torsh-jit 0.1.3

//! Adaptive Compilation for ToRSh JIT
//!
//! This module implements adaptive compilation that adjusts compilation strategies
//! based on runtime feedback, execution patterns, and performance characteristics.

use crate::{ComputationGraph, JitError, JitResult, NodeId};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::{
    atomic::{AtomicU64, Ordering},
    Arc, RwLock,
};
use std::time::{Duration, Instant};

/// Adaptive compilation manager
pub struct AdaptiveCompiler {
    config: AdaptiveConfig,
    compilation_strategies: Arc<RwLock<HashMap<NodeId, CompilationStrategy>>>,
    performance_monitor: Arc<RwLock<PerformanceMonitor>>,
    adaptation_engine: AdaptationEngine,
    compilation_counter: AtomicU64,
}

/// Configuration for adaptive compilation
#[derive(Debug, Clone)]
pub struct AdaptiveConfig {
    /// Minimum executions before adaptation
    pub min_executions_for_adaptation: u64,

    /// Performance monitoring window size
    pub monitoring_window_size: usize,

    /// Adaptation frequency (how often to reconsider strategies)
    pub adaptation_frequency: u64,

    /// Performance improvement threshold to trigger recompilation
    pub improvement_threshold: f64,

    /// Maximum number of compilation attempts per node
    pub max_compilation_attempts: u32,

    /// Enable tier-based compilation
    pub enable_tiered_compilation: bool,

    /// Enable profile-driven adaptation
    pub enable_profile_driven_adaptation: bool,

    /// Enable workload-aware adaptation
    pub enable_workload_aware_adaptation: bool,

    /// Enable resource-aware adaptation
    pub enable_resource_aware_adaptation: bool,

    /// Compilation timeout
    pub compilation_timeout: Duration,

    /// Memory limit for compilation
    pub compilation_memory_limit: usize,
}

impl Default for AdaptiveConfig {
    fn default() -> Self {
        Self {
            min_executions_for_adaptation: 50,
            monitoring_window_size: 100,
            adaptation_frequency: 1000,
            improvement_threshold: 0.05, // 5% improvement threshold
            max_compilation_attempts: 5,
            enable_tiered_compilation: true,
            enable_profile_driven_adaptation: true,
            enable_workload_aware_adaptation: true,
            enable_resource_aware_adaptation: true,
            compilation_timeout: Duration::from_secs(60),
            compilation_memory_limit: 1024 * 1024 * 1024, // 1GB
        }
    }
}

/// Compilation strategy for a node
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub struct CompilationStrategy {
    pub strategy_type: StrategyType,
    pub optimization_level: OptimizationLevel,
    pub compilation_tier: CompilationTier,
    pub target_metrics: TargetMetrics,
    pub compilation_flags: CompilationFlags,
    pub performance_history: VecDeque<PerformanceMetrics>,
    pub compilation_attempts: u32,
    pub last_updated: std::time::SystemTime,
}

/// Types of compilation strategies
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum StrategyType {
    /// Fast compilation with basic optimizations
    FastCompilation,

    /// Balanced compilation with moderate optimizations
    BalancedCompilation,

    /// Aggressive optimization for hot code
    AggressiveOptimization,

    /// Specialized compilation for specific workloads
    WorkloadSpecific { workload_type: WorkloadType },

    /// Adaptive strategy that changes based on feedback
    Adaptive { base_strategy: Box<StrategyType> },

    /// Custom strategy with user-defined parameters
    Custom { parameters: HashMap<String, String> },
}

/// Workload types for specialized compilation
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum WorkloadType {
    /// Compute-intensive workloads
    ComputeIntensive,

    /// Memory-intensive workloads
    MemoryIntensive,

    /// I/O intensive workloads
    IoIntensive,

    /// Irregular computation patterns
    Irregular,

    /// Streaming/pipeline workloads
    Streaming,

    /// Batch processing workloads
    Batch,
}

/// Optimization levels
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum OptimizationLevel {
    /// No optimizations (-O0)
    None,

