torsh-jit 0.1.3

//! Hardware-Specific Tuning for ToRSh JIT
//!
//! This module implements hardware detection and automatic tuning of compilation
//! strategies based on the target hardware architecture and capabilities.

use crate::adaptive_compilation::OptimizationLevel;
use crate::{CompilationStrategy, ComputationGraph, JitError, JitResult};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::{
    atomic::{AtomicBool, Ordering},
    Arc, RwLock,
};

/// Hardware-specific tuning manager
pub struct HardwareTuner {
    hardware_info: Arc<RwLock<HardwareInfo>>,
    tuning_profiles: Arc<RwLock<HashMap<String, TuningProfile>>>,
    auto_tuning_enabled: AtomicBool,
    config: HardwareTuningConfig,
}

/// Configuration for hardware tuning
#[derive(Debug, Clone)]
pub struct HardwareTuningConfig {
    /// Enable automatic hardware detection
    pub enable_auto_detection: bool,

    /// Enable architecture-specific optimizations
    pub enable_arch_optimizations: bool,

    /// Enable SIMD optimizations
    pub enable_simd_optimizations: bool,

    /// Enable cache-aware optimizations
    pub enable_cache_optimizations: bool,

    /// Enable power-aware optimizations
    pub enable_power_optimizations: bool,

    /// Enable thermal-aware optimizations
    pub enable_thermal_optimizations: bool,

    /// Tuning aggressiveness (0.0 to 1.0)
    pub tuning_aggressiveness: f64,

    /// Profile cache size
    pub profile_cache_size: usize,
}

impl Default for HardwareTuningConfig {
    fn default() -> Self {
        Self {
            enable_auto_detection: true,
            enable_arch_optimizations: true,
            enable_simd_optimizations: true,
            enable_cache_optimizations: true,
            enable_power_optimizations: true,
            enable_thermal_optimizations: false, // May be unstable
            tuning_aggressiveness: 0.7,
            profile_cache_size: 100,
        }
    }
}

/// Hardware information detected at runtime
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareInfo {
    pub cpu_info: CpuInfo,
    pub memory_info: MemoryInfo,
    pub cache_info: CacheInfo,
    pub simd_capabilities: SimdCapabilities,
    pub power_info: PowerInfo,
    pub thermal_info: ThermalInfo,
    pub architecture: Architecture,
}

/// CPU-specific information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CpuInfo {
    pub vendor: String,
    pub model: String,
    pub family: u32,
    pub model_number: u32,
    pub stepping: u32,
    pub cores: usize,
    pub logical_cores: usize,
    pub base_frequency: u64, // MHz
    pub max_frequency: u64,  // MHz
    pub features: Vec<String>,
}

/// Memory hierarchy information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryInfo {
    pub total_memory: usize,     // bytes
    pub available_memory: usize, // bytes
    pub memory_bandwidth: u64,   // MB/s
    pub memory_latency: u32,     // nanoseconds
    pub numa_nodes: usize,
}

/// Cache hierarchy information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheInfo {
    pub l1_instruction_cache: CacheLevel,
    pub l1_data_cache: CacheLevel,
    pub l2_cache: CacheLevel,
    pub l3_cache: Option<CacheLevel>,
    pub l4_cache: Option<CacheLevel>,
}

/// Individual cache level information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheLevel {
    pub size: usize, // bytes
    pub associativity: usize,
    pub line_size: usize, // bytes
    pub latency: u32,     // cycles
    pub shared: bool,
}

/// SIMD and vector capabilities
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SimdCapabilities {
    pub sse: bool,
    pub sse2: bool,
    pub sse3: bool,
    pub ssse3: bool,
    pub sse41: bool,
    pub sse42: bool,
    pub avx: bool,
    pub avx2: bool,
    pub avx512f: bool,
    pub avx512dq: bool,
    pub avx512vl: bool,
    pub avx512bw: bool,
    pub fma: bool,
    pub neon: bool,          // ARM NEON
    pub sve: bool,           // ARM SVE
    pub vector_width: usize, // bits
}

