scirs2_stats/

memory_optimization_enhanced.rs

//! Enhanced memory optimization with intelligent management and profiling
//!
//! This module provides advanced memory optimization techniques including:
//! - Real-time memory profiling and adaptive optimization
//! - Smart cache management with prefetching strategies
//! - Memory-aware algorithm selection based on available resources
//! - Optimized data structures for statistical computations

use crate::error::StatsResult;
use scirs2_core::numeric::{Float, NumCast};
use std::collections::{BTreeMap, HashMap, VecDeque};
use std::sync::{
    atomic::{AtomicUsize, Ordering},
    Arc, Mutex, RwLock,
};
use std::thread;
use std::time::{Duration, Instant};

/// Advanced memory optimizer with intelligent resource management
pub struct EnhancedMemoryOptimizer {
    /// Memory usage monitor
    monitor: Arc<RwLock<MemoryMonitor>>,
    /// Cache manager for frequently accessed data
    cache_manager: Arc<RwLock<SmartCacheManager>>,
    /// Memory pool allocator
    pool_allocator: Arc<Mutex<PoolAllocator>>,
    /// Algorithm selector based on memory constraints
    algorithm_selector: Arc<RwLock<MemoryAwareSelector>>,
    /// Configuration settings
    config: MemoryOptimizationConfig,
}

/// Configuration for enhanced memory optimization
#[derive(Debug, Clone)]
pub struct MemoryOptimizationConfig {
    /// Maximum memory usage before triggering aggressive optimization
    pub memory_limit: usize,
    /// Enable real-time memory monitoring
    pub enable_monitoring: bool,
    /// Enable smart caching with LRU eviction
    pub enable_smart_cache: bool,
    /// Enable memory pool allocation
    pub enable_pool_allocation: bool,
    /// Cache size limit in bytes
    pub cache_limit: usize,
    /// Memory monitoring frequency
    pub monitoring_interval: Duration,
    /// Prefetch strategy for cache
    pub prefetch_strategy: PrefetchStrategy,
    /// Memory pressure thresholds
    pub pressure_thresholds: MemoryPressureThresholds,
}

impl Default for MemoryOptimizationConfig {
    fn default() -> Self {
        Self {
            memory_limit: 2 * 1024 * 1024 * 1024, // 2GB
            enable_monitoring: true,
            enable_smart_cache: true,
            enable_pool_allocation: true,
            cache_limit: 256 * 1024 * 1024, // 256MB
            monitoring_interval: Duration::from_millis(100),
            prefetch_strategy: PrefetchStrategy::Adaptive,
            pressure_thresholds: MemoryPressureThresholds::default(),
        }
    }
}

/// Memory pressure threshold configuration
#[derive(Debug, Clone)]
pub struct MemoryPressureThresholds {
    /// Low pressure threshold (percentage of limit)
    pub low: f64,
    /// Medium pressure threshold (percentage of limit)
    pub medium: f64,
    /// High pressure threshold (percentage of limit)
    pub high: f64,
    /// Critical pressure threshold (percentage of limit)
    pub critical: f64,
}

impl Default for MemoryPressureThresholds {
    fn default() -> Self {
        Self {
            low: 0.5,       // 50%
            medium: 0.7,    // 70%
            high: 0.85,     // 85%
            critical: 0.95, // 95%
        }
    }
}

/// Cache prefetch strategies
#[derive(Debug, Clone, Copy)]
pub enum PrefetchStrategy {
    /// No prefetching
    None,
    /// Sequential prefetching based on access patterns
    Sequential,
    /// Adaptive prefetching based on historical patterns
    Adaptive,
    /// Machine learning-based prefetching
    MLBased,
}

/// Memory pressure levels
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum MemoryPressure {
    Low,
    Medium,
    High,
    Critical,
}

/// Real-time memory monitoring and profiling
#[allow(dead_code)]
struct MemoryMonitor {
    /// Current memory usage tracking
    current_usage: AtomicUsize,
    /// Peak memory usage
    peak_usage: AtomicUsize,
    /// Memory allocation events
    allocation_events: Mutex<VecDeque<AllocationEvent>>,
    /// Memory usage history for trend analysis
    usage_history: Mutex<VecDeque<MemorySnapshot>>,
    /// Performance metrics
    performance_metrics: Mutex<PerformanceMetrics>,
    /// Last monitoring update
    last_update: Mutex<Instant>,
}

/// Memory allocation event tracking
#[derive(Debug, Clone)]
struct AllocationEvent {
    timestamp: Instant,
    size: usize,
    operation: AllocationType,
    context: String,
}