    /// Basic optimizations (-O1)
    Basic,

    /// Standard optimizations (-O2)
    Standard,

    /// Aggressive optimizations (-O3)
    Aggressive,

    /// Size optimizations (-Os)
    Size,

    /// Custom optimization level
    Custom { level: u8, flags: Vec<String> },
}

/// Compilation tiers for tiered compilation
#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum CompilationTier {
    /// Interpreted execution
    Interpreter,

    /// Quick compilation with minimal optimizations
    Tier1,

    /// Optimized compilation
    Tier2,

    /// Highly optimized compilation
    Tier3,

    /// Specialized compilation for hot code
    Tier4,
}

/// Target performance metrics for compilation
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub struct TargetMetrics {
    /// Target execution time in microseconds
    pub target_execution_time: Option<u64>,

    /// Target memory usage in bytes
    pub target_memory_usage: Option<usize>,

    /// Target compilation time in milliseconds
    pub target_compilation_time: Option<u64>,

    /// Target throughput (operations per second)
    pub target_throughput: Option<f64>,

    /// Target energy efficiency (operations per joule)
    pub target_energy_efficiency: Option<f64>,
}

/// Compilation flags and options
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub struct CompilationFlags {
    /// Enable vectorization
    pub enable_vectorization: bool,

    /// Enable parallelization
    pub enable_parallelization: bool,

    /// Enable loop unrolling
    pub enable_loop_unrolling: bool,

    /// Enable function inlining
    pub enable_inlining: bool,

    /// Enable dead code elimination
    pub enable_dead_code_elimination: bool,

    /// Enable constant folding
    pub enable_constant_folding: bool,

    /// Enable instruction selection optimization
    pub enable_instruction_selection: bool,

    /// Enable register allocation optimization
    pub enable_register_allocation: bool,

    /// Custom compilation flags
    pub custom_flags: Vec<String>,
}

impl Default for CompilationFlags {
    fn default() -> Self {
        Self {
            enable_vectorization: true,
            enable_parallelization: true,
            enable_loop_unrolling: true,
            enable_inlining: true,
            enable_dead_code_elimination: true,
            enable_constant_folding: true,
            enable_instruction_selection: true,
            enable_register_allocation: true,
            custom_flags: Vec::new(),
        }
    }
}

/// Performance metrics for a compilation
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    /// Execution time in microseconds
    pub execution_time: u64,

    /// Memory usage in bytes
    pub memory_usage: usize,

    /// Compilation time in milliseconds
    pub compilation_time: u64,

    /// Throughput (operations per second)
    pub throughput: f64,

    /// Cache hit rate
    pub cache_hit_rate: f64,

    /// Energy consumption in millijoules
    pub energy_consumption: f64,

    /// CPU utilization percentage
    pub cpu_utilization: f64,

    /// Memory bandwidth utilization
    pub memory_bandwidth_utilization: f64,

    /// Timestamp of measurement
    pub timestamp: std::time::SystemTime,
}

/// Performance monitoring system
#[derive(Debug, Clone)]
pub struct PerformanceMonitor {
    /// Recent performance measurements
    measurements: VecDeque<PerformanceMetrics>,

    /// Performance baselines for comparison
    baselines: HashMap<NodeId, PerformanceMetrics>,

    /// System resource monitor
    resource_monitor: ResourceMonitor,

    /// Workload classifier
    workload_classifier: WorkloadClassifier,
}

/// System resource monitoring
#[derive(Debug, Clone)]
pub struct ResourceMonitor {
    /// Available CPU cores
    cpu_cores: usize,

    /// Available memory in bytes
    available_memory: usize,

    /// Current CPU usage
    cpu_usage: f64,

    /// Current memory usage
    memory_usage: f64,

    /// Thermal throttling status
    thermal_throttling: bool,

    /// Power consumption
    power_consumption: f64,
}

/// Workload classification system
#[derive(Debug, Clone)]
pub struct WorkloadClassifier {
    /// Current workload characteristics
    current_workload: WorkloadCharacteristics,