/// Power management information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PowerInfo {
    pub max_power: f64,         // watts
    pub current_power: f64,     // watts
    pub power_limit: f64,       // watts
    pub energy_efficiency: f64, // operations per joule
    pub battery_powered: bool,
    pub power_management_enabled: bool,
}

/// Thermal information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ThermalInfo {
    pub current_temperature: f64,  // celsius
    pub max_temperature: f64,      // celsius
    pub thermal_design_power: f64, // watts
    pub thermal_throttling: bool,
    pub cooling_solution: CoolingSolution,
}

/// Cooling solution type
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum CoolingSolution {
    Passive,
    ActiveAir,
    Liquid,
    Custom,
}

/// Target architecture
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum Architecture {
    X86_64,
    X86,
    Aarch64,
    Arm,
    Riscv64,
    Wasm32,
    Unknown,
}

/// Hardware-specific tuning profile
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TuningProfile {
    pub name: String,
    pub architecture: Architecture,
    pub optimization_hints: HashMap<String, String>,
    pub compilation_flags: Vec<String>,
    pub simd_preferences: SimdPreferences,
    pub cache_strategy: CacheStrategy,
    pub power_strategy: PowerStrategy,
    pub performance_characteristics: PerformanceCharacteristics,
}

/// SIMD optimization preferences
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SimdPreferences {
    pub preferred_width: usize,
    pub auto_vectorization: bool,
    pub manual_vectorization: bool,
    pub preferred_instructions: Vec<String>,
    pub alignment_requirements: usize,
}

/// Cache optimization strategy
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheStrategy {
    pub prefetch_strategy: PrefetchStrategy,
    pub blocking_factor: usize,
    pub cache_line_size: usize,
    pub working_set_optimization: bool,
    pub data_layout_optimization: bool,
}

/// Prefetching strategy
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum PrefetchStrategy {
    None,
    Conservative,
    Aggressive,
    Adaptive,
}

/// Power optimization strategy
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PowerStrategy {
    pub frequency_scaling: bool,
    pub core_parking: bool,
    pub voltage_scaling: bool,
    pub idle_optimization: bool,
    pub energy_efficiency_priority: f64, // 0.0 = performance, 1.0 = efficiency
}

/// Performance characteristics for the hardware
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceCharacteristics {
    pub integer_throughput: f64,          // operations per cycle
    pub float_throughput: f64,            // operations per cycle
    pub memory_bandwidth_efficiency: f64, // 0.0 to 1.0
    pub branch_prediction_accuracy: f64,  // 0.0 to 1.0
    pub cache_efficiency: f64,            // 0.0 to 1.0
    pub simd_efficiency: f64,             // 0.0 to 1.0
}

/// Hardware tuning recommendation
#[derive(Debug, Clone)]
pub struct TuningRecommendation {
    pub optimization_type: HardwareOptimizationType,
    pub confidence: f64,
    pub expected_improvement: f64,
    pub implementation_cost: f64,
    pub description: String,
    pub parameters: HashMap<String, String>,
}

/// Types of hardware-specific optimizations
#[derive(Debug, Clone, PartialEq)]
pub enum HardwareOptimizationType {
    /// SIMD vectorization optimization
    SimdVectorization,

    /// Cache-aware data layout
    CacheOptimization,

    /// Branch prediction optimization
    BranchOptimization,

    /// Memory prefetching
    MemoryPrefetching,

    /// Power-aware frequency scaling
    PowerOptimization,

    /// Thermal-aware throttling
    ThermalOptimization,

    /// Architecture-specific instruction selection
    InstructionSelection,

    /// Pipeline optimization
    PipelineOptimization,

    /// Register allocation optimization
    RegisterAllocation,

    /// Memory bandwidth optimization
    MemoryBandwidth,
}

impl HardwareTuner {
    /// Create a new hardware tuner
    pub fn new(config: HardwareTuningConfig) -> JitResult<Self> {
        let hardware_info = Self::detect_hardware()?;
        let tuning_profiles = Self::initialize_profiles(&hardware_info)?;

