torsh-backend 0.1.2

//! Performance Tuning Coordination
//!
//! This module contains the main coordination logic for the performance tuning system,
//! including the PerformanceTuningCoordinator implementation and supporting components
//! for global monitoring, workload classification, adaptive control, and optimization caching.

use super::strategies::{
    CpuTuningStrategy, CudaTuningStrategy, MetalTuningStrategy, WebGpuTuningStrategy,
};
use super::types::*;
use crate::backend::BackendType;
use crate::error::BackendResult;
use std::collections::hash_map::DefaultHasher;
use std::collections::HashMap;
use std::hash::{Hash, Hasher};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant};
use torsh_core::error::TorshError;

// ================================================================================================
// PerformanceTuningCoordinator Implementation
// ================================================================================================

impl PerformanceTuningCoordinator {
    /// Create a new performance tuning coordinator
    pub fn new() -> BackendResult<Self> {
        let mut strategies = HashMap::new();

        // Initialize backend-specific strategies
        strategies.insert(
            BackendType::Cpu,
            Box::new(CpuTuningStrategy::new()?) as Box<dyn BackendTuningStrategy + Send + Sync>,
        );
        strategies.insert(
            BackendType::Cuda,
            Box::new(CudaTuningStrategy::new()?) as Box<dyn BackendTuningStrategy + Send + Sync>,
        );
        strategies.insert(
            BackendType::Metal,
            Box::new(MetalTuningStrategy::new()?) as Box<dyn BackendTuningStrategy + Send + Sync>,
        );
        strategies.insert(
            BackendType::WebGpu,
            Box::new(WebGpuTuningStrategy::new()?) as Box<dyn BackendTuningStrategy + Send + Sync>,
        );

        Ok(Self {
            strategies: Arc::new(RwLock::new(strategies)),
            global_monitor: Arc::new(Mutex::new(GlobalPerformanceMonitor::new())),
            workload_classifier: WorkloadClassifier::new()?,
            adaptive_controller: AdaptiveTuningController::new(),
            optimization_cache: Arc::new(RwLock::new(OptimizationCache::new(1000))),
        })
    }

    /// Get tuning recommendation for a workload
    pub fn get_tuning_recommendation(
        &self,
        backend_type: BackendType,
        workload: &WorkloadCharacteristics,
        system_state: &SystemState,
        constraints: &TuningConstraints,
    ) -> BackendResult<TuningRecommendation> {
        // Check cache first
        let cache_key = self.compute_cache_key(backend_type, workload, system_state);
        if let Some(cached) = self.get_cached_optimization(cache_key)? {
            if cached.confidence > 0.8 && cached.timestamp.elapsed() < Duration::from_secs(300) {
                return Ok(TuningRecommendation {
                    parameters: cached.parameters,
                    expected_performance: cached.prediction,
                    confidence_score: cached.confidence,
                    alternative_configs: Vec::new(),
                    reasoning: "Retrieved from optimization cache".to_string(),
                });
            }
        }

        // Classify workload
        let workload_class = self.workload_classifier.classify(workload)?;

        // Get backend-specific strategy
        let strategies = self.strategies.read().map_err(|_| {
            TorshError::BackendError("Failed to acquire strategies lock".to_string())
        })?;

        let strategy = strategies.get(&backend_type).ok_or_else(|| {
            TorshError::BackendError(format!("No strategy for backend {:?}", backend_type))
        })?;

        // Get tuning recommendation
        let mut recommendation = strategy.tune_for_workload(workload, system_state, constraints)?;

        // Apply adaptive controller suggestions
        let adaptive_params = self
            .adaptive_controller
            .suggest_parameters(workload_class, &recommendation.parameters)?;
        if let Some(params) = adaptive_params {
            recommendation.alternative_configs.push(params);
        }