    /// Historical workload patterns
    workload_history: VecDeque<WorkloadCharacteristics>,
}

/// Characteristics of the current workload
#[derive(Debug, Clone)]
pub struct WorkloadCharacteristics {
    /// Computation intensity
    compute_intensity: f64,

    /// Memory intensity
    memory_intensity: f64,

    /// I/O intensity
    io_intensity: f64,

    /// Parallelism degree
    parallelism_degree: f64,

    /// Data locality
    data_locality: f64,

    /// Execution regularity
    execution_regularity: f64,

    /// Working set size
    working_set_size: usize,
}

/// Adaptation engine that makes compilation decisions
pub struct AdaptationEngine {
    /// Decision algorithms
    decision_algorithms: Vec<Box<dyn DecisionAlgorithm>>,

    /// Learning models for performance prediction
    performance_models: HashMap<String, Box<dyn PerformanceModel>>,

    /// Adaptation history
    adaptation_history: VecDeque<AdaptationDecision>,
}

/// Decision made by the adaptation engine
#[derive(Debug, Clone)]
pub struct AdaptationDecision {
    pub node_id: NodeId,
    pub old_strategy: CompilationStrategy,
    pub new_strategy: CompilationStrategy,
    pub reason: String,
    pub expected_improvement: f64,
    pub confidence: f64,
    pub timestamp: std::time::SystemTime,
}

/// Algorithm for making adaptation decisions
pub trait DecisionAlgorithm: Send + Sync {
    fn evaluate(
        &self,
        node_id: NodeId,
        current_strategy: &CompilationStrategy,
        performance_history: &[PerformanceMetrics],
        workload_characteristics: &WorkloadCharacteristics,
        resource_state: &ResourceMonitor,
    ) -> Option<CompilationStrategy>;

    fn confidence(&self) -> f64;
    fn name(&self) -> &str;
}

/// Model for predicting performance of compilation strategies
pub trait PerformanceModel: Send + Sync {
    fn predict(
        &self,
        strategy: &CompilationStrategy,
        workload: &WorkloadCharacteristics,
    ) -> PerformanceMetrics;

    fn update(&mut self, actual_performance: &PerformanceMetrics);
    fn accuracy(&self) -> f64;
}

/// Result of adaptive compilation
#[derive(Debug, Clone)]
pub struct AdaptationResult {
    pub adaptations_made: Vec<AdaptationDecision>,
    pub performance_improvement: f64,
    pub compilation_overhead: Duration,
    pub strategies_updated: usize,
}

impl AdaptiveCompiler {
    /// Create a new adaptive compiler
    pub fn new(config: AdaptiveConfig) -> Self {
        let performance_monitor = PerformanceMonitor::new();
        let adaptation_engine = AdaptationEngine::new();

        Self {
            config,
            compilation_strategies: Arc::new(RwLock::new(HashMap::new())),
            performance_monitor: Arc::new(RwLock::new(performance_monitor)),
            adaptation_engine,
            compilation_counter: AtomicU64::new(0),
        }
    }

    /// Get or initialize compilation strategy for a node
    pub fn get_compilation_strategy(&self, node_id: NodeId) -> JitResult<CompilationStrategy> {
        let strategies = self.compilation_strategies.read().map_err(|_| {
            JitError::RuntimeError("Failed to read compilation strategies".to_string())
        })?;

        if let Some(strategy) = strategies.get(&node_id) {
            Ok(strategy.clone())
        } else {
            // Initialize with default strategy
            Ok(self.create_initial_strategy(node_id))
        }
    }

    /// Update performance metrics for a node
    pub fn record_performance(
        &self,
        node_id: NodeId,
        metrics: PerformanceMetrics,
    ) -> JitResult<()> {
        // Update strategy performance history
        if let Ok(mut strategies) = self.compilation_strategies.write() {
            if let Some(strategy) = strategies.get_mut(&node_id) {
                strategy.performance_history.push_back(metrics.clone());

