torsh-backend 0.1.2

//! Enhanced SciRS2 kernel auto-tuning integration
//!
//! This module provides deep integration with SciRS2's kernel auto-tuning system,
//! enabling automatic optimization of kernel parameters based on runtime characteristics.

use crate::cpu::autotuning::{TuningConfig, TuningResult, PerformanceMeasurement, TuningCache};
use crate::cpu::platform_optimization::{X86Microarchitecture, ArmMicroarchitecture, CpuFeatures};
use crate::error::BackendResult;
use torsh_core::error::TorshError;
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};

#[cfg(feature = "serialize")]
use serde::{Serialize, Deserialize};

#[cfg(not(feature = "std"))]
use alloc::{boxed::Box, string::String, vec::Vec};

/// SciRS2 kernel auto-tuner with advanced optimization strategies
#[derive(Debug)]
pub struct SciRS2AutoTuner {
    /// Cache for tuning results
    cache: TuningCache,
    /// Architecture-specific optimization profiles
    optimization_profiles: Arc<Mutex<HashMap<String, OptimizationProfile>>>,
    /// Runtime performance tracker
    performance_tracker: Arc<Mutex<PerformanceTracker>>,
    /// Kernel registry for auto-tuning
    kernel_registry: Arc<Mutex<KernelRegistry>>,
    /// Adaptive tuning controller
    adaptive_controller: AdaptiveTuningController,
}

/// Architecture-specific optimization profile
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serialize", derive(Serialize, Deserialize))]
pub struct OptimizationProfile {
    pub architecture: String,
    pub microarchitecture: String,
    pub preferred_vector_width: usize,
    pub optimal_thread_ratios: Vec<f32>,
    pub cache_blocking_factors: Vec<usize>,
    pub memory_access_patterns: MemoryAccessProfile,
    pub kernel_fusion_opportunities: Vec<FusionOpportunity>,
}

/// Memory access pattern profile for optimization
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serialize", derive(Serialize, Deserialize))]
pub struct MemoryAccessProfile {
    pub sequential_bandwidth: f64, // GB/s
    pub random_bandwidth: f64,     // GB/s
    pub latency_penalty: f64,      // cycles
    pub prefetch_effectiveness: f64, // 0.0 to 1.0
    pub cache_line_utilization: f64, // 0.0 to 1.0
}

/// Kernel fusion opportunity
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serialize", derive(Serialize, Deserialize))]
pub struct FusionOpportunity {
    pub pattern: String,
    pub kernel_types: Vec<String>,
    pub performance_gain: f64, // Expected speedup ratio
    pub memory_reduction: f64, // Expected memory usage reduction
}

/// Runtime performance tracking
#[derive(Debug)]
struct PerformanceTracker {
    operation_stats: HashMap<String, OperationStats>,
    global_stats: GlobalPerformanceStats,
    thermal_history: Vec<ThermalMeasurement>,
    power_history: Vec<PowerMeasurement>,
}

/// Performance statistics for specific operations
#[derive(Debug, Clone)]
struct OperationStats {
    total_calls: usize,
    total_time: Duration,
    average_throughput: f64,
    performance_variance: f64,
    cache_efficiency: f64,
    thermal_impact: f64,
}

/// Global performance statistics
#[derive(Debug, Default)]
struct GlobalPerformanceStats {
    cpu_utilization: f64,
    memory_bandwidth_utilization: f64,
    cache_hit_ratio: f64,
    thermal_throttling_events: usize,
    power_efficiency_score: f64,
}

/// Thermal measurement for thermal-aware optimization
#[derive(Debug, Clone)]
struct ThermalMeasurement {
    timestamp: Instant,
    temperature: f32, // Celsius
    throttling_level: f32, // 0.0 to 1.0
}

/// Power measurement for power-aware optimization
#[derive(Debug, Clone)]
struct PowerMeasurement {
    timestamp: Instant,
    power_draw: f32, // Watts
    efficiency: f32, // Performance per watt
}

/// Kernel registry for auto-tuning
#[derive(Debug)]
struct KernelRegistry {
    kernels: HashMap<String, KernelDescriptor>,
    tuning_sessions: HashMap<String, TuningSession>,
    optimization_history: Vec<OptimizationEvent>,
}

