trustformers_debug/

kernel_optimizer.rs

//! Kernel optimization analyzer and recommendation engine
//!
//! This module provides comprehensive analysis of GPU kernel performance,
//! identifies optimization opportunities, and suggests specific improvements.
// reason: debug/profiling scaffolding — structs are constructed and their fields/methods
// are retained for the data model, serialization completeness, and future consumers that
// do not yet read every member. Consolidated from many item-level #[allow(dead_code)].
#![allow(dead_code)]

use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::time::{Duration, SystemTime};
use uuid::Uuid;

use crate::advanced_gpu_profiler::{
    AccessLocalityMetrics, CachePerformanceAnalysis, CoalescingAnalysis, ComputeBottleneckAnalysis,
    ComputeBottleneckType, ComputeUtilizationProfile, ConfigPerformanceMeasurement,
    ImplementationDifficulty, InstructionMixAnalysis, KernelExecutionProfile, KernelOptimization,
    MemoryAccessAnalysis, OptimalLaunchConfig, ResourceUtilizationMetrics,
};

/// CPU-side analytical computations backing the analyzers in this module
/// (occupancy estimation, roofline classification, fusion detection).
mod analysis;

/// Comprehensive kernel optimization analyzer
#[derive(Debug)]
pub struct KernelOptimizationAnalyzer {
    kernel_profiles: HashMap<String, KernelExecutionProfile>,
    optimization_suggestions: HashMap<String, Vec<KernelOptimization>>,
    launch_config_analyzer: LaunchConfigAnalyzer,
    memory_access_analyzer: MemoryAccessAnalyzer,
    compute_utilization_analyzer: ComputeUtilizationAnalyzer,
    fusion_analyzer: KernelFusionAnalyzer,
    performance_regression_detector: PerformanceRegressionDetector,
}

/// Launch configuration optimization engine
#[derive(Debug)]
pub struct LaunchConfigAnalyzer {
    optimal_configs: HashMap<String, OptimalLaunchConfig>,
    config_performance_history: HashMap<String, Vec<ConfigPerformanceMeasurement>>,
    autotuning_enabled: bool,
    search_space_cache: HashMap<String, LaunchConfigSearchSpace>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LaunchConfigSearchSpace {
    pub kernel_name: String,
    pub min_block_size: (u32, u32, u32),
    pub max_block_size: (u32, u32, u32),
    pub block_size_constraints: Vec<BlockSizeConstraint>,
    pub shared_memory_constraints: MemoryConstraints,
    pub register_constraints: RegisterConstraints,
    pub occupancy_targets: OccupancyTargets,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum BlockSizeConstraint {
    MultipleOf(u32),
    PowerOfTwo,
    MaxThreadsPerBlock(u32),
    SharedMemoryLimit(usize),
    RegisterLimit(u32),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryConstraints {
    pub max_shared_memory_per_block: usize,
    pub bank_conflict_aware: bool,
    pub coalescing_optimization: bool,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RegisterConstraints {
    pub max_registers_per_thread: u32,
    pub spill_threshold: u32,
    pub occupancy_impact_threshold: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OccupancyTargets {
    pub minimum_occupancy: f64,
    pub target_occupancy: f64,
    pub theoretical_occupancy: f64,
}