                // Keep only recent measurements
                while strategy.performance_history.len() > self.config.monitoring_window_size {
                    strategy.performance_history.pop_front();
                }
            }
        }

        // Update global performance monitor
        if let Ok(mut monitor) = self.performance_monitor.write() {
            monitor.record_measurement(metrics);
        }

        Ok(())
    }

    /// Trigger adaptation process
    pub fn adapt(&mut self) -> JitResult<AdaptationResult> {
        let start_time = Instant::now();
        let mut adaptations = Vec::new();
        let mut strategies_updated = 0;

        let compilation_count = self.compilation_counter.fetch_add(1, Ordering::Relaxed);

        // Check if it's time to adapt
        if compilation_count % self.config.adaptation_frequency != 0 {
            return Ok(AdaptationResult {
                adaptations_made: adaptations,
                performance_improvement: 0.0,
                compilation_overhead: Duration::ZERO,
                strategies_updated,
            });
        }

        // Get current strategies and performance data
        let strategies = self
            .compilation_strategies
            .read()
            .map_err(|_| JitError::RuntimeError("Failed to read strategies".to_string()))?
            .clone();

        let monitor = self.performance_monitor.read().map_err(|_| {
            JitError::RuntimeError("Failed to read performance monitor".to_string())
        })?;

        let workload_characteristics = monitor.workload_classifier.current_workload.clone();
        let resource_state = monitor.resource_monitor.clone();

        drop(monitor); // Release read lock

        // Evaluate each node for potential adaptation
        for (node_id, current_strategy) in &strategies {
            if current_strategy.performance_history.len()
                < self.config.min_executions_for_adaptation as usize
            {
                continue; // Not enough data for adaptation
            }

            if current_strategy.compilation_attempts >= self.config.max_compilation_attempts {
                continue; // Maximum attempts reached
            }

            // Use adaptation engine to evaluate potential improvements
            if let Some(new_strategy) = self.adaptation_engine.evaluate_adaptation(
                *node_id,
                current_strategy,
                &workload_characteristics,
                &resource_state,
            ) {
                // Calculate expected improvement
                let expected_improvement =
                    self.calculate_expected_improvement(current_strategy, &new_strategy);

                if expected_improvement > self.config.improvement_threshold {
                    // Calculate confidence based on data quality and improvement magnitude
                    let confidence = self
                        .calculate_adaptation_confidence(current_strategy, expected_improvement);

                    let decision = AdaptationDecision {
                        node_id: *node_id,
                        old_strategy: current_strategy.clone(),
                        new_strategy: new_strategy.clone(),
                        reason: "Performance improvement detected".to_string(),
                        expected_improvement,
                        confidence,
                        timestamp: std::time::SystemTime::now(),
                    };

                    adaptations.push(decision);
                    strategies_updated += 1;
                }
            }
        }

        // Apply adaptations
        if !adaptations.is_empty() {
            self.apply_adaptations(&adaptations)?;
        }

        let compilation_overhead = start_time.elapsed();
        let total_improvement = adaptations
            .iter()
            .map(|a| a.expected_improvement)
            .sum::<f64>()
            / adaptations.len().max(1) as f64;

        Ok(AdaptationResult {
            adaptations_made: adaptations,
            performance_improvement: total_improvement,
            compilation_overhead,
            strategies_updated,
        })
    }

    /// Apply compilation strategy to generate code
    pub fn compile_with_strategy(
        &self,
        graph: &ComputationGraph,
        node_id: NodeId,
        strategy: &CompilationStrategy,
    ) -> JitResult<CompiledCode> {
        let start_time = Instant::now();