/// Kernel descriptor for auto-tuning
#[derive(Debug, Clone)]
struct KernelDescriptor {
    name: String,
    operation_type: OperationType,
    parameter_space: ParameterSpace,
    constraints: Vec<OptimizationConstraint>,
    scirs2_kernel_id: Option<String>,
}

/// Types of operations for specialized tuning
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
enum OperationType {
    ElementWise,
    MatrixMultiply,
    Convolution,
    Reduction,
    FFT,
    Sort,
    Scan,
    Gather,
    Scatter,
    Custom,
}

/// Parameter space for kernel tuning
#[derive(Debug, Clone)]
struct ParameterSpace {
    thread_counts: Vec<usize>,
    block_sizes: Vec<usize>,
    vector_widths: Vec<usize>,
    unroll_factors: Vec<usize>,
    tile_sizes: Vec<(usize, usize)>,
    scheduling_strategies: Vec<SchedulingStrategy>,
}

/// Scheduling strategies for different workloads
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
enum SchedulingStrategy {
    Static,
    Dynamic,
    Guided,
    WorkStealing,
    NUMA_Aware,
    ThermalAware,
}

/// Optimization constraints
#[derive(Debug, Clone)]
enum OptimizationConstraint {
    MaxMemoryUsage(usize),
    MaxPowerDraw(f32),
    MaxTemperature(f32),
    MinPerformance(f64),
    LatencyBound(Duration),
}

/// Active tuning session
#[derive(Debug)]
struct TuningSession {
    kernel_name: String,
    start_time: Instant,
    tested_configurations: Vec<TestedConfiguration>,
    best_configuration: Option<TestedConfiguration>,
    convergence_threshold: f64,
    max_evaluations: usize,
}

/// Configuration that has been tested
#[derive(Debug, Clone)]
struct TestedConfiguration {
    parameters: HashMap<String, usize>,
    performance: PerformanceMeasurement,
    thermal_impact: f32,
    power_efficiency: f32,
    stability_score: f32,
}

/// Optimization event for learning
#[derive(Debug, Clone)]
struct OptimizationEvent {
    timestamp: Instant,
    kernel_name: String,
    input_characteristics: InputCharacteristics,
    optimization_result: OptimizationResult,
    environment_conditions: EnvironmentConditions,
}

/// Input characteristics for pattern recognition
#[derive(Debug, Clone)]
struct InputCharacteristics {
    size: usize,
    shape: Vec<usize>,
    data_type: String,
    access_pattern: String,
    locality_score: f64,
}

/// Optimization result summary
#[derive(Debug, Clone)]
struct OptimizationResult {
    speedup_achieved: f64,
    memory_efficiency_gain: f64,
    power_efficiency_gain: f64,
    thermal_impact_reduction: f64,
}

/// Environment conditions during optimization
#[derive(Debug, Clone)]
struct EnvironmentConditions {
    cpu_temperature: f32,
    memory_pressure: f64,
    system_load: f64,
    thermal_throttling: bool,
}

/// Adaptive tuning controller using machine learning techniques
#[derive(Debug)]
struct AdaptiveTuningController {
    learning_rate: f64,
    exploration_factor: f64,
    exploitation_threshold: f64,
    convergence_history: Vec<f64>,
    prediction_model: PredictionModel,
}

/// Simple prediction model for parameter optimization
#[derive(Debug)]
struct PredictionModel {
    feature_weights: HashMap<String, f64>,
    bias: f64,
    confidence_threshold: f64,
}

impl SciRS2AutoTuner {
    /// Create a new SciRS2 auto-tuner
    pub fn new() -> BackendResult<Self> {
        let optimization_profiles = Self::initialize_optimization_profiles()?;
        
        Ok(Self {
            cache: TuningCache::new(),
            optimization_profiles: Arc::new(Mutex::new(optimization_profiles)),
            performance_tracker: Arc::new(Mutex::new(PerformanceTracker::new())),
            kernel_registry: Arc::new(Mutex::new(KernelRegistry::new())),
            adaptive_controller: AdaptiveTuningController::new(),
        })
    }
    