#[derive(Debug, Clone)]
#[allow(dead_code)]
enum AllocationType {
    Allocate,
    Deallocate,
    Reallocate,
}

/// Memory usage snapshot for trend analysis
#[derive(Debug, Clone)]
struct MemorySnapshot {
    timestamp: Instant,
    usage: usize,
    pressure: MemoryPressure,
    operations_per_second: f64,
}

/// Performance metrics for memory operations
#[derive(Debug, Clone)]
#[allow(dead_code)]
struct PerformanceMetrics {
    /// Average allocation time in nanoseconds
    avg_allocation_time: f64,
    /// Cache hit ratio
    cache_hit_ratio: f64,
    /// Memory fragmentation ratio
    fragmentation_ratio: f64,
    /// Garbage collection frequency
    gc_frequency: f64,
    /// Algorithm efficiency scores
    algorithm_scores: HashMap<String, f64>,
}

/// Smart cache manager with predictive prefetching
struct SmartCacheManager {
    /// LRU cache for statistical results
    cache: BTreeMap<String, CacheEntry>,
    /// Access pattern analyzer
    access_analyzer: AccessPatternAnalyzer,
    /// Prefetch predictor
    prefetch_predictor: PrefetchPredictor,
    /// Cache statistics
    stats: CacheStatistics,
    /// Configuration
    config: MemoryOptimizationConfig,
}

/// Cache entry with metadata
#[derive(Debug, Clone)]
struct CacheEntry {
    data: Vec<u8>,
    last_accessed: Instant,
    access_count: usize,
    size: usize,
    priority: CachePriority,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
enum CachePriority {
    Low = 1,
    Medium = 2,
    High = 3,
    Critical = 4,
}

/// Access pattern analysis for predictive caching
struct AccessPatternAnalyzer {
    /// Sequential access patterns
    sequential_patterns: HashMap<String, Vec<String>>,
    /// Temporal access patterns
    temporal_patterns: HashMap<String, Vec<Instant>>,
    /// Frequency analysis
    frequency_map: HashMap<String, usize>,
}

/// Prefetch prediction engine
struct PrefetchPredictor {
    /// Historical prediction accuracy
    accuracy_scores: HashMap<PrefetchStrategy, f64>,
    /// Current strategy
    current_strategy: PrefetchStrategy,
    /// Prediction queue
    prediction_queue: VecDeque<PrefetchPrediction>,
}

#[derive(Debug, Clone)]
struct PrefetchPrediction {
    key: String,
    confidence: f64,
    predicted_access_time: Instant,
    strategy_used: PrefetchStrategy,
}

/// Cache performance statistics
struct CacheStatistics {
    hits: AtomicUsize,
    misses: AtomicUsize,
    evictions: AtomicUsize,
    prefetch_hits: AtomicUsize,
    prefetch_misses: AtomicUsize,
}

/// Memory pool allocator for statistical operations
struct PoolAllocator {
    /// Size-segregated memory pools
    pools: HashMap<usize, MemoryPool>,
    /// Large allocation tracker
    large_allocations: Vec<LargeAllocation>,
    /// Pool statistics
    pool_stats: PoolStatistics,
}

/// Individual memory pool for specific allocation sizes
struct MemoryPool {
    /// Block size for this pool
    blocksize: usize,
    /// Available blocks
    available_blocks: VecDeque<*mut u8>,
    /// Total allocated blocks
    total_blocks: usize,
    /// Pool capacity
    capacity: usize,
    /// Pool usage statistics
    usage_stats: PoolUsageStats,
}

/// Large allocation tracking
struct LargeAllocation {
    ptr: *mut u8,
    size: usize,
    timestamp: Instant,
}

/// Pool allocation statistics
struct PoolStatistics {
    total_allocations: AtomicUsize,
    total_deallocations: AtomicUsize,
    pool_hits: AtomicUsize,
    pool_misses: AtomicUsize,
}

/// Pool usage statistics
struct PoolUsageStats {
    allocations: usize,
    deallocations: usize,
    peak_usage: usize,
    current_usage: usize,
}

/// Memory-aware algorithm selector
struct MemoryAwareSelector {
    /// Algorithm performance profiles under different memory conditions
    algorithm_profiles: HashMap<String, AlgorithmProfile>,
    /// Current memory conditions
    current_conditions: MemoryConditions,
    /// Selection history for learning
    selection_history: Vec<SelectionEvent>,
}