        // Cache the result
        let cached_opt = CachedOptimization {
            parameters: recommendation.parameters.clone(),
            prediction: recommendation.expected_performance.clone(),
            timestamp: Instant::now(),
            hit_count: 1,
            confidence: recommendation.confidence_score,
        };
        self.cache_optimization(cache_key, cached_opt)?;

        Ok(recommendation)
    }

    /// Provide performance feedback for learning
    pub fn provide_feedback(&mut self, feedback: PerformanceFeedback) -> BackendResult<()> {
        // Update backend strategy
        let backend_type = self.determine_backend_from_feedback(&feedback);
        {
            let mut strategies = self.strategies.write().map_err(|_| {
                TorshError::BackendError("Failed to acquire strategies lock".to_string())
            })?;

            if let Some(strategy) = strategies.get_mut(&backend_type) {
                strategy.update_from_feedback(&feedback)?;
            }
        }

        // Update adaptive controller
        self.adaptive_controller.update_from_feedback(&feedback)?;

        // Update workload classifier
        let workload_class = self.workload_classifier.classify(&feedback.workload)?;
        self.workload_classifier
            .update_classification(&feedback.workload, workload_class)?;

        // Update global monitor
        {
            let mut monitor = self.global_monitor.lock().map_err(|_| {
                TorshError::BackendError("Failed to acquire global monitor lock".to_string())
            })?;
            monitor.update_performance_stats(backend_type, &feedback)?;
        }

        Ok(())
    }

    /// Get global performance statistics with enhanced analytics
    pub fn get_global_stats(&self) -> BackendResult<GlobalPerformanceStats> {
        let monitor = self.global_monitor.lock().map_err(|_| {
            TorshError::BackendError("Failed to acquire global monitor lock".to_string())
        })?;

        let mut stats = monitor.compute_global_stats();

        // Enhance with advanced analytics
        self.enhance_stats_with_analytics(&mut stats)?;

        Ok(stats)
    }

    /// Enhance performance statistics with advanced analytics
    fn enhance_stats_with_analytics(
        &self,
        stats: &mut GlobalPerformanceStats,
    ) -> BackendResult<()> {
        // Calculate performance trends
        let trend_analysis = self.analyze_performance_trends()?;

        // Add trend data to stats
        stats.efficiency_trend = trend_analysis.efficiency_trend;
        stats.throughput_trend = trend_analysis.throughput_trend;
        stats.latency_trend = trend_analysis.latency_trend;

        // Calculate predictive metrics
        let predictions = self.generate_performance_predictions(&stats)?;
        stats.predicted_efficiency = predictions.next_efficiency;
        stats.predicted_bottlenecks = predictions.likely_bottlenecks;

        // Add optimization recommendations
        stats.optimization_recommendations = self.generate_optimization_recommendations(&stats)?;

        Ok(())
    }

    /// Analyze performance trends over time
    fn analyze_performance_trends(&self) -> BackendResult<PerformanceTrendAnalysis> {
        // Simplified trend analysis - generate synthetic data for now
        // In a real implementation, this would analyze historical performance data

        // Return insufficient data to indicate trends need more historical data
        Ok(PerformanceTrendAnalysis::insufficient_data())
    }

    /// Calculate efficiency trend using simple linear regression
    fn calculate_efficiency_trend(&self, metrics: &[PerformanceMetric]) -> TrendDirection {
        if metrics.len() < 2 {
            return TrendDirection::Stable;
        }

        let n = metrics.len() as f64;
        let mut sum_x = 0.0;
        let mut sum_y = 0.0;
        let mut sum_xy = 0.0;
        let mut sum_x2 = 0.0;

        for (i, metric) in metrics.iter().enumerate() {
            let x = i as f64;
            let y = metric.efficiency_score;

            sum_x += x;
            sum_y += y;
            sum_xy += x * y;
            sum_x2 += x * x;
        }

        // Calculate slope of linear regression line
        let slope = (n * sum_xy - sum_x * sum_y) / (n * sum_x2 - sum_x * sum_x);

        if slope > 0.01 {
            TrendDirection::Improving
        } else if slope < -0.01 {
            TrendDirection::Declining
        } else {
            TrendDirection::Stable
        }
    }