    /// Initialize architecture-specific optimization profiles
    fn initialize_optimization_profiles() -> BackendResult<HashMap<String, OptimizationProfile>> {
        let mut profiles = HashMap::new();
        
        // Intel profiles
        profiles.insert("intel_haswell".to_string(), OptimizationProfile {
            architecture: "x86_64".to_string(),
            microarchitecture: "Haswell".to_string(),
            preferred_vector_width: 256,
            optimal_thread_ratios: vec![1.0, 0.8, 0.6, 0.4],
            cache_blocking_factors: vec![16, 128, 4096],
            memory_access_patterns: MemoryAccessProfile {
                sequential_bandwidth: 25.6,
                random_bandwidth: 8.5,
                latency_penalty: 300.0,
                prefetch_effectiveness: 0.85,
                cache_line_utilization: 0.7,
            },
            kernel_fusion_opportunities: vec![
                FusionOpportunity {
                    pattern: "element_wise_chain".to_string(),
                    kernel_types: vec!["add".to_string(), "mul".to_string(), "relu".to_string()],
                    performance_gain: 2.1,
                    memory_reduction: 0.6,
                }
            ],
        });
        
        profiles.insert("intel_skylake".to_string(), OptimizationProfile {
            architecture: "x86_64".to_string(),
            microarchitecture: "Skylake".to_string(),
            preferred_vector_width: 512,
            optimal_thread_ratios: vec![1.0, 0.85, 0.7, 0.5],
            cache_blocking_factors: vec![24, 192, 6144],
            memory_access_patterns: MemoryAccessProfile {
                sequential_bandwidth: 38.4,
                random_bandwidth: 12.8,
                latency_penalty: 280.0,
                prefetch_effectiveness: 0.9,
                cache_line_utilization: 0.8,
            },
            kernel_fusion_opportunities: vec![
                FusionOpportunity {
                    pattern: "conv_bn_relu".to_string(),
                    kernel_types: vec!["conv2d".to_string(), "batch_norm".to_string(), "relu".to_string()],
                    performance_gain: 3.2,
                    memory_reduction: 0.75,
                }
            ],
        });
        
        // Apple Silicon profiles
        profiles.insert("apple_m1".to_string(), OptimizationProfile {
            architecture: "aarch64".to_string(),
            microarchitecture: "M1".to_string(),
            preferred_vector_width: 128,
            optimal_thread_ratios: vec![1.0, 0.8, 0.5, 0.3], // P cores preferred
            cache_blocking_factors: vec![48, 3072, 24576],
            memory_access_patterns: MemoryAccessProfile {
                sequential_bandwidth: 68.25,
                random_bandwidth: 45.5,
                latency_penalty: 120.0,
                prefetch_effectiveness: 0.95,
                cache_line_utilization: 0.9,
            },
            kernel_fusion_opportunities: vec![
                FusionOpportunity {
                    pattern: "neural_engine_chain".to_string(),
                    kernel_types: vec!["matmul".to_string(), "add".to_string(), "activation".to_string()],
                    performance_gain: 4.5,
                    memory_reduction: 0.8,
                }
            ],
        });
        
        profiles.insert("apple_m3".to_string(), OptimizationProfile {
            architecture: "aarch64".to_string(),
            microarchitecture: "M3".to_string(),
            preferred_vector_width: 128,
            optimal_thread_ratios: vec![1.0, 0.85, 0.6, 0.35],
            cache_blocking_factors: vec![48, 4608, 36864],
            memory_access_patterns: MemoryAccessProfile {
                sequential_bandwidth: 150.0,
                random_bandwidth: 100.0,
                latency_penalty: 100.0,
                prefetch_effectiveness: 0.97,
                cache_line_utilization: 0.95,
            },
            kernel_fusion_opportunities: vec![
                FusionOpportunity {
                    pattern: "advanced_ml_chain".to_string(),
                    kernel_types: vec!["matmul".to_string(), "gelu".to_string(), "layernorm".to_string()],
                    performance_gain: 5.2,
                    memory_reduction: 0.85,
                }
            ],
        });
        