        // Apply strategy-specific compilation
        let compiled_code = match strategy.strategy_type {
            StrategyType::FastCompilation => self.compile_fast(graph, node_id, strategy),
            StrategyType::BalancedCompilation => self.compile_balanced(graph, node_id, strategy),
            StrategyType::AggressiveOptimization => {
                self.compile_aggressive(graph, node_id, strategy)
            }
            StrategyType::WorkloadSpecific { ref workload_type } => {
                self.compile_workload_specific(graph, node_id, strategy, workload_type)
            }
            StrategyType::Adaptive { ref base_strategy } => {
                self.compile_adaptive(graph, node_id, strategy, base_strategy)
            }
            StrategyType::Custom { ref parameters } => {
                self.compile_custom(graph, node_id, strategy, parameters)
            }
        }?;

        let compilation_time = start_time.elapsed();

        // Record compilation metrics
        self.record_compilation_metrics(node_id, compilation_time)?;

        Ok(compiled_code)
    }

    /// Get adaptation statistics
    pub fn get_adaptation_statistics(&self) -> JitResult<AdaptationStatistics> {
        let strategies = self
            .compilation_strategies
            .read()
            .map_err(|_| JitError::RuntimeError("Failed to read strategies".to_string()))?;

        let total_nodes = strategies.len();
        let adaptations_count = self.adaptation_engine.adaptation_history.len();

        let tier_distribution = strategies
            .values()
            .fold(HashMap::new(), |mut acc, strategy| {
                *acc.entry(strategy.compilation_tier.clone()).or_insert(0) += 1;
                acc
            });

        let avg_performance = if !strategies.is_empty() {
            strategies
                .values()
                .flat_map(|s| s.performance_history.iter())
                .map(|m| m.execution_time)
                .sum::<u64>() as f64
                / strategies
                    .values()
                    .map(|s| s.performance_history.len())
                    .sum::<usize>()
                    .max(1) as f64
        } else {
            0.0
        };

        Ok(AdaptationStatistics {
            total_nodes,
            adaptations_count,
            tier_distribution,
            avg_performance,
            compilation_count: self.compilation_counter.load(Ordering::Relaxed),
        })
    }

    // Helper methods
    fn create_initial_strategy(&self, _node_id: NodeId) -> CompilationStrategy {
        let strategy_type = if self.config.enable_tiered_compilation {
            StrategyType::FastCompilation // Start with fast compilation
        } else {
            StrategyType::BalancedCompilation
        };

        CompilationStrategy {
            strategy_type,
            optimization_level: OptimizationLevel::Basic,
            compilation_tier: CompilationTier::Tier1,
            target_metrics: TargetMetrics {
                target_execution_time: None,
                target_memory_usage: None,
                target_compilation_time: Some(1000), // 1 second
                target_throughput: None,
                target_energy_efficiency: None,
            },
            compilation_flags: CompilationFlags::default(),
            performance_history: VecDeque::new(),
            compilation_attempts: 0,
            last_updated: std::time::SystemTime::now(),
        }
    }

    fn calculate_expected_improvement(
        &self,
        current_strategy: &CompilationStrategy,
        new_strategy: &CompilationStrategy,
    ) -> f64 {
        // Simple heuristic based on optimization level and tier
        let tier_improvement = match (
            current_strategy.compilation_tier.clone(),
            new_strategy.compilation_tier.clone(),
        ) {
            (CompilationTier::Tier1, CompilationTier::Tier2) => 0.15,
            (CompilationTier::Tier2, CompilationTier::Tier3) => 0.10,
            (CompilationTier::Tier3, CompilationTier::Tier4) => 0.05,
            _ => 0.0,
        };

        let optimization_improvement = match (
            current_strategy.optimization_level.clone(),
            new_strategy.optimization_level.clone(),
        ) {
            (OptimizationLevel::Basic, OptimizationLevel::Standard) => 0.10,
            (OptimizationLevel::Standard, OptimizationLevel::Aggressive) => 0.08,
            _ => 0.0,
        };

        tier_improvement + optimization_improvement
    }

    /// Calculate confidence for an adaptation decision based on data quality and improvement magnitude
    fn calculate_adaptation_confidence(
        &self,
        current_strategy: &CompilationStrategy,
        expected_improvement: f64,
    ) -> f64 {
        // Base confidence on amount of historical data
        let history_size = current_strategy.performance_history.len();
        let min_executions = self.config.min_executions_for_adaptation as usize;