    /// Calculate throughput trend
    fn calculate_throughput_trend(&self, metrics: &[PerformanceMetric]) -> TrendDirection {
        if metrics.len() < 5 {
            return TrendDirection::Stable;
        }

        let recent_avg = metrics
            .iter()
            .rev()
            .take(5)
            .map(|m| m.throughput_ops_per_sec)
            .sum::<f64>()
            / 5.0;

        let older_avg = metrics
            .iter()
            .rev()
            .skip(5)
            .take(5)
            .map(|m| m.throughput_ops_per_sec)
            .sum::<f64>()
            / 5.0;

        let change_ratio = (recent_avg - older_avg) / older_avg;

        if change_ratio > 0.05 {
            TrendDirection::Improving
        } else if change_ratio < -0.05 {
            TrendDirection::Declining
        } else {
            TrendDirection::Stable
        }
    }

    /// Calculate latency trend
    fn calculate_latency_trend(&self, metrics: &[PerformanceMetric]) -> TrendDirection {
        if metrics.len() < 5 {
            return TrendDirection::Stable;
        }

        let recent_avg = metrics
            .iter()
            .rev()
            .take(5)
            .map(|m| m.average_latency_ms)
            .sum::<f64>()
            / 5.0;

        let older_avg = metrics
            .iter()
            .rev()
            .skip(5)
            .take(5)
            .map(|m| m.average_latency_ms)
            .sum::<f64>()
            / 5.0;

        let change_ratio = (recent_avg - older_avg) / older_avg;

        // For latency, lower is better, so trend is inverted
        if change_ratio < -0.05 {
            TrendDirection::Improving
        } else if change_ratio > 0.05 {
            TrendDirection::Declining
        } else {
            TrendDirection::Stable
        }
    }

    /// Calculate confidence level for trend analysis
    fn calculate_trend_confidence(&self, sample_size: usize) -> f64 {
        match sample_size {
            0..=10 => 0.3,
            11..=30 => 0.6,
            31..=60 => 0.8,
            61..=100 => 0.9,
            _ => 0.95,
        }
    }

    /// Generate performance predictions
    fn generate_performance_predictions(
        &self,
        stats: &GlobalPerformanceStats,
    ) -> BackendResult<PerformancePredictions> {
        let current_efficiency = stats.average_efficiency;

        // Simple prediction based on trends
        let next_efficiency = match stats.efficiency_trend {
            TrendDirection::Improving => current_efficiency * 1.05,
            TrendDirection::Declining => current_efficiency * 0.95,
            TrendDirection::Stable => current_efficiency,
        };

        // Predict likely bottlenecks based on current performance patterns
        let likely_bottlenecks = self.predict_bottlenecks(stats)?;

        Ok(PerformancePredictions {
            next_efficiency: next_efficiency.clamp(0.0, 1.0),
            likely_bottlenecks,
            prediction_confidence: 0.75, // Fixed confidence for simplicity
        })
    }

    /// Predict likely performance bottlenecks
    fn predict_bottlenecks(&self, stats: &GlobalPerformanceStats) -> BackendResult<Vec<String>> {
        let mut bottlenecks = Vec::new();

        if stats.average_efficiency < 0.7 {
            bottlenecks.push("Low overall efficiency detected".to_string());
        }

        if stats.memory_utilization > 0.9 {
            bottlenecks.push("High memory utilization - potential memory bottleneck".to_string());
        }

        if stats.average_latency_ms > 100.0 {
            bottlenecks.push("High latency detected - potential I/O bottleneck".to_string());
        }

        if stats.throughput_ops_per_sec < 1000.0 {
            bottlenecks.push("Low throughput - potential CPU or compute bottleneck".to_string());
        }

        if bottlenecks.is_empty() {
            bottlenecks.push("No significant bottlenecks detected".to_string());
        }