        Ok(profiles)
    }
    
    /// Register a kernel for auto-tuning
    pub fn register_kernel(&self, kernel_descriptor: KernelDescriptor) -> BackendResult<()> {
        let mut registry = self.kernel_registry.lock().map_err(|_| {
            TorshError::AllocationError("Failed to acquire kernel registry lock".to_string())
        })?;
        
        registry.kernels.insert(kernel_descriptor.name.clone(), kernel_descriptor);
        Ok(())
    }
    
    /// Tune a kernel for specific input characteristics
    pub fn tune_kernel(
        &self,
        kernel_name: &str,
        input_chars: InputCharacteristics,
        environment: EnvironmentConditions,
    ) -> BackendResult<TuningResult> {
        // Check cache first
        let cache_key = self.generate_cache_key(kernel_name, &input_chars, &environment);
        if let Some(cached_result) = self.cache.get(&cache_key) {
            return Ok(cached_result);
        }
        
        // Get kernel descriptor
        let kernel_desc = {
            let registry = self.kernel_registry.lock().map_err(|_| {
                TorshError::AllocationError("Failed to acquire kernel registry lock".to_string())
            })?;
            
            registry.kernels.get(kernel_name)
                .ok_or_else(|| TorshError::InvalidArgument(format!("Kernel {} not found", kernel_name)))?
                .clone()
        };
        
        // Get optimization profile for current architecture
        let profile = self.get_optimization_profile()?;
        
        // Create tuning session
        let mut session = TuningSession {
            kernel_name: kernel_name.to_string(),
            start_time: Instant::now(),
            tested_configurations: Vec::new(),
            best_configuration: None,
            convergence_threshold: 0.01, // 1% improvement threshold
            max_evaluations: 100,
        };
        
        // Generate candidate configurations
        let candidate_configs = self.generate_candidate_configurations(&kernel_desc, &profile, &input_chars)?;
        
        // Evaluate configurations
        for config in candidate_configs.into_iter().take(session.max_evaluations) {
            let test_result = self.evaluate_configuration(&kernel_desc, &config, &input_chars, &environment)?;
            session.tested_configurations.push(test_result);
            
            // Update best configuration
            if session.best_configuration.is_none() ||
               self.is_better_configuration(&test_result, session.best_configuration.as_ref().expect("best configuration should be present")) {
                session.best_configuration = Some(test_result);
            }
            
            // Check for convergence
            if self.check_convergence(&session) {
                break;
            }
        }
        
        // Convert best configuration to TuningResult
        let tuning_result = self.create_tuning_result(&session, &kernel_desc)?;
        
        // Cache the result
        self.cache.put(cache_key, tuning_result.clone());
        
        // Record optimization event
        self.record_optimization_event(kernel_name, input_chars, &tuning_result, environment)?;
        
        Ok(tuning_result)
    }
    
    /// Generate cache key for tuning results
    fn generate_cache_key(
        &self,
        kernel_name: &str,
        input_chars: &InputCharacteristics,
        environment: &EnvironmentConditions,
    ) -> String {
        format!(
            "{}:{}:{}:{}:{}",
            kernel_name,
            input_chars.size,
            input_chars.data_type,
            (environment.cpu_temperature as u32),
            (environment.memory_pressure * 100.0) as u32
        )
    }
    
    /// Get optimization profile for current architecture
    fn get_optimization_profile(&self) -> BackendResult<OptimizationProfile> {
        let profiles = self.optimization_profiles.lock().map_err(|_| {
            TorshError::AllocationError("Failed to acquire optimization profiles lock".to_string())
        })?;
        
        // Detect current architecture and get appropriate profile
        let arch_key = self.detect_architecture_key()?;
        
        profiles.get(&arch_key)
            .cloned()
            .or_else(|| profiles.get("default"))
            .cloned()
            .ok_or_else(|| TorshError::BackendError("No optimization profile available".to_string()))
    }
    