        Ok(Self {
            hardware_info: Arc::new(RwLock::new(hardware_info)),
            tuning_profiles: Arc::new(RwLock::new(tuning_profiles)),
            auto_tuning_enabled: AtomicBool::new(config.enable_auto_detection),
            config,
        })
    }

    /// Detect hardware capabilities
    pub fn detect_hardware() -> JitResult<HardwareInfo> {
        let cpu_info = Self::detect_cpu_info()?;
        let memory_info = Self::detect_memory_info()?;
        let cache_info = Self::detect_cache_info()?;
        let simd_capabilities = Self::detect_simd_capabilities()?;
        let power_info = Self::detect_power_info()?;
        let thermal_info = Self::detect_thermal_info()?;
        let architecture = Self::detect_architecture()?;

        Ok(HardwareInfo {
            cpu_info,
            memory_info,
            cache_info,
            simd_capabilities,
            power_info,
            thermal_info,
            architecture,
        })
    }

    /// Generate hardware-specific tuning recommendations
    pub fn generate_tuning_recommendations(
        &self,
        graph: &ComputationGraph,
    ) -> JitResult<Vec<TuningRecommendation>> {
        let hardware = self
            .hardware_info
            .read()
            .map_err(|_| JitError::RuntimeError("Failed to read hardware info".to_string()))?;

        let mut recommendations = Vec::new();

        // SIMD vectorization analysis
        if self.config.enable_simd_optimizations {
            recommendations.extend(self.analyze_simd_opportunities(graph, &hardware)?);
        }

        // Cache optimization analysis
        if self.config.enable_cache_optimizations {
            recommendations.extend(self.analyze_cache_opportunities(graph, &hardware)?);
        }

        // Architecture-specific optimizations
        if self.config.enable_arch_optimizations {
            recommendations.extend(self.analyze_architecture_opportunities(graph, &hardware)?);
        }

        // Power optimization analysis
        if self.config.enable_power_optimizations {
            recommendations.extend(self.analyze_power_opportunities(graph, &hardware)?);
        }

        // Thermal optimization analysis
        if self.config.enable_thermal_optimizations {
            recommendations.extend(self.analyze_thermal_opportunities(graph, &hardware)?);
        }

        // Sort by expected improvement
        recommendations.sort_by(|a, b| {
            b.expected_improvement
                .partial_cmp(&a.expected_improvement)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        Ok(recommendations)
    }

    /// Apply hardware-specific optimizations to compilation strategy
    pub fn apply_hardware_optimizations(
        &self,
        strategy: &mut CompilationStrategy,
        recommendations: &[TuningRecommendation],
    ) -> JitResult<usize> {
        let mut applied_count = 0;

        for recommendation in recommendations {
            if recommendation.confidence < 0.6 {
                continue; // Skip low-confidence recommendations
            }

            match recommendation.optimization_type {
                HardwareOptimizationType::SimdVectorization => {
                    if self.apply_simd_optimization(strategy, recommendation)? {
                        applied_count += 1;
                    }
                }
                HardwareOptimizationType::CacheOptimization => {
                    if self.apply_cache_optimization(strategy, recommendation)? {
                        applied_count += 1;
                    }
                }
                HardwareOptimizationType::PowerOptimization => {
                    if self.apply_power_optimization(strategy, recommendation)? {
                        applied_count += 1;
                    }
                }
                HardwareOptimizationType::InstructionSelection => {
                    if self.apply_instruction_selection(strategy, recommendation)? {
                        applied_count += 1;
                    }
                }
                _ => {
                    // Other optimizations can be implemented as needed
                }
            }
        }