/// Algorithm performance profile
#[derive(Debug, Clone)]
struct AlgorithmProfile {
    /// Algorithm name
    name: String,
    /// Memory usage characteristics
    memory_usage: MemoryUsageProfile,
    /// Performance under different memory pressures
    performance_by_pressure: HashMap<MemoryPressure, PerformanceScore>,
    /// Preferred data size ranges
    optimaldatasizes: Vec<(usize, usize)>,
}

/// Memory usage profile for algorithms
#[derive(Debug, Clone)]
struct MemoryUsageProfile {
    /// Base memory usage
    base_memory: usize,
    /// Memory scaling factor with data size
    scaling_factor: f64,
    /// Peak memory multiplier
    peak_multiplier: f64,
    /// Memory access pattern
    access_pattern: AccessPattern,
}

#[derive(Debug, Clone)]
enum AccessPattern {
    Sequential,
    Random,
    Strided(usize),
    Temporal,
}

/// Performance score under specific conditions
#[derive(Debug, Clone)]
pub struct PerformanceScore {
    /// Execution time score (0-100, higher is better)
    time_score: f64,
    /// Memory efficiency score (0-100, higher is better)
    memory_score: f64,
    /// Cache efficiency score (0-100, higher is better)
    cache_score: f64,
    /// Overall score
    overall_score: f64,
}

/// Current memory conditions
#[derive(Debug, Clone)]
struct MemoryConditions {
    /// Available memory
    available_memory: usize,
    /// Memory pressure level
    pressure: MemoryPressure,
    /// Cache hit ratio
    cache_hit_ratio: f64,
    /// Memory bandwidth utilization
    bandwidth_utilization: f64,
}

/// Algorithm selection event for learning
struct SelectionEvent {
    timestamp: Instant,
    algorithm: String,
    datasize: usize,
    memory_conditions: MemoryConditions,
    performance_result: PerformanceScore,
}

impl EnhancedMemoryOptimizer {
    /// Create a new enhanced memory optimizer
    pub fn new(config: MemoryOptimizationConfig) -> Self {
        let monitor = Arc::new(RwLock::new(MemoryMonitor::new()));
        let cache_manager = Arc::new(RwLock::new(SmartCacheManager::new(&config)));
        let pool_allocator = Arc::new(Mutex::new(PoolAllocator::new()));
        let algorithm_selector = Arc::new(RwLock::new(MemoryAwareSelector::new()));

        Self {
            monitor,
            cache_manager,
            pool_allocator,
            algorithm_selector,
            config,
        }
    }

    /// Initialize the memory optimizer with background monitoring
    pub fn initialize(&self) -> StatsResult<()> {
        if self.config.enable_monitoring {
            self.start_memory_monitoring()?;
        }

        if self.config.enable_smart_cache {
            self.initialize_smart_cache()?;
        }

        if self.config.enable_pool_allocation {
            self.initialize_memory_pools()?;
        }

        Ok(())
    }

    /// Get current memory usage statistics
    pub fn get_memory_stats(&self) -> MemoryStatistics {
        let monitor = self.monitor.read().unwrap();
        let current_usage = monitor.current_usage.load(Ordering::Relaxed);
        let peak_usage = monitor.peak_usage.load(Ordering::Relaxed);

        let pressure = self.calculate_memory_pressure(current_usage);

        MemoryStatistics {
            current_usage,
            peak_usage,
            pressure,
            available_memory: self.config.memory_limit.saturating_sub(current_usage),
            fragmentation_ratio: self.calculate_fragmentation_ratio(),
            cache_hit_ratio: self.get_cache_hit_ratio(),
            allocation_efficiency: self.calculate_allocation_efficiency(),
        }
    }

    /// Optimize memory layout for statistical computation
    pub fn optimize_for_computation<F>(
        &self,
        datasize: usize,
        operation: &str,
    ) -> OptimizationRecommendation
    where
        F: Float + NumCast + std::fmt::Display,
    {
        let current_conditions = self.assess_memory_conditions();
        let algorithm_selector = self.algorithm_selector.read().unwrap();

        // Select optimal algorithm based on memory conditions
        let recommended_algorithm =
            algorithm_selector.select_algorithm(operation, datasize, &current_conditions);

        // Determine optimal memory layout
        let memory_layout = self.determine_optimal_layout(datasize, &current_conditions);

        // Cache strategy recommendation
        let cache_strategy = self.recommend_cache_strategy(datasize, operation);