    /// Detect architecture key for profile lookup
    fn detect_architecture_key(&self) -> BackendResult<String> {
        #[cfg(target_arch = "x86_64")]
        {
            // Use x86_64 enhanced optimizer to detect microarchitecture
            use crate::cpu::x86_64_enhanced::get_optimizer;
            let optimizer = get_optimizer();
            let info = optimizer.get_microarch_info();
            
            let key = match info.name.as_str() {
                "Haswell" | "Broadwell" => "intel_haswell",
                "Skylake" | "KabyLake" | "CoffeeLake" | "IceLake" | "TigerLake" => "intel_skylake",
                _ => "intel_skylake", // Default to Skylake for newer Intel
            };
            
            Ok(key.to_string())
        }
        
        #[cfg(target_arch = "aarch64")]
        {
            // Use ARM64 enhanced optimizer to detect microarchitecture
            use crate::cpu::arm64_enhanced::get_arm64_optimizer;
            let optimizer = get_arm64_optimizer();
            let info = optimizer.get_microarch_info();
            
            let key = match info.name.as_str() {
                "M1" => "apple_m1",
                "M2" => "apple_m1", // Use M1 profile for M2
                "M3" => "apple_m3",
                _ => "apple_m1", // Default to M1 for Apple Silicon
            };
            
            Ok(key.to_string())
        }
        
        #[cfg(not(any(target_arch = "x86_64", target_arch = "aarch64")))]
        {
            Ok("default".to_string())
        }
    }
    
    /// Generate candidate configurations for tuning
    fn generate_candidate_configurations(
        &self,
        kernel_desc: &KernelDescriptor,
        profile: &OptimizationProfile,
        input_chars: &InputCharacteristics,
    ) -> BackendResult<Vec<HashMap<String, usize>>> {
        let mut configs = Vec::new();
        
        // Generate configurations based on operation type and input characteristics
        match kernel_desc.operation_type {
            OperationType::MatrixMultiply => {
                self.generate_matmul_configurations(&mut configs, profile, input_chars)?;
            }
            OperationType::Convolution => {
                self.generate_conv_configurations(&mut configs, profile, input_chars)?;
            }
            OperationType::ElementWise => {
                self.generate_elementwise_configurations(&mut configs, profile, input_chars)?;
            }
            _ => {
                self.generate_default_configurations(&mut configs, profile, input_chars)?;
            }
        }
        
        // Use adaptive controller to suggest additional configurations
        let suggested_configs = self.adaptive_controller.suggest_configurations(kernel_desc, input_chars)?;
        configs.extend(suggested_configs);
        
        Ok(configs)
    }
    
    /// Generate matrix multiplication specific configurations
    fn generate_matmul_configurations(
        &self,
        configs: &mut Vec<HashMap<String, usize>>,
        profile: &OptimizationProfile,
        input_chars: &InputCharacteristics,
    ) -> BackendResult<()> {
        let size = input_chars.size;
        let cache_factors = &profile.cache_blocking_factors;
        
        // Generate different blocking strategies
        for &l1_block in cache_factors.iter() {
            for &l2_block in cache_factors.iter() {
                if l2_block > l1_block {
                    for &thread_count in &[1, 2, 4, 8, 16] {
                        if thread_count <= num_cpus::get() {
                            let mut config = HashMap::new();
                            config.insert("block_m".to_string(), l1_block.min(size));
                            config.insert("block_n".to_string(), l1_block.min(size));
                            config.insert("block_k".to_string(), l2_block.min(size));
                            config.insert("thread_count".to_string(), thread_count);
                            config.insert("vector_width".to_string(), profile.preferred_vector_width);
                            configs.push(config);
                        }
                    }
                }
            }
        }
        
        Ok(())
    }
    
    /// Generate convolution specific configurations
    fn generate_conv_configurations(
        &self,
        configs: &mut Vec<HashMap<String, usize>>,
        profile: &OptimizationProfile,
        input_chars: &InputCharacteristics,
    ) -> BackendResult<()> {
        // Generate tile sizes based on input shape
        let base_tile_sizes = vec![4, 8, 16, 32];
        
        for &tile_h in &base_tile_sizes {
            for &tile_w in &base_tile_sizes {
                for &channel_unroll in &[1, 2, 4, 8, 16] {
                    for &thread_count in &[1, 2, 4, 8] {
                        if thread_count <= num_cpus::get() {
                            let mut config = HashMap::new();
                            config.insert("tile_height".to_string(), tile_h);
                            config.insert("tile_width".to_string(), tile_w);
                            config.insert("channel_unroll".to_string(), channel_unroll);
                            config.insert("thread_count".to_string(), thread_count);
                            config.insert("vector_width".to_string(), profile.preferred_vector_width);
                            configs.push(config);
                        }
                    }
                }
            }
        }
        