        Ok(applied_count)
    }

    /// Get current hardware information
    pub fn get_hardware_info(&self) -> JitResult<HardwareInfo> {
        let hardware = self
            .hardware_info
            .read()
            .map_err(|_| JitError::RuntimeError("Failed to read hardware info".to_string()))?;
        Ok(hardware.clone())
    }

    /// Update hardware information (for dynamic detection)
    pub fn update_hardware_info(&self) -> JitResult<()> {
        if self.auto_tuning_enabled.load(Ordering::Relaxed) {
            let new_hardware_info = Self::detect_hardware()?;

            if let Ok(mut hardware) = self.hardware_info.write() {
                *hardware = new_hardware_info;
            }
        }

        Ok(())
    }

    // Hardware detection methods
    fn detect_cpu_info() -> JitResult<CpuInfo> {
        // Use raw_cpuid or similar crate for detailed CPU detection
        Ok(CpuInfo {
            vendor: std::env::consts::ARCH.to_string(),
            model: "Generic".to_string(),
            family: 0,
            model_number: 0,
            stepping: 0,
            cores: num_cpus::get_physical(),
            logical_cores: num_cpus::get(),
            base_frequency: 2400, // MHz placeholder
            max_frequency: 3600,  // MHz placeholder
            features: Self::detect_cpu_features(),
        })
    }

    fn detect_cpu_features() -> Vec<String> {
        #[cfg_attr(not(target_arch = "x86_64"), allow(unused_mut))]
        let mut features = Vec::new();

        #[cfg(target_arch = "x86_64")]
        {
            if is_x86_feature_detected!("sse") {
                features.push("sse".to_string());
            }
            if is_x86_feature_detected!("sse2") {
                features.push("sse2".to_string());
            }
            if is_x86_feature_detected!("sse3") {
                features.push("sse3".to_string());
            }
            if is_x86_feature_detected!("ssse3") {
                features.push("ssse3".to_string());
            }
            if is_x86_feature_detected!("sse4.1") {
                features.push("sse4.1".to_string());
            }
            if is_x86_feature_detected!("sse4.2") {
                features.push("sse4.2".to_string());
            }
            if is_x86_feature_detected!("avx") {
                features.push("avx".to_string());
            }
            if is_x86_feature_detected!("avx2") {
                features.push("avx2".to_string());
            }
            if is_x86_feature_detected!("fma") {
                features.push("fma".to_string());
            }
        }

        features
    }

    fn detect_memory_info() -> JitResult<MemoryInfo> {
        // Placeholder implementation
        Ok(MemoryInfo {
            total_memory: 16 * 1024 * 1024 * 1024,    // 16GB
            available_memory: 8 * 1024 * 1024 * 1024, // 8GB
            memory_bandwidth: 25600,                  // 25.6 GB/s
            memory_latency: 100,                      // 100ns
            numa_nodes: 1,
        })
    }

    fn detect_cache_info() -> JitResult<CacheInfo> {
        // Placeholder implementation - would use cpuid or /proc/cpuinfo on Linux
        Ok(CacheInfo {
            l1_instruction_cache: CacheLevel {
                size: 32 * 1024, // 32KB
                associativity: 8,
                line_size: 64,
                latency: 4,
                shared: false,
            },
            l1_data_cache: CacheLevel {
                size: 32 * 1024, // 32KB
                associativity: 8,
                line_size: 64,
                latency: 4,
                shared: false,
            },
            l2_cache: CacheLevel {
                size: 256 * 1024, // 256KB
                associativity: 8,
                line_size: 64,
                latency: 12,
                shared: false,
            },
            l3_cache: Some(CacheLevel {
                size: 8 * 1024 * 1024, // 8MB
                associativity: 16,
                line_size: 64,
                latency: 40,
                shared: true,
            }),
            l4_cache: None,
        })
    }

    fn detect_simd_capabilities() -> JitResult<SimdCapabilities> {
        let mut capabilities = SimdCapabilities {
            sse: false,
            sse2: false,
            sse3: false,
            ssse3: false,
            sse41: false,
            sse42: false,
            avx: false,
            avx2: false,
            avx512f: false,
            avx512dq: false,
            avx512vl: false,
            avx512bw: false,
            fma: false,
            neon: false,
            sve: false,
            vector_width: 128, // Default to 128-bit
        };