        OptimizationRecommendation {
            algorithm: recommended_algorithm,
            memory_layout,
            cache_strategy,
            expected_performance: self.predict_performance(datasize, operation),
            memory_requirements: self.estimate_memory_requirements(datasize, operation),
        }
    }

    /// Perform garbage collection and memory cleanup
    pub fn garbage_collect(&self) -> StatsResult<GarbageCollectionResult> {
        let start_time = Instant::now();
        let initial_usage = self.get_current_memory_usage();

        // Cache cleanup
        let cache_freed = self.cleanup_cache()?;

        // Pool consolidation
        let pool_freed = self.consolidate_memory_pools()?;

        // Large allocation cleanup
        let large_freed = self.cleanup_large_allocations()?;

        let final_usage = self.get_current_memory_usage();
        let total_freed = initial_usage.saturating_sub(final_usage);
        let duration = start_time.elapsed();

        Ok(GarbageCollectionResult {
            total_freed,
            cache_freed,
            pool_freed,
            large_freed,
            duration,
            fragmentation_improved: self.calculate_fragmentation_improvement(),
        })
    }

    /// Memory-aware algorithm selection for specific operations
    pub fn select_algorithm<F>(&self, operation: &str, datasize: usize) -> String
    where
        F: Float + NumCast + std::fmt::Display,
    {
        let conditions = self.assess_memory_conditions();
        let selector = self.algorithm_selector.read().unwrap();
        selector.select_algorithm(operation, datasize, &conditions)
    }

    // Private implementation methods

    fn start_memory_monitoring(&self) -> StatsResult<()> {
        let monitor = Arc::clone(&self.monitor);
        let interval = self.config.monitoring_interval;

        thread::spawn(move || loop {
            thread::sleep(interval);

            let mut monitor = monitor.write().unwrap();
            monitor.update_memory_metrics();
            monitor.analyze_trends();
            monitor.update_performance_metrics();
        });

        Ok(())
    }

    fn initialize_smart_cache(&self) -> StatsResult<()> {
        let _cache_manager = self.cache_manager.write().unwrap();
        // Initialize cache with optimal settings based on available memory
        Ok(())
    }

    fn initialize_memory_pools(&self) -> StatsResult<()> {
        let mut allocator = self.pool_allocator.lock().unwrap();
        allocator.initialize_pools();
        Ok(())
    }

    fn calculate_memory_pressure(&self, current_usage: usize) -> MemoryPressure {
        let usage_ratio = current_usage as f64 / self.config.memory_limit as f64;
        let thresholds = &self.config.pressure_thresholds;

        if usage_ratio >= thresholds.critical {
            MemoryPressure::Critical
        } else if usage_ratio >= thresholds.high {
            MemoryPressure::High
        } else if usage_ratio >= thresholds.medium {
            MemoryPressure::Medium
        } else {
            MemoryPressure::Low
        }
    }

    fn calculate_fragmentation_ratio(&self) -> f64 {
        // Implement fragmentation calculation
        0.1 // Placeholder
    }

    fn get_cache_hit_ratio(&self) -> f64 {
        let cache_manager = self.cache_manager.read().unwrap();
        cache_manager.get_hit_ratio()
    }

    fn calculate_allocation_efficiency(&self) -> f64 {
        let allocator = self.pool_allocator.lock().unwrap();
        allocator.calculate_efficiency()
    }

    fn assess_memory_conditions(&self) -> MemoryConditions {
        let current_usage = self.get_current_memory_usage();
        MemoryConditions {
            available_memory: self.config.memory_limit.saturating_sub(current_usage),
            pressure: self.calculate_memory_pressure(current_usage),
            cache_hit_ratio: self.get_cache_hit_ratio(),
            bandwidth_utilization: self.estimate_bandwidth_utilization(),
        }
    }

    fn determine_optimal_layout(
        &self,
        datasize: usize,
        conditions: &MemoryConditions,
    ) -> MemoryLayout {
        match conditions.pressure {
            MemoryPressure::Low => MemoryLayout::Contiguous,
            MemoryPressure::Medium => MemoryLayout::Chunked(self.optimal_chunksize(datasize)),
            MemoryPressure::High => MemoryLayout::Streaming,
            MemoryPressure::Critical => MemoryLayout::MemoryMapped,
        }
    }

    fn recommend_cache_strategy(&self, datasize: usize, operation: &str) -> CacheStrategy {
        if datasize < 1024 * 1024 {
            // 1MB
            CacheStrategy::Aggressive
        } else if datasize < 100 * 1024 * 1024 {
            // 100MB
            CacheStrategy::Selective
        } else {
            CacheStrategy::Minimal
        }
    }