        Ok(())
    }
    
    /// Generate element-wise operation configurations
    fn generate_elementwise_configurations(
        &self,
        configs: &mut Vec<HashMap<String, usize>>,
        profile: &OptimizationProfile,
        input_chars: &InputCharacteristics,
    ) -> BackendResult<()> {
        let size = input_chars.size;
        let chunk_sizes = vec![64, 256, 1024, 4096, 16384];
        
        for &chunk_size in &chunk_sizes {
            for &thread_count in &[1, 2, 4, 8, 16] {
                if thread_count <= num_cpus::get() {
                    let mut config = HashMap::new();
                    config.insert("chunk_size".to_string(), chunk_size.min(size / thread_count + 1));
                    config.insert("thread_count".to_string(), thread_count);
                    config.insert("vector_width".to_string(), profile.preferred_vector_width);
                    config.insert("unroll_factor".to_string(), 4);
                    configs.push(config);
                }
            }
        }
        
        Ok(())
    }
    
    /// Generate default configurations for unknown operation types
    fn generate_default_configurations(
        &self,
        configs: &mut Vec<HashMap<String, usize>>,
        profile: &OptimizationProfile,
        _input_chars: &InputCharacteristics,
    ) -> BackendResult<()> {
        // Generate basic configurations
        for &thread_count in &[1, 2, 4, 8] {
            if thread_count <= num_cpus::get() {
                let mut config = HashMap::new();
                config.insert("thread_count".to_string(), thread_count);
                config.insert("vector_width".to_string(), profile.preferred_vector_width);
                configs.push(config);
            }
        }
        
        Ok(())
    }
    
    /// Evaluate a configuration's performance
    fn evaluate_configuration(
        &self,
        _kernel_desc: &KernelDescriptor,
        config: &HashMap<String, usize>,
        _input_chars: &InputCharacteristics,
        _environment: &EnvironmentConditions,
    ) -> BackendResult<TestedConfiguration> {
        // This would call into SciRS2's kernel execution system
        // For now, we'll simulate the evaluation
        
        let start_time = Instant::now();
        
        // Simulate kernel execution based on configuration
        let simulated_performance = self.simulate_kernel_execution(config)?;
        
        let execution_time = start_time.elapsed();
        
        Ok(TestedConfiguration {
            parameters: config.clone(),
            performance: PerformanceMeasurement {
                execution_time,
                throughput: simulated_performance.throughput,
                efficiency: simulated_performance.efficiency,
                cache_hit_ratio: simulated_performance.cache_hit_ratio,
            },
            thermal_impact: simulated_performance.thermal_impact,
            power_efficiency: simulated_performance.power_efficiency,
            stability_score: simulated_performance.stability_score,
        })
    }
    
    /// Simulate kernel execution for testing
    fn simulate_kernel_execution(&self, config: &HashMap<String, usize>) -> BackendResult<SimulatedPerformance> {
        // Simple simulation based on configuration parameters
        let thread_count = config.get("thread_count").unwrap_or(&1);
        let vector_width = config.get("vector_width").unwrap_or(&128);
        
        // Simulate performance characteristics
        let base_throughput = 1e6; // Base 1M ops/sec
        let thread_scaling = (*thread_count as f64).sqrt(); // Diminishing returns
        let vector_scaling = (*vector_width as f64) / 128.0; // Relative to 128-bit baseline
        
        let throughput = base_throughput * thread_scaling * vector_scaling;
        let efficiency = (thread_scaling / *thread_count as f64).min(1.0);
        let cache_hit_ratio = 0.95 - ((*thread_count as f64 - 1.0) * 0.05); // Decreases with threads
        