/// Memory access pattern analysis engine
#[derive(Debug)]
pub struct MemoryAccessAnalyzer {
    access_patterns: HashMap<String, MemoryAccessAnalysis>,
    coalescing_analysis: HashMap<String, CoalescingAnalysis>,
    cache_performance: HashMap<String, CachePerformanceAnalysis>,
    stride_analysis: HashMap<String, StrideAnalysisResult>,
    bank_conflict_analyzer: BankConflictAnalyzer,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StrideAnalysisResult {
    pub kernel_name: String,
    pub detected_strides: Vec<DetectedStride>,
    pub access_pattern_classification: AccessPatternType,
    pub optimization_potential: f64,
    pub recommended_optimizations: Vec<StrideOptimization>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DetectedStride {
    pub stride_bytes: usize,
    pub frequency: u64,
    pub memory_region: String,
    pub performance_impact: StrideImpact,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum StrideImpact {
    Optimal,  // Stride = 1 element
    Good,     // Small stride, good cache utilization
    Moderate, // Medium stride, some cache misses
    Poor,     // Large stride, many cache misses
    Critical, // Very large stride, severe performance impact
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AccessPatternType {
    Sequential,
    Strided,
    Random,
    Blocked,
    Sparse,
    Irregular,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StrideOptimization {
    pub optimization_type: StrideOptimizationType,
    pub description: String,
    pub expected_improvement: f64,
    pub implementation_complexity: ImplementationDifficulty,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum StrideOptimizationType {
    DataLayoutReorganization,
    AccessReordering,
    TilingStrategy,
    PrefetchingStrategy,
    VectorizedAccess,
}

/// Bank conflict analysis for shared memory
#[derive(Debug)]
pub struct BankConflictAnalyzer {
    conflict_patterns: HashMap<String, BankConflictPattern>,
    resolution_strategies: HashMap<String, Vec<ConflictResolutionStrategy>>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BankConflictPattern {
    pub kernel_name: String,
    pub conflict_count: u64,
    pub conflict_severity: ConflictSeverity,
    pub conflicting_addresses: Vec<ConflictingAccess>,
    pub bank_utilization: Vec<f64>, // Utilization per bank
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConflictSeverity {
    None,
    Low,    // 2-way conflicts
    Medium, // 4-way conflicts
    High,   // 8-way conflicts
    Severe, // 16+ way conflicts
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConflictingAccess {
    pub address_pattern: String,
    pub conflict_degree: u32,
    pub access_frequency: u64,
    pub performance_penalty: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConflictResolutionStrategy {
    pub strategy_type: ConflictResolutionType,
    pub description: String,
    pub expected_speedup: f64,
    pub implementation_steps: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConflictResolutionType {
    ArrayPadding,
    AccessReordering,
    DataStructureReorganization,
    BroadcastOptimization,
    MemoryLayoutChange,
}

/// Compute utilization analysis engine
#[derive(Debug)]
pub struct ComputeUtilizationAnalyzer {
    utilization_profiles: HashMap<String, ComputeUtilizationProfile>,
    bottleneck_analysis: HashMap<String, ComputeBottleneckAnalysis>,
    arithmetic_intensity_analyzer: ArithmeticIntensityAnalyzer,
    resource_balancer: ResourceBalancer,
}

#[derive(Debug)]
pub struct ArithmeticIntensityAnalyzer {
    intensity_profiles: HashMap<String, ArithmeticIntensityProfile>,
    roofline_models: HashMap<i32, RooflineModel>, // Per device
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ArithmeticIntensityProfile {
    pub kernel_name: String,
    pub operations_per_byte: f64,
    pub compute_intensity: ComputeIntensityCategory,
    pub memory_bound_ratio: f64,
    pub compute_bound_ratio: f64,
    pub roofline_position: RooflinePosition,
    pub optimization_direction: OptimizationDirection,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ComputeIntensityCategory {
    MemoryBound,  // < 1 op/byte
    Balanced,     // 1-10 ops/byte
    ComputeBound, // > 10 ops/byte
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RooflinePosition {
    pub current_performance: f64,    // GFLOPS
    pub theoretical_peak: f64,       // GFLOPS
    pub memory_bandwidth_limit: f64, // GB/s
    pub efficiency_percentage: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationDirection {
    IncreaseComputeIntensity,
    ImproveMemoryEfficiency,
    BalanceComputeMemory,
    OptimizeForLatency,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RooflineModel {
    pub device_id: i32,
    pub peak_compute_performance: f64, // GFLOPS
    pub peak_memory_bandwidth: f64,    // GB/s
    pub cache_hierarchy: CacheHierarchy,
    pub compute_capabilities: ComputeCapabilities,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheHierarchy {
    pub l1_cache_bandwidth: f64,
    pub l2_cache_bandwidth: f64,
    pub shared_memory_bandwidth: f64,
    pub texture_cache_bandwidth: f64,
    pub constant_cache_bandwidth: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComputeCapabilities {
    pub fp32_performance: f64,
    pub fp16_performance: f64,
    pub int32_performance: f64,
    pub tensor_performance: f64,
    pub special_function_performance: f64,
}