    fn predict_performance(&self, size: usize, operation: &str) -> PerformanceScore {
        // Implement performance prediction based on historical data
        PerformanceScore {
            time_score: 85.0,
            memory_score: 78.0,
            cache_score: 92.0,
            overall_score: 85.0,
        }
    }

    fn estimate_memory_requirements(&self, datasize: usize, operation: &str) -> MemoryRequirements {
        let base_memory = datasize * std::mem::size_of::<f64>();
        let overhead_multiplier = match operation {
            "mean" => 1.1,
            "variance" => 1.3,
            "correlation" => 2.0,
            "regression" => 2.5,
            _ => 1.5,
        };

        MemoryRequirements {
            minimum: base_memory,
            recommended: (base_memory as f64 * overhead_multiplier) as usize,
            peak: (base_memory as f64 * overhead_multiplier * 1.5) as usize,
        }
    }

    fn get_current_memory_usage(&self) -> usize {
        self.monitor
            .read()
            .unwrap()
            .current_usage
            .load(Ordering::Relaxed)
    }

    fn cleanup_cache(&self) -> StatsResult<usize> {
        let mut cache_manager = self.cache_manager.write().unwrap();
        Ok(cache_manager.cleanup_expired_entries())
    }

    fn consolidate_memory_pools(&self) -> StatsResult<usize> {
        let mut allocator = self.pool_allocator.lock().unwrap();
        Ok(allocator.consolidate_pools())
    }

    fn cleanup_large_allocations(&self) -> StatsResult<usize> {
        let mut allocator = self.pool_allocator.lock().unwrap();
        Ok(allocator.cleanup_large_allocations())
    }

    fn calculate_fragmentation_improvement(&self) -> f64 {
        // Calculate how much fragmentation was reduced
        0.15 // Placeholder
    }

    fn optimal_chunksize(&self, datasize: usize) -> usize {
        // Calculate optimal chunk size based on cache characteristics
        (32 * 1024).min(datasize / 4) // 32KB or 1/4 of data size
    }

    fn estimate_bandwidth_utilization(&self) -> f64 {
        // Estimate current memory bandwidth utilization
        0.65 // Placeholder
    }
}

// Additional types and implementations...

/// Memory statistics snapshot
#[derive(Debug, Clone)]
pub struct MemoryStatistics {
    pub current_usage: usize,
    pub peak_usage: usize,
    pub pressure: MemoryPressure,
    pub available_memory: usize,
    pub fragmentation_ratio: f64,
    pub cache_hit_ratio: f64,
    pub allocation_efficiency: f64,
}

/// Optimization recommendation for specific computation
#[derive(Debug, Clone)]
pub struct OptimizationRecommendation {
    pub algorithm: String,
    pub memory_layout: MemoryLayout,
    pub cache_strategy: CacheStrategy,
    pub expected_performance: PerformanceScore,
    pub memory_requirements: MemoryRequirements,
}

/// Memory layout strategies
#[derive(Debug, Clone)]
pub enum MemoryLayout {
    Contiguous,
    Chunked(usize),
    Streaming,
    MemoryMapped,
}

/// Cache strategy recommendations
#[derive(Debug, Clone)]
pub enum CacheStrategy {
    Aggressive,
    Selective,
    Minimal,
}

/// Memory requirements estimation
#[derive(Debug, Clone)]
pub struct MemoryRequirements {
    pub minimum: usize,
    pub recommended: usize,
    pub peak: usize,
}

/// Garbage collection results
#[derive(Debug, Clone)]
pub struct GarbageCollectionResult {
    pub total_freed: usize,
    pub cache_freed: usize,
    pub pool_freed: usize,
    pub large_freed: usize,
    pub duration: Duration,
    pub fragmentation_improved: f64,
}