        Ok(SimulatedPerformance {
            throughput,
            efficiency,
            cache_hit_ratio: cache_hit_ratio.max(0.7),
            thermal_impact: (*thread_count as f32) * 0.1,
            power_efficiency: efficiency as f32 * 0.8,
            stability_score: 0.95,
        })
    }
    
    /// Check if one configuration is better than another
    fn is_better_configuration(&self, a: &TestedConfiguration, b: &TestedConfiguration) -> bool {
        a.performance.composite_score() > b.performance.composite_score()
    }
    
    /// Check if tuning has converged
    fn check_convergence(&self, session: &TuningSession) -> bool {
        if session.tested_configurations.len() < 5 {
            return false;
        }
        
        // Check if recent improvements are below threshold
        let recent_scores: Vec<f64> = session.tested_configurations
            .iter()
            .rev()
            .take(5)
            .map(|c| c.performance.composite_score())
            .collect();
        
        let max_score = recent_scores.iter().fold(0.0f64, |a, &b| a.max(b));
        let min_score = recent_scores.iter().fold(f64::INFINITY, |a, &b| a.min(b));
        
        (max_score - min_score) / max_score < session.convergence_threshold
    }
    
    /// Create TuningResult from session
    fn create_tuning_result(&self, session: &TuningSession, kernel_desc: &KernelDescriptor) -> BackendResult<TuningResult> {
        let best_config = session.best_configuration.as_ref()
            .ok_or_else(|| TorshError::BackendError("No best configuration found".to_string()))?;
        
        let config = TuningConfig {
            operation_name: kernel_desc.name.clone(),
            input_size_ranges: vec![(100, 1000000)], // Placeholder
            thread_counts: vec![best_config.parameters.get("thread_count").unwrap_or(&1).clone()],
            chunk_sizes: vec![best_config.parameters.get("chunk_size").unwrap_or(&1024).clone()],
            block_sizes: vec![best_config.parameters.get("block_size").unwrap_or(&64).clone()],
            iterations_per_test: 10,
            warmup_iterations: 3,
        };
        
        Ok(TuningResult {
            config,
            optimal_thread_count: best_config.parameters.get("thread_count").unwrap_or(&1).clone(),
            optimal_chunk_size: best_config.parameters.get("chunk_size").unwrap_or(&1024).clone(),
            optimal_block_size: best_config.parameters.get("block_size").cloned(),
            best_performance: best_config.performance,
            size_range: (100, 1000000), // Placeholder
        })
    }
    
    /// Record optimization event for learning
    fn record_optimization_event(
        &self,
        kernel_name: &str,
        input_chars: InputCharacteristics,
        tuning_result: &TuningResult,
        environment: EnvironmentConditions,
    ) -> BackendResult<()> {
        let event = OptimizationEvent {
            timestamp: Instant::now(),
            kernel_name: kernel_name.to_string(),
            input_characteristics: input_chars,
            optimization_result: OptimizationResult {
                speedup_achieved: 1.5, // Placeholder
                memory_efficiency_gain: 0.2,
                power_efficiency_gain: 0.1,
                thermal_impact_reduction: 0.05,
            },
            environment_conditions: environment,
        };
        
        let mut registry = self.kernel_registry.lock().map_err(|_| {
            TorshError::AllocationError("Failed to acquire kernel registry lock".to_string())
        })?;
        
        registry.optimization_history.push(event);
        
        // Update adaptive controller with new data
        self.adaptive_controller.update_model(tuning_result)?;
        
        Ok(())
    }
    
    /// Get performance statistics
    pub fn get_performance_stats(&self) -> BackendResult<GlobalPerformanceStats> {
        let tracker = self.performance_tracker.lock().map_err(|_| {
            TorshError::AllocationError("Failed to acquire performance tracker lock".to_string())
        })?;
        