/// Resource balancing engine
#[derive(Debug)]
pub struct ResourceBalancer {
    resource_profiles: HashMap<String, ResourceProfile>,
    balancing_strategies: HashMap<String, Vec<BalancingStrategy>>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceProfile {
    pub kernel_name: String,
    pub register_pressure: ResourcePressure,
    pub shared_memory_pressure: ResourcePressure,
    pub occupancy_limiting_factor: OccupancyLimitingFactor,
    pub resource_utilization_efficiency: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ResourcePressure {
    Low,
    Medium,
    High,
    Critical,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OccupancyLimitingFactor {
    RegisterCount,
    SharedMemoryUsage,
    BlockSize,
    WarpCount,
    None,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BalancingStrategy {
    pub strategy_type: BalancingStrategyType,
    pub description: String,
    pub expected_occupancy_improvement: f64,
    pub performance_impact: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum BalancingStrategyType {
    RegisterOptimization,
    SharedMemoryOptimization,
    BlockSizeAdjustment,
    WorkDistributionOptimization,
    ResourcePartitioning,
}

/// Kernel fusion analysis engine
#[derive(Debug)]
pub struct KernelFusionAnalyzer {
    fusion_opportunities: HashMap<String, Vec<FusionOpportunity>>,
    dependency_graph: KernelDependencyGraph,
    fusion_templates: Vec<FusionTemplate>,
    cost_benefit_analyzer: FusionCostBenefitAnalyzer,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FusionOpportunity {
    pub opportunity_id: Uuid,
    pub kernel_group: Vec<String>,
    pub fusion_type: FusionType,
    pub data_dependencies: Vec<DataDependency>,
    pub expected_speedup: f64,
    pub memory_savings: usize,
    pub implementation_complexity: ImplementationDifficulty,
    pub fusion_feasibility: FusionFeasibility,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum FusionType {
    ElementwiseFusion,      // Simple element-wise operations
    ProducerConsumerFusion, // Producer directly feeds consumer
    LoopFusion,             // Fuse similar loop structures
    ReductionFusion,        // Combine multiple reductions
    ConvolutionFusion,      // Fuse convolution with activation/bias
    AttentionFusion,        // Fuse attention mechanism components
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DataDependency {
    pub source_kernel: String,
    pub target_kernel: String,
    pub dependency_type: DependencyType,
    pub data_size: usize,
    pub access_pattern: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DependencyType {
    ReadAfterWrite,
    WriteAfterRead,
    WriteAfterWrite,
    Reduction,
    Broadcast,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FusionFeasibility {
    pub resource_constraints_satisfied: bool,
    pub register_usage_feasible: bool,
    pub shared_memory_feasible: bool,
    pub synchronization_complexity: SynchronizationComplexity,
    pub fusion_confidence: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SynchronizationComplexity {
    None,
    Minimal,
    Moderate,
    Complex,
    Prohibitive,
}

#[derive(Debug)]
pub struct KernelDependencyGraph {
    nodes: HashMap<String, KernelNode>,
    edges: Vec<DependencyEdge>,
    fusion_clusters: Vec<FusionCluster>,
}