        // Data quality factor: more data = higher confidence, capped at 1.0
        let data_quality_factor = if history_size >= min_executions * 4 {
            1.0
        } else if history_size >= min_executions * 2 {
            0.9
        } else if history_size >= min_executions {
            0.8
        } else {
            0.6
        };

        // Calculate variance in performance history to assess stability
        let variance_factor = if !current_strategy.performance_history.is_empty() {
            // Extract execution times as f64 for statistical analysis
            let exec_times: Vec<f64> = current_strategy
                .performance_history
                .iter()
                .map(|m| m.execution_time as f64)
                .collect();

            let mean: f64 = exec_times.iter().sum::<f64>() / history_size as f64;
            let variance: f64 =
                exec_times.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / history_size as f64;
            let std_dev = variance.sqrt();
            let coefficient_of_variation = if mean > 0.0 { std_dev / mean } else { 1.0 };

            // Lower variance = higher confidence
            if coefficient_of_variation < 0.1 {
                1.0
            } else if coefficient_of_variation < 0.2 {
                0.9
            } else if coefficient_of_variation < 0.3 {
                0.8
            } else {
                0.7
            }
        } else {
            0.7 // Default if no history
        };

        // Improvement magnitude factor: larger improvements = higher confidence
        let improvement_factor = if expected_improvement > 0.2 {
            1.0
        } else if expected_improvement > 0.1 {
            0.95
        } else if expected_improvement > 0.05 {
            0.9
        } else {
            0.85
        };

        // Combine factors with weights
        let raw_confidence =
            data_quality_factor * 0.4 + variance_factor * 0.4 + improvement_factor * 0.2;
        let confidence = f64::min(f64::max(raw_confidence, 0.0), 1.0);

        confidence
    }

    fn apply_adaptations(&self, adaptations: &[AdaptationDecision]) -> JitResult<()> {
        if let Ok(mut strategies) = self.compilation_strategies.write() {
            for adaptation in adaptations {
                strategies.insert(adaptation.node_id, adaptation.new_strategy.clone());
            }
        }

        // Record adaptations in history
        self.adaptation_engine.record_adaptations(adaptations);

        Ok(())
    }

    fn compile_fast(
        &self,
        _graph: &ComputationGraph,
        _node_id: NodeId,
        _strategy: &CompilationStrategy,
    ) -> JitResult<CompiledCode> {
        // Fast compilation with minimal optimizations
        Ok(CompiledCode {
            code: vec![0xCC; 100], // Placeholder
            metadata: CompilationMetadata {
                optimization_level: OptimizationLevel::Basic,
                compilation_time: Duration::from_millis(10),
                code_size: 100,
            },
        })
    }

    fn compile_balanced(
        &self,
        _graph: &ComputationGraph,
        _node_id: NodeId,
        _strategy: &CompilationStrategy,
    ) -> JitResult<CompiledCode> {
        // Balanced compilation
        Ok(CompiledCode {
            code: vec![0xCC; 200], // Placeholder
            metadata: CompilationMetadata {
                optimization_level: OptimizationLevel::Standard,
                compilation_time: Duration::from_millis(100),
                code_size: 200,
            },
        })
    }

    fn compile_aggressive(
        &self,
        _graph: &ComputationGraph,
        _node_id: NodeId,
        _strategy: &CompilationStrategy,
    ) -> JitResult<CompiledCode> {
        // Aggressive optimization
        Ok(CompiledCode {
            code: vec![0xCC; 150], // Placeholder - smaller due to optimizations
            metadata: CompilationMetadata {
                optimization_level: OptimizationLevel::Aggressive,
                compilation_time: Duration::from_millis(500),
                code_size: 150,
            },
        })
    }

