = device_id;
                        }
                    }
                }

                Ok(best_device)
            }
        }
    }

    fn initialize_background_tasks(&self) -> Result<(), String> {
        let mut tasks = self
            .background_tasks
            .lock()
            .map_err(|e| format!("Failed to acquire background tasks lock: {}", e))?;

        // Statistics collection task
        let stats_manager = Arc::clone(&self.statistics_manager);
        let stats_interval = self.config.stats_collection_interval;
        let stats_task = thread::spawn(move || loop {
            thread::sleep(stats_interval);
            let _ = stats_manager.collect_statistics();
        });
        tasks.push(stats_task);

        // Optimization task
        let opt_engine = Arc::clone(&self.optimization_engine);
        let opt_task = thread::spawn(move || loop {
            thread::sleep(Duration::from_secs(10));
            let _ = opt_engine.run_background_optimization();
        });
        tasks.push(opt_task);

        // Memory pressure monitoring task
        let system_state = Arc::clone(&self.system_state);
        let pressure_thresholds = self.config.pressure_thresholds.clone();
        let pressure_task = thread::spawn(move || {
            loop {
                thread::sleep(Duration::from_millis(500));
                // Monitor memory pressure and update system state
                // Implementation would check actual memory usage
            }
        });
        tasks.push(pressure_task);

        Ok(())
    }

    fn setup_emergency_protocols(&self) -> Result<(), String> {
        // Implementation would set up emergency protocols
        Ok(())
    }

    fn record_allocation_attempt(
        &self,
        size: usize,
        allocation_type: AllocationType,
        device_id: Option<usize>,
    ) {
        // Implementation would record allocation attempts for analytics
    }

    fn record_allocation_result(
        &self,
        result: &Result<Box<dyn CudaMemoryAllocation>, String>,
        duration: Duration,
        size: usize,
        allocation_type: AllocationType,
    ) {
        // Implementation would record allocation results for learning
    }

    fn update_system_state_on_allocation(
        &self,
        size: usize,
        allocation_type: AllocationType,
        device_id: Option<usize>,
    ) {
        if let Ok(mut state) = self.system_state.lock() {
            state.total_allocated += size;
            if let Some(device_id) = device_id {
                *state.device_allocations.entry(device_id).or_insert(0) += size;
            }
        }
    }

    fn update_system_state_on_deallocation(&self, size: usize, allocation_type: AllocationType) {
        if let Ok(mut state) = self.system_state.lock() {
            state.total_allocated = state.total_allocated.saturating_sub(size);
        }
    }

    fn attempt_fallback_allocation(
        &self,
        size: usize,
        allocation_type: AllocationType,
        device_id: Option<usize>,
    ) -> Result<Box<dyn CudaMemoryAllocation>, String> {
        // Implementation would attempt fallback allocation strategies
        Err("No fallback strategies available".to_string())
    }

    fn execute_emergency_protocols(&self) -> Result<(), String> {
        // Implementation would execute emergency memory release protocols
        Ok(())
    }

    fn apply_pressure_relief_strategies(&self) -> Result<(), String> {
        // Implementation would apply memory pressure relief strategies
        Ok(())
    }

    fn should_trigger_optimization(&self) -> bool {
        // Implementation would determine if optimization should be triggered
        false
    }

    fn trigger_background_optimization(&self) -> Result<(), String> {
        // Implementation would trigger background optimization
        Ok(())
    }

    fn apply_optimization_result(&self, result: &OptimizationResult) {
        // Implementation would apply optimization results
    }

    fn start_prediction_engine(&self) -> Result<(), String> {
        // Implementation would start the prediction engine
        Ok(())
    }

    fn stop_prediction_engine(&self) -> Result<(), String> {
        // Implementation would stop the prediction engine
        Ok(())
    }

    fn compact_memory_pools(&self) -> Result<(), String> {
        // Implementation would compact memory pools
        Ok(())
    }

    fn cleanup_old_statistics(&self) -> Result<(), String> {
        // Implementation would cleanup old statistics
        Ok(())
    }

    fn update_optimization_models(&self) -> Result<(), String> {
        // Implementation would update optimization models
        Ok(())
    }
}

impl SystemState {
    fn new() -> Self {
        Self {
            total_allocated: 0,
            device_allocations: HashMap::new(),
            memory_pressure_level: MemoryPressureLevel::Normal,
            active_optimizations: Vec::new(),
            health_status: SystemHealthStatus::Healthy,
            last_maintenance: Instant::now(),
        }
    }
}

impl OperationHistory {
    fn new() -> Self {
        Self {
            allocation_patterns: Vec::new(),
            performance_history: Vec::new(),
            optimization_effectiveness: HashMap::new(),
            error_patterns: Vec::new(),
        }
    }
}

impl EmergencyProtocols {
    fn new() -> Self {
        Self {
            release_strategies: Vec::new(),
            fallback_methods: Vec::new(),
            recovery_procedures: Vec::new(),
        }
    }
}

impl AllocationPredictor {
    fn new() -> Self {
        Self {
            historical_patterns: Vec::new(),
            prediction_models: HashMap::new(),
            confidence_thresholds: HashMap::new(),
        }
    }
}

/// Global memory manager instance
pub static mut GLOBAL_MEMORY_MANAGER: Option<CudaMemoryManagerCoordinator> = None;

/// Initialize the global memory manager
pub fn initialize_global_manager(config: CudaMemoryManagerConfig) -> Result<(), String> {
    unsafe {
        if GLOBAL_MEMORY_MANAGER.is_some() {
            return Err("Global memory manager already initialized".to_string());
        }

        let manager = CudaMemoryManagerCoordinator::new(config)?;
        GLOBAL_MEMORY_MANAGER = Some(manager);
    }

    Ok(())
}

/// Get reference to the global memory manager
pub fn get_global_manager() -> Result<&'static CudaMemoryManagerCoordinator, String> {
    unsafe {
        GLOBAL_MEMORY_MANAGER
            .as_ref()
            .ok_or_else(|| "Global memory manager not initialized".to_string())
    }
}