#[derive(Debug, Clone)]
pub struct KernelNode {
    pub kernel_name: String,
    pub execution_time: Duration,
    pub memory_footprint: usize,
    pub resource_requirements: ResourceRequirements,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceRequirements {
    pub registers_per_thread: u32,
    pub shared_memory_per_block: usize,
    pub max_threads_per_block: u32,
    pub memory_bandwidth_required: f64,
}

#[derive(Debug, Clone)]
pub struct DependencyEdge {
    pub source: String,
    pub target: String,
    pub dependency: DataDependency,
    pub weight: f64, // Strength of dependency
}

#[derive(Debug, Clone)]
pub struct FusionCluster {
    pub cluster_id: Uuid,
    pub kernels: Vec<String>,
    pub fusion_potential: f64,
    pub estimated_speedup: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FusionTemplate {
    pub template_name: String,
    pub pattern_signature: String,
    pub applicable_kernels: Vec<String>,
    pub fusion_strategy: FusionStrategy,
    pub expected_benefits: FusionBenefits,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FusionStrategy {
    pub strategy_name: String,
    pub implementation_approach: String,
    pub resource_management: String,
    pub synchronization_strategy: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FusionBenefits {
    pub memory_bandwidth_reduction: f64,
    pub kernel_launch_overhead_reduction: f64,
    pub cache_locality_improvement: f64,
    pub register_pressure_impact: f64,
}

/// Fusion cost-benefit analyzer
#[derive(Debug)]
pub struct FusionCostBenefitAnalyzer {
    cost_models: HashMap<FusionType, CostModel>,
    benefit_predictors: HashMap<FusionType, BenefitPredictor>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostModel {
    pub fusion_type: FusionType,
    pub development_cost: f64,
    pub validation_cost: f64,
    pub maintenance_cost: f64,
    pub risk_factor: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenefitPredictor {
    pub fusion_type: FusionType,
    pub performance_model: PerformanceModel,
    pub memory_model: MemoryModel,
    pub energy_model: EnergyModel,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceModel {
    pub base_speedup_factor: f64,
    pub scaling_factors: HashMap<String, f64>,
    pub confidence_interval: (f64, f64),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryModel {
    pub memory_reduction_factor: f64,
    pub bandwidth_savings: f64,
    pub cache_improvement: f64,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EnergyModel {
    pub energy_reduction_factor: f64,
    pub power_efficiency_improvement: f64,
}

/// Performance regression detection
#[derive(Debug)]
pub struct PerformanceRegressionDetector {
    baseline_profiles: HashMap<String, BaselineProfile>,
    regression_alerts: Vec<RegressionAlert>,
    statistical_analyzer: StatisticalAnalyzer,
    alert_thresholds: RegressionThresholds,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BaselineProfile {
    pub kernel_name: String,
    pub baseline_performance: Duration,
    pub performance_distribution: PerformanceDistribution,
    pub established_date: SystemTime,
    pub confidence_interval: (Duration, Duration),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceDistribution {
    pub mean: Duration,
    pub std_dev: Duration,
    pub percentiles: HashMap<u8, Duration>, // 50th, 90th, 95th, 99th percentiles
    pub outlier_threshold: Duration,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RegressionAlert {
    pub alert_id: Uuid,
    pub kernel_name: String,
    pub alert_type: RegressionType,
    pub severity: RegressionSeverity,
    pub current_performance: Duration,
    pub baseline_performance: Duration,
    pub regression_magnitude: f64,
    pub detection_timestamp: SystemTime,
    pub potential_causes: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RegressionType {
    PerformanceDegradation,
    MemoryUsageIncrease,
    OccupancyDecrease,
    BandwidthUtilizationDrop,
    EnergyEfficiencyLoss,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RegressionSeverity {
    Minor,    // < 5% regression
    Moderate, // 5-15% regression
    Major,    // 15-30% regression
    Critical, // > 30% regression
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RegressionThresholds {
    pub minor_threshold: f64,
    pub moderate_threshold: f64,
    pub major_threshold: f64,
    pub critical_threshold: f64,
    pub detection_window: Duration,
    pub confidence_level: f64,
}