        #[cfg(target_arch = "x86_64")]
        {
            capabilities.sse = is_x86_feature_detected!("sse");
            capabilities.sse2 = is_x86_feature_detected!("sse2");
            capabilities.sse3 = is_x86_feature_detected!("sse3");
            capabilities.ssse3 = is_x86_feature_detected!("ssse3");
            capabilities.sse41 = is_x86_feature_detected!("sse4.1");
            capabilities.sse42 = is_x86_feature_detected!("sse4.2");
            capabilities.avx = is_x86_feature_detected!("avx");
            capabilities.avx2 = is_x86_feature_detected!("avx2");
            capabilities.fma = is_x86_feature_detected!("fma");

            // Determine vector width based on capabilities
            if capabilities.avx2 {
                capabilities.vector_width = 256;
            } else if capabilities.avx {
                capabilities.vector_width = 256;
            } else if capabilities.sse2 {
                capabilities.vector_width = 128;
            }
        }

        #[cfg(target_arch = "aarch64")]
        {
            capabilities.neon = true; // NEON is standard on AArch64
            capabilities.vector_width = 128;
        }

        Ok(capabilities)
    }

    fn detect_power_info() -> JitResult<PowerInfo> {
        // Placeholder implementation
        Ok(PowerInfo {
            max_power: 95.0,          // 95W TDP
            current_power: 35.0,      // 35W current
            power_limit: 95.0,        // 95W limit
            energy_efficiency: 100.0, // 100 ops/joule
            battery_powered: false,
            power_management_enabled: true,
        })
    }

    fn detect_thermal_info() -> JitResult<ThermalInfo> {
        // Placeholder implementation
        Ok(ThermalInfo {
            current_temperature: 45.0,  // 45°C
            max_temperature: 85.0,      // 85°C max
            thermal_design_power: 95.0, // 95W TDP
            thermal_throttling: false,
            cooling_solution: CoolingSolution::ActiveAir,
        })
    }

    fn detect_architecture() -> JitResult<Architecture> {
        match std::env::consts::ARCH {
            "x86_64" => Ok(Architecture::X86_64),
            "x86" => Ok(Architecture::X86),
            "aarch64" => Ok(Architecture::Aarch64),
            "arm" => Ok(Architecture::Arm),
            "riscv64" => Ok(Architecture::Riscv64),
            "wasm32" => Ok(Architecture::Wasm32),
            _ => Ok(Architecture::Unknown),
        }
    }

    fn initialize_profiles(hardware: &HardwareInfo) -> JitResult<HashMap<String, TuningProfile>> {
        let mut profiles = HashMap::new();

        // Create architecture-specific profile
        let arch_profile = Self::create_architecture_profile(hardware)?;
        profiles.insert(hardware.architecture.to_string(), arch_profile);

        // Create SIMD-specific profiles
        if hardware.simd_capabilities.avx2 {
            let avx2_profile = Self::create_avx2_profile(hardware)?;
            profiles.insert("avx2".to_string(), avx2_profile);
        }

        if hardware.simd_capabilities.avx {
            let avx_profile = Self::create_avx_profile(hardware)?;
            profiles.insert("avx".to_string(), avx_profile);
        }

        Ok(profiles)
    }

    fn create_architecture_profile(hardware: &HardwareInfo) -> JitResult<TuningProfile> {
        let mut optimization_hints = HashMap::new();
        let mut compilation_flags = Vec::new();

        match hardware.architecture {
            Architecture::X86_64 => {
                optimization_hints.insert("target_arch".to_string(), "x86_64".to_string());
                compilation_flags.push("-march=native".to_string());
                compilation_flags.push("-mtune=native".to_string());
            }
            Architecture::Aarch64 => {
                optimization_hints.insert("target_arch".to_string(), "aarch64".to_string());
                compilation_flags.push("-march=native".to_string());
            }
            _ => {}
        }