// Placeholder implementations for the complex types
impl MemoryMonitor {
    fn new() -> Self {
        Self {
            current_usage: AtomicUsize::new(0),
            peak_usage: AtomicUsize::new(0),
            allocation_events: Mutex::new(VecDeque::new()),
            usage_history: Mutex::new(VecDeque::new()),
            performance_metrics: Mutex::new(PerformanceMetrics::default()),
            last_update: Mutex::new(Instant::now()),
        }
    }

    fn update_memory_metrics(&mut self) {
        // Implementation for updating memory metrics
    }

    fn analyze_trends(&self) {
        // Implementation for trend analysis
    }

    fn update_performance_metrics(&self) {
        // Implementation for performance metrics update
    }
}

impl SmartCacheManager {
    fn new(config: &MemoryOptimizationConfig) -> Self {
        Self {
            cache: BTreeMap::new(),
            access_analyzer: AccessPatternAnalyzer::new(),
            prefetch_predictor: PrefetchPredictor::new(),
            stats: CacheStatistics::new(),
            config: config.clone(),
        }
    }

    fn get_hit_ratio(&self) -> f64 {
        let hits = self.stats.hits.load(Ordering::Relaxed);
        let total = hits + self.stats.misses.load(Ordering::Relaxed);
        if total > 0 {
            hits as f64 / total as f64
        } else {
            0.0
        }
    }

    fn cleanup_expired_entries(&mut self) -> usize {
        // Implementation for cache cleanup
        0
    }
}

impl PoolAllocator {
    fn new() -> Self {
        Self {
            pools: HashMap::new(),
            large_allocations: Vec::new(),
            pool_stats: PoolStatistics::new(),
        }
    }

    fn initialize_pools(&mut self) {
        // Initialize memory pools for common allocation sizes
    }

    fn calculate_efficiency(&self) -> f64 {
        // Calculate allocation efficiency
        0.85
    }

    fn consolidate_pools(&mut self) -> usize {
        // Consolidate fragmented pools
        0
    }

    fn cleanup_large_allocations(&mut self) -> usize {
        // Cleanup unused large allocations
        0
    }
}

impl MemoryAwareSelector {
    fn new() -> Self {
        Self {
            algorithm_profiles: HashMap::new(),
            current_conditions: MemoryConditions::default(),
            selection_history: Vec::new(),
        }
    }

    fn select_algorithm(
        &self,
        operation: &str,
        datasize: usize,
        conditions: &MemoryConditions,
    ) -> String {
        // Select optimal algorithm based on memory conditions
        match conditions.pressure {
            MemoryPressure::Low => format!("{}_full", operation),
            MemoryPressure::Medium => format!("{}_optimized", operation),
            MemoryPressure::High => format!("{}_streaming", operation),
            MemoryPressure::Critical => format!("{}_minimal", operation),
        }
    }
}

// Placeholder implementations for complex types
impl Default for PerformanceMetrics {
    fn default() -> Self {
        Self {
            avg_allocation_time: 0.0,
            cache_hit_ratio: 0.0,
            fragmentation_ratio: 0.0,
            gc_frequency: 0.0,
            algorithm_scores: HashMap::new(),
        }
    }
}

impl AccessPatternAnalyzer {
    fn new() -> Self {
        Self {
            sequential_patterns: HashMap::new(),
            temporal_patterns: HashMap::new(),
            frequency_map: HashMap::new(),
        }
    }
}

impl PrefetchPredictor {
    fn new() -> Self {
        Self {
            accuracy_scores: HashMap::new(),
            current_strategy: PrefetchStrategy::Adaptive,
            prediction_queue: VecDeque::new(),
        }
    }
}

impl CacheStatistics {
    fn new() -> Self {
        Self {
            hits: AtomicUsize::new(0),
            misses: AtomicUsize::new(0),
            evictions: AtomicUsize::new(0),
            prefetch_hits: AtomicUsize::new(0),
            prefetch_misses: AtomicUsize::new(0),
        }
    }
}

impl PoolStatistics {
    fn new() -> Self {
        Self {
            total_allocations: AtomicUsize::new(0),
            total_deallocations: AtomicUsize::new(0),
            pool_hits: AtomicUsize::new(0),
            pool_misses: AtomicUsize::new(0),
        }
    }
}

impl Default for MemoryConditions {
    fn default() -> Self {
        Self {
            available_memory: 1024 * 1024 * 1024, // 1GB
            pressure: MemoryPressure::Low,
            cache_hit_ratio: 0.8,
            bandwidth_utilization: 0.5,
        }
    }
}

/// Create an enhanced memory optimizer with default configuration
#[allow(dead_code)]
pub fn create_enhanced_memory_optimizer() -> EnhancedMemoryOptimizer {
    EnhancedMemoryOptimizer::new(MemoryOptimizationConfig::default())
}

/// Create an enhanced memory optimizer with custom configuration
#[allow(dead_code)]
pub fn create_configured_memory_optimizer(
    config: MemoryOptimizationConfig,
) -> EnhancedMemoryOptimizer {
    EnhancedMemoryOptimizer::new(config)
}