#[derive(Debug)]
pub struct StatisticalAnalyzer {
    sample_size_requirements: HashMap<String, usize>,
    statistical_tests: Vec<StatisticalTest>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StatisticalTest {
    pub test_name: String,
    pub test_type: TestType,
    pub significance_level: f64,
    pub power: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TestType {
    TTest,
    MannWhitneyU,
    KolmogorovSmirnov,
    ChangePointDetection,
    AnomalyDetection,
}

// Implementation of the main analyzer

impl KernelOptimizationAnalyzer {
    pub fn new() -> Result<Self> {
        Ok(Self {
            kernel_profiles: HashMap::new(),
            optimization_suggestions: HashMap::new(),
            launch_config_analyzer: LaunchConfigAnalyzer::new()?,
            memory_access_analyzer: MemoryAccessAnalyzer::new()?,
            compute_utilization_analyzer: ComputeUtilizationAnalyzer::new()?,
            fusion_analyzer: KernelFusionAnalyzer::new()?,
            performance_regression_detector: PerformanceRegressionDetector::new()?,
        })
    }

    /// Create a stub analyzer for fallback when initialization fails
    pub fn new_stub() -> Self {
        Self {
            kernel_profiles: HashMap::new(),
            optimization_suggestions: HashMap::new(),
            launch_config_analyzer: LaunchConfigAnalyzer::new_stub(),
            memory_access_analyzer: MemoryAccessAnalyzer::new_stub(),
            compute_utilization_analyzer: ComputeUtilizationAnalyzer::new_stub(),
            fusion_analyzer: KernelFusionAnalyzer::new_stub(),
            performance_regression_detector: PerformanceRegressionDetector::new_stub(),
        }
    }

    /// Analyze a kernel execution and generate optimization suggestions
    pub fn analyze_kernel(
        &mut self,
        kernel_name: &str,
        profile_data: KernelProfileData,
    ) -> Result<Vec<KernelOptimization>> {
        // Update kernel profile
        self.update_kernel_profile(kernel_name, profile_data.clone())?;

        // Analyze different aspects
        let launch_config_optimizations =
            self.launch_config_analyzer.analyze(kernel_name, &profile_data)?;
        let memory_optimizations =
            self.memory_access_analyzer.analyze(kernel_name, &profile_data)?;
        let compute_optimizations =
            self.compute_utilization_analyzer.analyze(kernel_name, &profile_data)?;

        // Combine all optimizations
        let mut all_optimizations = Vec::new();
        all_optimizations.extend(launch_config_optimizations);
        all_optimizations.extend(memory_optimizations);
        all_optimizations.extend(compute_optimizations);

        // Rank optimizations by expected impact
        all_optimizations.sort_by(|a, b| {
            b.expected_improvement
                .performance_gain_percentage
                .partial_cmp(&a.expected_improvement.performance_gain_percentage)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        // Store suggestions
        self.optimization_suggestions
            .insert(kernel_name.to_string(), all_optimizations.clone());

        // Check for performance regressions
        self.performance_regression_detector
            .check_regression(kernel_name, &profile_data)?;

        Ok(all_optimizations)
    }

    /// Analyze kernel fusion opportunities
    pub fn analyze_fusion_opportunities(
        &mut self,
        kernel_sequence: &[String],
    ) -> Result<Vec<FusionOpportunity>> {
        self.fusion_analyzer.find_fusion_opportunities(kernel_sequence)
    }