        Ok(TuningProfile {
            name: format!("{:?}_default", hardware.architecture),
            architecture: hardware.architecture.clone(),
            optimization_hints,
            compilation_flags,
            simd_preferences: SimdPreferences {
                preferred_width: hardware.simd_capabilities.vector_width,
                auto_vectorization: true,
                manual_vectorization: false,
                preferred_instructions: Vec::new(),
                alignment_requirements: 16,
            },
            cache_strategy: CacheStrategy {
                prefetch_strategy: PrefetchStrategy::Conservative,
                blocking_factor: hardware.cache_info.l1_data_cache.size / 4,
                cache_line_size: hardware.cache_info.l1_data_cache.line_size,
                working_set_optimization: true,
                data_layout_optimization: true,
            },
            power_strategy: PowerStrategy {
                frequency_scaling: hardware.power_info.power_management_enabled,
                core_parking: false,
                voltage_scaling: false,
                idle_optimization: true,
                energy_efficiency_priority: 0.3, // Favor performance
            },
            performance_characteristics: PerformanceCharacteristics {
                integer_throughput: 2.0,
                float_throughput: 1.5,
                memory_bandwidth_efficiency: 0.7,
                branch_prediction_accuracy: 0.95,
                cache_efficiency: 0.8,
                simd_efficiency: 0.6,
            },
        })
    }

    fn create_avx2_profile(hardware: &HardwareInfo) -> JitResult<TuningProfile> {
        let mut base_profile = Self::create_architecture_profile(hardware)?;

        base_profile.name = "avx2_optimized".to_string();
        base_profile.compilation_flags.push("-mavx2".to_string());
        base_profile.compilation_flags.push("-mfma".to_string());

        base_profile.simd_preferences.preferred_width = 256;
        base_profile.simd_preferences.auto_vectorization = true;
        base_profile.simd_preferences.preferred_instructions = vec![
            "vmulpd".to_string(),
            "vaddpd".to_string(),
            "vfmadd231pd".to_string(),
        ];
        base_profile.simd_preferences.alignment_requirements = 32;

        base_profile.performance_characteristics.simd_efficiency = 0.9;

        Ok(base_profile)
    }

    fn create_avx_profile(hardware: &HardwareInfo) -> JitResult<TuningProfile> {
        let mut base_profile = Self::create_architecture_profile(hardware)?;

        base_profile.name = "avx_optimized".to_string();
        base_profile.compilation_flags.push("-mavx".to_string());

        base_profile.simd_preferences.preferred_width = 256;
        base_profile.simd_preferences.alignment_requirements = 32;

        base_profile.performance_characteristics.simd_efficiency = 0.8;

        Ok(base_profile)
    }

    // Analysis methods for generating recommendations
    fn analyze_simd_opportunities(
        &self,
        graph: &ComputationGraph,
        hardware: &HardwareInfo,
    ) -> JitResult<Vec<TuningRecommendation>> {
        let mut recommendations = Vec::new();

        for (node_id, node) in graph.nodes() {
            if node.is_vectorizable() && hardware.simd_capabilities.avx2 {
                recommendations.push(TuningRecommendation {
                    optimization_type: HardwareOptimizationType::SimdVectorization,
                    confidence: 0.8,
                    expected_improvement: 0.3, // 30% improvement with AVX2
                    implementation_cost: 0.2,
                    description: format!("Vectorize node {} with AVX2", node_id.index()),
                    parameters: [
                        ("vector_width".to_string(), "256".to_string()),
                        ("instruction_set".to_string(), "avx2".to_string()),
                    ]
                    .into(),
                });
            }
        }