    fn compile_workload_specific(
        &self,
        _graph: &ComputationGraph,
        _node_id: NodeId,
        _strategy: &CompilationStrategy,
        _workload_type: &WorkloadType,
    ) -> JitResult<CompiledCode> {
        // Workload-specific compilation
        Ok(CompiledCode {
            code: vec![0xCC; 180], // Placeholder
            metadata: CompilationMetadata {
                optimization_level: OptimizationLevel::Standard,
                compilation_time: Duration::from_millis(200),
                code_size: 180,
            },
        })
    }

    fn compile_adaptive(
        &self,
        _graph: &ComputationGraph,
        _node_id: NodeId,
        _strategy: &CompilationStrategy,
        _base_strategy: &StrategyType,
    ) -> JitResult<CompiledCode> {
        // Adaptive compilation
        Ok(CompiledCode {
            code: vec![0xCC; 160], // Placeholder
            metadata: CompilationMetadata {
                optimization_level: OptimizationLevel::Standard,
                compilation_time: Duration::from_millis(150),
                code_size: 160,
            },
        })
    }

    fn compile_custom(
        &self,
        _graph: &ComputationGraph,
        _node_id: NodeId,
        _strategy: &CompilationStrategy,
        _parameters: &HashMap<String, String>,
    ) -> JitResult<CompiledCode> {
        // Custom compilation
        Ok(CompiledCode {
            code: vec![0xCC; 170], // Placeholder
            metadata: CompilationMetadata {
                optimization_level: OptimizationLevel::Custom {
                    level: 2,
                    flags: vec!["custom".to_string()],
                },
                compilation_time: Duration::from_millis(300),
                code_size: 170,
            },
        })
    }

    fn record_compilation_metrics(
        &self,
        _node_id: NodeId,
        _compilation_time: Duration,
    ) -> JitResult<()> {
        // Record compilation metrics for analysis
        Ok(())
    }
}

impl PerformanceMonitor {
    pub fn new() -> Self {
        Self {
            measurements: VecDeque::new(),
            baselines: HashMap::new(),
            resource_monitor: ResourceMonitor::new(),
            workload_classifier: WorkloadClassifier::new(),
        }
    }

    pub fn record_measurement(&mut self, metrics: PerformanceMetrics) {
        self.measurements.push_back(metrics);

        // Keep only recent measurements
        while self.measurements.len() > 1000 {
            self.measurements.pop_front();
        }
    }
}

impl ResourceMonitor {
    pub fn new() -> Self {
        Self {
            cpu_cores: std::thread::available_parallelism()
                .map(|p| p.get())
                .unwrap_or(4),
            available_memory: 8 * 1024 * 1024 * 1024, // 8GB placeholder
            cpu_usage: 0.5,                           // 50% placeholder
            memory_usage: 0.3,                        // 30% placeholder
            thermal_throttling: false,
            power_consumption: 50.0, // 50W placeholder
        }
    }
}

impl WorkloadClassifier {
    pub fn new() -> Self {
        Self {
            current_workload: WorkloadCharacteristics {
                compute_intensity: 0.5,
                memory_intensity: 0.3,
                io_intensity: 0.2,
                parallelism_degree: 0.6,
                data_locality: 0.7,
                execution_regularity: 0.8,
                working_set_size: 1024 * 1024, // 1MB
            },
            workload_history: VecDeque::new(),
        }
    }
}

impl AdaptationEngine {
    pub fn new() -> Self {
        Self {
            decision_algorithms: Vec::new(),
            performance_models: HashMap::new(),
            adaptation_history: VecDeque::new(),
        }
    }

    pub fn evaluate_adaptation(
        &self,
        _node_id: NodeId,
        current_strategy: &CompilationStrategy,
        _workload_characteristics: &WorkloadCharacteristics,
        _resource_state: &ResourceMonitor,
    ) -> Option<CompilationStrategy> {
        // Simple adaptation logic - upgrade tier if performance is poor
        if current_strategy.performance_history.len() > 10 {
            let avg_execution_time = current_strategy
                .performance_history
                .iter()
                .map(|m| m.execution_time)
                .sum::<u64>()
                / current_strategy.performance_history.len() as u64;