    /// Get comprehensive optimization report for a kernel
    pub fn get_optimization_report(&self, kernel_name: &str) -> Result<KernelOptimizationReport> {
        let profile = self
            .kernel_profiles
            .get(kernel_name)
            .ok_or_else(|| anyhow::anyhow!("Kernel profile not found: {}", kernel_name))?;

        let optimizations =
            self.optimization_suggestions.get(kernel_name).cloned().unwrap_or_default();

        let launch_config_analysis = self.launch_config_analyzer.get_analysis(kernel_name)?;
        let memory_analysis = self.memory_access_analyzer.get_analysis(kernel_name)?;
        let compute_analysis = self.compute_utilization_analyzer.get_analysis(kernel_name)?;

        let fusion_opportunities =
            self.fusion_analyzer.get_opportunities_for_kernel(kernel_name)?;
        let regression_status = self.performance_regression_detector.get_status(kernel_name)?;

        Ok(KernelOptimizationReport {
            kernel_name: kernel_name.to_string(),
            current_performance: profile.clone(),
            optimization_suggestions: optimizations,
            launch_config_analysis,
            memory_analysis,
            compute_analysis,
            fusion_opportunities,
            regression_status,
            overall_optimization_potential: self.calculate_optimization_potential(kernel_name)?,
        })
    }

    fn update_kernel_profile(
        &mut self,
        kernel_name: &str,
        profile_data: KernelProfileData,
    ) -> Result<()> {
        let profile = self.kernel_profiles.entry(kernel_name.to_string()).or_insert_with(|| {
            KernelExecutionProfile {
                kernel_name: kernel_name.to_string(),
                execution_count: 0,
                total_execution_time: Duration::ZERO,
                avg_execution_time: Duration::ZERO,
                min_execution_time: Duration::MAX,
                max_execution_time: Duration::ZERO,
                grid_sizes: Vec::new(),
                block_sizes: Vec::new(),
                shared_memory_usage: Vec::new(),
                register_usage: Vec::new(),
                occupancy_measurements: Vec::new(),
                compute_utilization: Vec::new(),
                memory_bandwidth_utilization: Vec::new(),
                warp_efficiency: Vec::new(),
                memory_efficiency: Vec::new(),
            }
        });

        // Update profile with new data
        profile.execution_count += 1;
        profile.total_execution_time += profile_data.execution_time;
        profile.avg_execution_time = profile.total_execution_time / profile.execution_count as u32;

        if profile_data.execution_time < profile.min_execution_time {
            profile.min_execution_time = profile_data.execution_time;
        }
        if profile_data.execution_time > profile.max_execution_time {
            profile.max_execution_time = profile_data.execution_time;
        }

        profile.grid_sizes.push(profile_data.grid_size);
        profile.block_sizes.push(profile_data.block_size);
        profile.shared_memory_usage.push(profile_data.shared_memory_bytes);
        profile.register_usage.push(profile_data.registers_per_thread);
        profile.occupancy_measurements.push(profile_data.occupancy);
        profile.compute_utilization.push(profile_data.compute_utilization);
        profile
            .memory_bandwidth_utilization
            .push(profile_data.memory_bandwidth_utilization);
        profile.warp_efficiency.push(profile_data.warp_efficiency);
        profile.memory_efficiency.push(profile_data.memory_efficiency);

        Ok(())
    }

    fn calculate_optimization_potential(&self, kernel_name: &str) -> Result<OptimizationPotential> {
        let optimizations = self
            .optimization_suggestions
            .get(kernel_name)
            .ok_or_else(|| anyhow::anyhow!("No optimizations found for kernel: {}", kernel_name))?;

        let max_performance_gain = optimizations
            .iter()
            .map(|opt| opt.expected_improvement.performance_gain_percentage)
            .fold(0.0, f64::max);

        let total_memory_savings = optimizations
            .iter()
            .map(|opt| opt.expected_improvement.memory_usage_reduction_percentage)
            .sum::<f64>();

        let avg_implementation_difficulty = optimizations
            .iter()
            .map(|opt| match opt.implementation_difficulty {
                ImplementationDifficulty::Trivial => 1.0,
                ImplementationDifficulty::Easy => 2.0,
                ImplementationDifficulty::Moderate => 3.0,
                ImplementationDifficulty::Difficult => 4.0,
                ImplementationDifficulty::Expert => 5.0,
            })
            .sum::<f64>()
            / optimizations.len() as f64;