        Ok(tracker.global_stats.clone())
    }
}

/// Simulated performance for testing
#[derive(Debug)]
struct SimulatedPerformance {
    throughput: f64,
    efficiency: f64,
    cache_hit_ratio: f64,
    thermal_impact: f32,
    power_efficiency: f32,
    stability_score: f32,
}

impl PerformanceTracker {
    fn new() -> Self {
        Self {
            operation_stats: HashMap::new(),
            global_stats: GlobalPerformanceStats::default(),
            thermal_history: Vec::new(),
            power_history: Vec::new(),
        }
    }
}

impl KernelRegistry {
    fn new() -> Self {
        Self {
            kernels: HashMap::new(),
            tuning_sessions: HashMap::new(),
            optimization_history: Vec::new(),
        }
    }
}

impl AdaptiveTuningController {
    fn new() -> Self {
        Self {
            learning_rate: 0.01,
            exploration_factor: 0.1,
            exploitation_threshold: 0.8,
            convergence_history: Vec::new(),
            prediction_model: PredictionModel {
                feature_weights: HashMap::new(),
                bias: 0.0,
                confidence_threshold: 0.7,
            },
        }
    }
    
    fn suggest_configurations(
        &self,
        _kernel_desc: &KernelDescriptor,
        _input_chars: &InputCharacteristics,
    ) -> BackendResult<Vec<HashMap<String, usize>>> {
        // Simple implementation - would use ML model in practice
        Ok(Vec::new())
    }
    
    fn update_model(&self, _tuning_result: &TuningResult) -> BackendResult<()> {
        // Update prediction model with new data
        Ok(())
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    
    #[test]
    fn test_autotuner_creation() {
        let tuner = SciRS2AutoTuner::new();
        assert!(tuner.is_ok());
    }
    
    #[test]
    fn test_optimization_profile_creation() {
        let profiles = SciRS2AutoTuner::initialize_optimization_profiles();
        assert!(profiles.is_ok());
        
        let profiles = profiles.expect("operation should succeed");
        assert!(profiles.contains_key("intel_haswell"));
        assert!(profiles.contains_key("apple_m1"));
    }
    
    #[test]
    fn test_kernel_registration() {
        let tuner = SciRS2AutoTuner::new().expect("Sci RS2Auto Tuner should succeed");
        
        let kernel_desc = KernelDescriptor {
            name: "test_kernel".to_string(),
            operation_type: OperationType::ElementWise,
            parameter_space: ParameterSpace {
                thread_counts: vec![1, 2, 4],
                block_sizes: vec![64, 128],
                vector_widths: vec![128, 256],
                unroll_factors: vec![2, 4],
                tile_sizes: vec![(8, 8), (16, 16)],
                scheduling_strategies: vec![SchedulingStrategy::Static],
            },
            constraints: vec![],
            scirs2_kernel_id: None,
        };
        
        let result = tuner.register_kernel(kernel_desc);
        assert!(result.is_ok());
    }
    
    #[test]
    fn test_cache_key_generation() {
        let tuner = SciRS2AutoTuner::new().expect("Sci RS2Auto Tuner should succeed");
        
        let input_chars = InputCharacteristics {
            size: 1000,
            shape: vec![100, 10],
            data_type: "f32".to_string(),
            access_pattern: "sequential".to_string(),
            locality_score: 0.8,
        };
        
        let environment = EnvironmentConditions {
            cpu_temperature: 65.0,
            memory_pressure: 0.7,
            system_load: 0.5,
            thermal_throttling: false,
        };
        
        let key = tuner.generate_cache_key("test_kernel", &input_chars, &environment);
        assert!(!key.is_empty());
        assert!(key.contains("test_kernel"));
        assert!(key.contains("1000"));
    }
    
    #[test]
    fn test_configuration_generation() {
        let tuner = SciRS2AutoTuner::new().expect("Sci RS2Auto Tuner should succeed");
        let profile = tuner.get_optimization_profile().expect("optimization profile retrieval should succeed");
        
        let input_chars = InputCharacteristics {
            size: 1024,
            shape: vec![32, 32],
            data_type: "f32".to_string(),
            access_pattern: "sequential".to_string(),
            locality_score: 0.9,
        };
        
        let mut configs = Vec::new();
        let result = tuner.generate_elementwise_configurations(&mut configs, &profile, &input_chars);
        
        assert!(result.is_ok());
        assert!(!configs.is_empty());
        
        // Check that configurations have required parameters
        for config in configs {
            assert!(config.contains_key("thread_count"));
            assert!(config.contains_key("chunk_size"));
            assert!(config.contains_key("vector_width"));
        }
    }
}