            // If execution time is high, try a higher tier
            if avg_execution_time > 1000 {
                // 1ms threshold
                let new_tier = match current_strategy.compilation_tier {
                    CompilationTier::Tier1 => CompilationTier::Tier2,
                    CompilationTier::Tier2 => CompilationTier::Tier3,
                    CompilationTier::Tier3 => CompilationTier::Tier4,
                    _ => return None,
                };

                let mut new_strategy = current_strategy.clone();
                new_strategy.compilation_tier = new_tier;
                new_strategy.optimization_level = OptimizationLevel::Standard;
                new_strategy.compilation_attempts += 1;
                new_strategy.last_updated = std::time::SystemTime::now();

                return Some(new_strategy);
            }
        }

        None
    }

    pub fn record_adaptations(&self, _adaptations: &[AdaptationDecision]) {
        // Record adaptation decisions for learning
    }
}

/// Compiled code result
#[derive(Debug, Clone)]
pub struct CompiledCode {
    pub code: Vec<u8>,
    pub metadata: CompilationMetadata,
}

/// Metadata about the compilation process
#[derive(Debug, Clone)]
pub struct CompilationMetadata {
    pub optimization_level: OptimizationLevel,
    pub compilation_time: Duration,
    pub code_size: usize,
}

/// Statistics about the adaptation process
#[derive(Debug, Clone)]
pub struct AdaptationStatistics {
    pub total_nodes: usize,
    pub adaptations_count: usize,
    pub tier_distribution: HashMap<CompilationTier, usize>,
    pub avg_performance: f64,
    pub compilation_count: u64,
}

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

    #[test]
    fn test_adaptive_compiler_creation() {
        let config = AdaptiveConfig::default();
        let compiler = AdaptiveCompiler::new(config);
        assert_eq!(compiler.compilation_counter.load(Ordering::Relaxed), 0);
    }

    #[test]
    fn test_initial_strategy_creation() {
        let compiler = AdaptiveCompiler::new(AdaptiveConfig::default());
        let strategy = compiler.create_initial_strategy(NodeId::new(1));
        assert_eq!(strategy.compilation_tier, CompilationTier::Tier1);
        assert_eq!(strategy.compilation_attempts, 0);
    }

    #[test]
    fn test_performance_recording() {
        let compiler = AdaptiveCompiler::new(AdaptiveConfig::default());

        let metrics = PerformanceMetrics {
            execution_time: 1000,
            memory_usage: 1024,
            compilation_time: 100,
            throughput: 1000.0,
            cache_hit_rate: 0.8,
            energy_consumption: 10.0,
            cpu_utilization: 0.7,
            memory_bandwidth_utilization: 0.6,
            timestamp: std::time::SystemTime::now(),
        };

        compiler
            .record_performance(NodeId::new(1), metrics)
            .unwrap();

        let strategy = compiler.get_compilation_strategy(NodeId::new(1)).unwrap();
        assert!(strategy.performance_history.is_empty()); // No history yet since strategy wasn't initialized
    }

    #[test]
    fn test_expected_improvement_calculation() {
        let compiler = AdaptiveCompiler::new(AdaptiveConfig::default());

        let current_strategy = CompilationStrategy {
            strategy_type: StrategyType::FastCompilation,
            optimization_level: OptimizationLevel::Basic,
            compilation_tier: CompilationTier::Tier1,
            target_metrics: TargetMetrics {
                target_execution_time: None,
                target_memory_usage: None,
                target_compilation_time: None,
                target_throughput: None,
                target_energy_efficiency: None,
            },
            compilation_flags: CompilationFlags::default(),
            performance_history: VecDeque::new(),
            compilation_attempts: 0,
            last_updated: std::time::SystemTime::now(),
        };

        let new_strategy = CompilationStrategy {
            compilation_tier: CompilationTier::Tier2,
            optimization_level: OptimizationLevel::Standard,
            ..current_strategy.clone()
        };

        let improvement = compiler.calculate_expected_improvement(&current_strategy, &new_strategy);
        assert!(improvement > 0.0);
    }
}