        Ok(OptimizationPotential {
            max_performance_gain,
            total_memory_savings,
            avg_implementation_difficulty,
            optimization_count: optimizations.len(),
            priority_score: self
                .calculate_priority_score(max_performance_gain, avg_implementation_difficulty),
        })
    }

    fn calculate_priority_score(&self, performance_gain: f64, difficulty: f64) -> f64 {
        // Higher score = higher priority
        // Balance performance gain against implementation difficulty
        performance_gain / (difficulty * difficulty)
    }
}

// Helper structures and implementations

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct KernelProfileData {
    pub execution_time: Duration,
    pub grid_size: (u32, u32, u32),
    pub block_size: (u32, u32, u32),
    pub shared_memory_bytes: usize,
    pub registers_per_thread: u32,
    pub occupancy: f64,
    pub compute_utilization: f64,
    pub memory_bandwidth_utilization: f64,
    pub warp_efficiency: f64,
    pub memory_efficiency: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct KernelOptimizationReport {
    pub kernel_name: String,
    pub current_performance: KernelExecutionProfile,
    pub optimization_suggestions: Vec<KernelOptimization>,
    pub launch_config_analysis: LaunchConfigAnalysisResult,
    pub memory_analysis: MemoryAnalysisResult,
    pub compute_analysis: ComputeAnalysisResult,
    pub fusion_opportunities: Vec<FusionOpportunity>,
    pub regression_status: RegressionStatus,
    pub overall_optimization_potential: OptimizationPotential,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationPotential {
    pub max_performance_gain: f64,
    pub total_memory_savings: f64,
    pub avg_implementation_difficulty: f64,
    pub optimization_count: usize,
    pub priority_score: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LaunchConfigAnalysisResult {
    pub current_config: (u32, u32, u32, u32, u32, u32), // grid + block
    pub optimal_config: OptimalLaunchConfig,
    pub configuration_recommendations: Vec<ConfigurationRecommendation>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConfigurationRecommendation {
    pub recommendation_type: ConfigurationRecommendationType,
    pub current_value: String,
    pub recommended_value: String,
    pub expected_improvement: f64,
    pub rationale: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConfigurationRecommendationType {
    BlockSizeOptimization,
    GridSizeOptimization,
    SharedMemoryOptimization,
    OccupancyImprovement,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryAnalysisResult {
    pub access_pattern_analysis: MemoryAccessAnalysis,
    pub coalescing_analysis: CoalescingAnalysis,
    pub cache_performance: CachePerformanceAnalysis,
    pub memory_optimization_recommendations: Vec<MemoryOptimizationRecommendation>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryOptimizationRecommendation {
    pub recommendation_type: MemoryOptimizationRecommendationType,
    pub description: String,
    pub expected_improvement: f64,
    pub implementation_steps: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MemoryOptimizationRecommendationType {
    CoalescingImprovement,
    CacheOptimization,
    StrideOptimization,
    BankConflictResolution,
    PrefetchingStrategy,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComputeAnalysisResult {
    pub utilization_profile: ComputeUtilizationProfile,
    pub bottleneck_analysis: ComputeBottleneckAnalysis,
    pub arithmetic_intensity_analysis: ArithmeticIntensityProfile,
    pub resource_utilization_recommendations: Vec<ResourceOptimizationRecommendation>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceOptimizationRecommendation {
    pub recommendation_type: ResourceOptimizationRecommendationType,
    pub description: String,
    pub expected_benefit: f64,
    pub resource_impact: ResourceImpact,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ResourceOptimizationRecommendationType {
    RegisterOptimization,
    SharedMemoryOptimization,
    OccupancyImprovement,
    ComputeIntensityBalance,
    ResourceLoadBalancing,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceImpact {
    pub register_usage_change: i32,
    pub shared_memory_change: i32,
    pub occupancy_change: f64,
    pub performance_change: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RegressionStatus {
    pub has_regression: bool,
    pub regression_alerts: Vec<RegressionAlert>,
    pub performance_trend: PerformanceTrend,
    pub baseline_comparison: BaselineComparison,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PerformanceTrend {
    Improving,
    Stable,
    Degrading,
    Volatile,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BaselineComparison {
    pub current_vs_baseline: f64, // Percentage difference
    pub statistical_significance: f64,
    pub confidence_interval: (f64, f64),
}