        Ok(bottlenecks)
    }

    /// Generate optimization recommendations
    fn generate_optimization_recommendations(
        &self,
        stats: &GlobalPerformanceStats,
    ) -> BackendResult<Vec<String>> {
        let mut recommendations = Vec::new();

        // Memory-based recommendations
        if stats.memory_utilization > 0.85 {
            recommendations.push(
                "Consider increasing memory pool size or implementing memory compression"
                    .to_string(),
            );
        }

        if stats.fragmentation_ratio > 0.3 {
            recommendations
                .push("Run memory defragmentation to improve allocation efficiency".to_string());
        }

        // Performance-based recommendations
        if stats.average_efficiency < 0.8 {
            recommendations.push(
                "Enable aggressive optimization strategies for better performance".to_string(),
            );
        }

        if stats.cache_hit_ratio < 0.9 {
            recommendations.push(
                "Tune cache parameters or increase cache size for better hit rates".to_string(),
            );
        }

        // Throughput-based recommendations
        if stats.throughput_ops_per_sec < 5000.0 {
            recommendations
                .push("Consider parallel processing or batch optimization techniques".to_string());
        }

        // Trend-based recommendations
        match stats.efficiency_trend {
            TrendDirection::Declining => {
                recommendations.push(
                    "Performance declining - investigate recent changes or increase monitoring"
                        .to_string(),
                );
            }
            TrendDirection::Improving => {
                recommendations.push(
                    "Performance improving - maintain current optimization strategies".to_string(),
                );
            }
            TrendDirection::Stable => {
                recommendations.push(
                    "Performance stable - consider experimenting with new optimization techniques"
                        .to_string(),
                );
            }
        }

        if recommendations.is_empty() {
            recommendations
                .push("Performance is optimal - no specific recommendations".to_string());
        }

        Ok(recommendations)
    }

    /// Get strategy metrics for specific backend
    pub fn get_strategy_metrics(
        &self,
        backend_type: BackendType,
    ) -> BackendResult<StrategyMetrics> {
        let strategies = self.strategies.read().map_err(|_| {
            TorshError::BackendError("Failed to acquire strategies lock".to_string())
        })?;

        let strategy = strategies.get(&backend_type).ok_or_else(|| {
            TorshError::BackendError(format!("No strategy for backend {:?}", backend_type))
        })?;

        strategy.get_strategy_metrics()
    }

    /// Compute cache key for optimization
    pub fn compute_cache_key(
        &self,
        backend_type: BackendType,
        workload: &WorkloadCharacteristics,
        system_state: &SystemState,
    ) -> u64 {
        let mut hasher = DefaultHasher::new();
        backend_type.hash(&mut hasher);
        workload.operation_type.hash(&mut hasher);
        workload.data_size.hash(&mut hasher);
        workload.data_type.hash(&mut hasher);
        workload.access_pattern.hash(&mut hasher);
        ((system_state.cpu_utilization * 100.0) as u32).hash(&mut hasher);
        ((system_state.memory_utilization * 100.0) as u32).hash(&mut hasher);
        hasher.finish()
    }

    /// Get cached optimization
    fn get_cached_optimization(&self, cache_key: u64) -> BackendResult<Option<CachedOptimization>> {
        let cache = self.optimization_cache.read().map_err(|_| {
            TorshError::BackendError("Failed to acquire optimization cache lock".to_string())
        })?;

        Ok(cache.cache.get(&cache_key).cloned())
    }

    /// Cache optimization result
    fn cache_optimization(
        &self,
        cache_key: u64,
        optimization: CachedOptimization,
    ) -> BackendResult<()> {
        let mut cache = self.optimization_cache.write().map_err(|_| {
            TorshError::BackendError("Failed to acquire optimization cache lock".to_string())
        })?;

        cache.cache.insert(cache_key, optimization);

        // Evict old entries if cache is full
        if cache.cache.len() > cache.max_entries {
            let eviction_count = cache.max_entries / 4;
            cache.evict_lru_entries(eviction_count)?;
        }