        Ok(recommendations)
    }

    fn analyze_cache_opportunities(
        &self,
        graph: &ComputationGraph,
        hardware: &HardwareInfo,
    ) -> JitResult<Vec<TuningRecommendation>> {
        let mut recommendations = Vec::new();

        // Analyze memory access patterns for cache optimization
        for (node_id, node) in graph.nodes() {
            if node.has_memory_access() {
                let working_set_size = node.estimate_working_set_size();
                let l3_cache_size = hardware
                    .cache_info
                    .l3_cache
                    .as_ref()
                    .map(|c| c.size)
                    .unwrap_or(0);

                if working_set_size > l3_cache_size {
                    recommendations.push(TuningRecommendation {
                        optimization_type: HardwareOptimizationType::CacheOptimization,
                        confidence: 0.7,
                        expected_improvement: 0.15, // 15% improvement
                        implementation_cost: 0.3,
                        description: format!("Cache-blocking for node {}", node_id.index()),
                        parameters: [
                            ("block_size".to_string(), (l3_cache_size / 2).to_string()),
                            (
                                "cache_line_size".to_string(),
                                hardware.cache_info.l1_data_cache.line_size.to_string(),
                            ),
                        ]
                        .into(),
                    });
                }
            }
        }

        Ok(recommendations)
    }

    fn analyze_architecture_opportunities(
        &self,
        _graph: &ComputationGraph,
        hardware: &HardwareInfo,
    ) -> JitResult<Vec<TuningRecommendation>> {
        let mut recommendations = Vec::new();

        // Architecture-specific instruction selection
        match hardware.architecture {
            Architecture::X86_64 => {
                if hardware.simd_capabilities.fma {
                    recommendations.push(TuningRecommendation {
                        optimization_type: HardwareOptimizationType::InstructionSelection,
                        confidence: 0.9,
                        expected_improvement: 0.1, // 10% improvement with FMA
                        implementation_cost: 0.1,
                        description: "Use FMA instructions for multiply-add operations".to_string(),
                        parameters: [("use_fma".to_string(), "true".to_string())].into(),
                    });
                }
            }
            _ => {}
        }

        Ok(recommendations)
    }

    fn analyze_power_opportunities(
        &self,
        _graph: &ComputationGraph,
        hardware: &HardwareInfo,
    ) -> JitResult<Vec<TuningRecommendation>> {
        let mut recommendations = Vec::new();

        // Power-aware optimizations
        if hardware.power_info.battery_powered {
            recommendations.push(TuningRecommendation {
                optimization_type: HardwareOptimizationType::PowerOptimization,
                confidence: 0.6,
                expected_improvement: 0.05, // 5% power savings
                implementation_cost: 0.1,
                description: "Enable power-efficient compilation for battery operation".to_string(),
                parameters: [
                    ("optimize_for_power".to_string(), "true".to_string()),
                    ("frequency_scaling".to_string(), "enabled".to_string()),
                ]
                .into(),
            });
        }

        Ok(recommendations)
    }

    fn analyze_thermal_opportunities(
        &self,
        _graph: &ComputationGraph,
        hardware: &HardwareInfo,
    ) -> JitResult<Vec<TuningRecommendation>> {
        let mut recommendations = Vec::new();

        // Thermal-aware optimizations
        if hardware.thermal_info.thermal_throttling {
            recommendations.push(TuningRecommendation {
                optimization_type: HardwareOptimizationType::ThermalOptimization,
                confidence: 0.7,
                expected_improvement: 0.08, // 8% improvement by avoiding throttling
                implementation_cost: 0.2,
                description: "Reduce computational intensity to avoid thermal throttling"
                    .to_string(),
                parameters: [
                    ("thermal_aware".to_string(), "true".to_string()),
                    (
                        "max_temperature".to_string(),
                        hardware.thermal_info.max_temperature.to_string(),
                    ),
                ]
                .into(),
            });
        }