        Ok(())
    }

    /// Determine backend type from feedback
    fn determine_backend_from_feedback(&self, _feedback: &PerformanceFeedback) -> BackendType {
        // This would analyze the feedback to determine which backend was used
        // For now, we'll default to CPU
        BackendType::Cpu
    }
}

// ================================================================================================
// GlobalPerformanceMonitor Implementation
// ================================================================================================

impl GlobalPerformanceMonitor {
    pub fn new() -> Self {
        Self {
            backend_performance: HashMap::new(),
            cross_backend_analysis: CrossBackendAnalysis {
                backend_selection_recommendations: HashMap::new(),
                workload_migration_opportunities: Vec::new(),
                hybrid_execution_strategies: Vec::new(),
            },
            system_health_monitor: SystemHealthMonitor {
                thermal_history: Vec::new(),
                power_history: Vec::new(),
                performance_degradation_events: Vec::new(),
            },
        }
    }

    pub fn update_performance_stats(
        &mut self,
        backend_type: BackendType,
        feedback: &PerformanceFeedback,
    ) -> BackendResult<()> {
        let stats = self
            .backend_performance
            .entry(backend_type)
            .or_insert_with(BackendPerformanceStats::default);
        stats.total_operations += 1;
        stats.total_execution_time += feedback.actual_performance.execution_time;
        stats.average_throughput =
            (stats.average_throughput + feedback.actual_performance.throughput) / 2.0;
        stats.peak_memory_usage = stats
            .peak_memory_usage
            .max(feedback.actual_performance.memory_usage_peak);
        Ok(())
    }

    pub fn compute_global_stats(&self) -> GlobalPerformanceStats {
        let total_ops: usize = self
            .backend_performance
            .values()
            .map(|s| s.total_operations)
            .sum();
        let total_time: Duration = self
            .backend_performance
            .values()
            .map(|s| s.total_execution_time)
            .sum();

        GlobalPerformanceStats {
            total_operations: total_ops,
            average_execution_time: total_time / total_ops.max(1) as u32,
            overall_throughput: 1e6, // Placeholder
            energy_efficiency: 0.8,
            cache_hit_ratio: 0.85,
            thermal_efficiency: 0.9,
            backend_utilization: HashMap::new(),

            // Enhanced analytics fields - initialized with default values
            average_efficiency: 0.8,
            memory_utilization: 0.6,
            fragmentation_ratio: 0.2,
            average_latency_ms: 50.0,
            throughput_ops_per_sec: 1000.0,

            // Trend analysis - initialized as stable
            efficiency_trend: TrendDirection::Stable,
            throughput_trend: TrendDirection::Stable,
            latency_trend: TrendDirection::Stable,

            // Predictions - empty initially
            predicted_efficiency: 0.8,
            predicted_bottlenecks: Vec::new(),

            // Recommendations - empty initially
            optimization_recommendations: Vec::new(),
        }
    }
}

// ================================================================================================
// WorkloadClassifier Implementation
// ================================================================================================

impl WorkloadClassifier {
    pub fn new() -> BackendResult<Self> {
        Ok(Self {
            classification_models: HashMap::new(),
            feature_extractors: Vec::new(),
            classification_cache: HashMap::new(),
        })
    }

    pub fn classify(&self, _workload: &WorkloadCharacteristics) -> BackendResult<WorkloadClass> {
        // Simple classification logic - would be replaced with ML models in practice
        Ok(WorkloadClass::ComputeBound)
    }

    pub fn update_classification(
        &mut self,
        _workload: &WorkloadCharacteristics,
        _class: WorkloadClass,
    ) -> BackendResult<()> {
        // Update classification models based on feedback
        Ok(())
    }
}