        Ok(recommendations)
    }

    // Optimization application methods
    fn apply_simd_optimization(
        &self,
        strategy: &mut CompilationStrategy,
        recommendation: &TuningRecommendation,
    ) -> JitResult<bool> {
        if let Some(vector_width) = recommendation.parameters.get("vector_width") {
            strategy
                .compilation_flags
                .custom_flags
                .push(format!("-mvector-width={}", vector_width));
        }

        if let Some(instruction_set) = recommendation.parameters.get("instruction_set") {
            strategy
                .compilation_flags
                .custom_flags
                .push(format!("-m{}", instruction_set));
        }

        strategy.compilation_flags.enable_vectorization = true;

        Ok(true)
    }

    fn apply_cache_optimization(
        &self,
        strategy: &mut CompilationStrategy,
        recommendation: &TuningRecommendation,
    ) -> JitResult<bool> {
        if let Some(block_size) = recommendation.parameters.get("block_size") {
            strategy
                .compilation_flags
                .custom_flags
                .push(format!("-fcache-block-size={}", block_size));
        }

        if let Some(cache_line_size) = recommendation.parameters.get("cache_line_size") {
            strategy
                .compilation_flags
                .custom_flags
                .push(format!("-fcache-line-size={}", cache_line_size));
        }

        Ok(true)
    }

    fn apply_power_optimization(
        &self,
        strategy: &mut CompilationStrategy,
        _recommendation: &TuningRecommendation,
    ) -> JitResult<bool> {
        // Adjust optimization level for power efficiency
        strategy.optimization_level = OptimizationLevel::Size; // Optimize for size/power
        strategy
            .compilation_flags
            .custom_flags
            .push("-fpower-efficient".to_string());

        Ok(true)
    }

    fn apply_instruction_selection(
        &self,
        strategy: &mut CompilationStrategy,
        recommendation: &TuningRecommendation,
    ) -> JitResult<bool> {
        if recommendation.parameters.get("use_fma") == Some(&"true".to_string()) {
            strategy
                .compilation_flags
                .custom_flags
                .push("-mfma".to_string());
        }

        Ok(true)
    }
}

impl std::fmt::Display for Architecture {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            Architecture::X86_64 => write!(f, "x86_64"),
            Architecture::X86 => write!(f, "x86"),
            Architecture::Aarch64 => write!(f, "aarch64"),
            Architecture::Arm => write!(f, "arm"),
            Architecture::Riscv64 => write!(f, "riscv64"),
            Architecture::Wasm32 => write!(f, "wasm32"),
            Architecture::Unknown => write!(f, "unknown"),
        }
    }
}

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

    #[test]
    fn test_hardware_detection() {
        let hardware_info = HardwareTuner::detect_hardware().unwrap();
        assert!(hardware_info.cpu_info.cores > 0);
        assert!(hardware_info.cpu_info.logical_cores > 0);
        // Note: logical_cores is typically >= cores with hyperthreading,
        // but detection may vary across systems
    }

    #[test]
    fn test_simd_detection() {
        let simd_caps = HardwareTuner::detect_simd_capabilities().unwrap();
        assert!(simd_caps.vector_width >= 128);
    }

    #[test]
    fn test_architecture_detection() {
        let arch = HardwareTuner::detect_architecture().unwrap();
        assert_ne!(arch, Architecture::Unknown);
    }

    #[test]
    fn test_tuning_profile_creation() {
        let hardware_info = HardwareTuner::detect_hardware().unwrap();
        let profile = HardwareTuner::create_architecture_profile(&hardware_info).unwrap();
        assert_eq!(profile.architecture, hardware_info.architecture);
        assert!(!profile.compilation_flags.is_empty());
    }

    #[test]
    fn test_hardware_tuner_creation() {
        let config = HardwareTuningConfig::default();
        let tuner = HardwareTuner::new(config).unwrap();
        let hardware_info = tuner.get_hardware_info().unwrap();
        assert!(hardware_info.cpu_info.cores > 0);
    }
}