// ================================================================================================
// AdaptiveTuningController Implementation
// ================================================================================================

impl AdaptiveTuningController {
    pub fn new() -> Self {
        Self {
            exploration_rate: 0.1,
            learning_rate: 0.01,
            discount_factor: 0.9,
            state_action_values: HashMap::new(),
            experience_replay: Vec::new(),
            performance_baseline: HashMap::new(),
        }
    }

    pub fn suggest_parameters(
        &self,
        _workload_class: WorkloadClass,
        _current_params: &TuningParameters,
    ) -> BackendResult<Option<TuningParameters>> {
        // Reinforcement learning-based parameter suggestion
        // For now, return None (no alternative parameters)
        Ok(None)
    }

    pub fn update_from_feedback(&mut self, _feedback: &PerformanceFeedback) -> BackendResult<()> {
        // Update reinforcement learning models based on performance feedback
        Ok(())
    }
}

// ================================================================================================
// OptimizationCache Implementation
// ================================================================================================

impl OptimizationCache {
    pub fn new(max_entries: usize) -> Self {
        Self {
            cache: HashMap::new(),
            hit_count: 0,
            miss_count: 0,
            max_entries,
        }
    }

    pub fn evict_lru_entries(&mut self, _count: usize) -> BackendResult<()> {
        // Simple eviction implementation - would implement proper LRU in practice
        // For now, just clear some entries randomly
        if self.cache.len() > self.max_entries {
            let keys_to_remove: Vec<_> = self.cache.keys().take(_count).cloned().collect();
            for key in keys_to_remove {
                self.cache.remove(&key);
            }
        }
        Ok(())
    }

    pub fn get_hit_rate(&self) -> f64 {
        let total_requests = self.hit_count + self.miss_count;
        if total_requests == 0 {
            0.0
        } else {
            self.hit_count as f64 / total_requests as f64
        }
    }

    pub fn clear(&mut self) {
        self.cache.clear();
        self.hit_count = 0;
        self.miss_count = 0;
    }
}

// ================================================================================================
// Default Implementations
// ================================================================================================

impl Default for PerformanceTuningCoordinator {
    fn default() -> Self {
        Self::new().expect("Failed to create default PerformanceTuningCoordinator")
    }
}

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

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

// ================================================================================================
// Helper Functions
// ================================================================================================

/// Create a default system state for testing
pub fn create_default_system_state() -> SystemState {
    SystemState {
        cpu_utilization: 0.5,
        memory_utilization: 0.6,
        thermal_state: ThermalState {
            cpu_temperature: 65.0,
            gpu_temperature: None,
            thermal_throttling_active: false,
            cooling_efficiency: 0.8,
        },
        power_state: PowerState {
            power_limit: None,
            current_power_draw: 50.0,
            battery_level: None,
            power_efficiency_mode: PowerEfficiencyMode::Balanced,
        },
        concurrent_workloads: 2,
        available_memory_bandwidth: 0.7,
        cache_pressure: 0.4,
        numa_topology: NumaTopologyState {
            node_count: 1,
            current_node: 0,
            memory_distribution: vec![1.0],
            cross_node_traffic: 0.0,
        },
    }
}

/// Create default tuning constraints
pub fn create_default_constraints() -> TuningConstraints {
    TuningConstraints {
        max_memory_usage: None,
        max_power_draw: None,
        max_temperature: None,
        latency_requirement: None,
        throughput_requirement: None,
        energy_budget: None,
        real_time_constraints: false,
    }
}

/// Create a sample workload for testing
pub fn create_sample_workload(op_type: OperationType, data_size: usize) -> WorkloadCharacteristics {
    WorkloadCharacteristics {
        operation_type: op_type,
        data_size,
        data_shape: vec![(data_size as f64).sqrt() as usize; 2],
        data_type: DataType::F32,
        access_pattern: AccessPattern::Sequential,
        compute_intensity: 0.8,
        memory_bandwidth_requirement: 0.6,
        parallelization_potential: 0.9,
        cache_locality: 0.7,
        branch_predictability: 0.95,
        vectorization_potential: 0.85,
    }
}