// Implementation stubs for sub-analyzers

impl LaunchConfigAnalyzer {
    fn new() -> Result<Self> {
        Ok(Self {
            optimal_configs: HashMap::new(),
            config_performance_history: HashMap::new(),
            autotuning_enabled: true,
            search_space_cache: HashMap::new(),
        })
    }

    fn new_stub() -> Self {
        Self {
            optimal_configs: HashMap::new(),
            config_performance_history: HashMap::new(),
            autotuning_enabled: false,
            search_space_cache: HashMap::new(),
        }
    }

    fn analyze(
        &mut self,
        _kernel_name: &str,
        profile_data: &KernelProfileData,
    ) -> Result<Vec<KernelOptimization>> {
        // Real CPU-side launch-configuration analysis: estimate occupancy from
        // the launch descriptor + documented sm_86 device limits and emit
        // block-size / register / shared-memory recommendations.
        Ok(analysis::analyze_launch_config(profile_data))
    }

    fn get_analysis(&self, kernel_name: &str) -> Result<LaunchConfigAnalysisResult> {
        // Simplified implementation
        Ok(LaunchConfigAnalysisResult {
            current_config: (1, 1, 1, 256, 1, 1),
            optimal_config: OptimalLaunchConfig {
                kernel_name: kernel_name.to_string(),
                optimal_block_size: (256, 1, 1),
                optimal_grid_size: (1024, 1, 1),
                optimal_shared_memory: 0,
                expected_occupancy: 1.0,
                expected_performance: 1.0,
                constraints: vec![],
            },
            configuration_recommendations: vec![],
        })
    }
}

impl MemoryAccessAnalyzer {
    fn new() -> Result<Self> {
        Ok(Self {
            access_patterns: HashMap::new(),
            coalescing_analysis: HashMap::new(),
            cache_performance: HashMap::new(),
            stride_analysis: HashMap::new(),
            bank_conflict_analyzer: BankConflictAnalyzer::new()?,
        })
    }

    fn new_stub() -> Self {
        Self {
            access_patterns: HashMap::new(),
            coalescing_analysis: HashMap::new(),
            cache_performance: HashMap::new(),
            stride_analysis: HashMap::new(),
            bank_conflict_analyzer: BankConflictAnalyzer::new_stub(),
        }
    }

    fn analyze(
        &mut self,
        _kernel_name: &str,
        profile_data: &KernelProfileData,
    ) -> Result<Vec<KernelOptimization>> {
        // Real CPU-side memory-access analysis: classify coalescing / warp
        // divergence from the measured efficiency metrics and emit fixes.
        Ok(analysis::analyze_memory_access(profile_data))
    }

    fn get_analysis(&self, kernel_name: &str) -> Result<MemoryAnalysisResult> {
        // Simplified implementation
        Ok(MemoryAnalysisResult {
            access_pattern_analysis: MemoryAccessAnalysis {
                kernel_name: kernel_name.to_string(),
                total_memory_transactions: 0,
                coalesced_transactions: 0,
                uncoalesced_transactions: 0,
                stride_patterns: vec![],
                access_locality: AccessLocalityMetrics {
                    temporal_locality_score: 0.8,
                    spatial_locality_score: 0.9,
                    working_set_size: 1024,
                    reuse_distance_avg: 10.0,
                },
                bank_conflicts: 0,
                cache_line_utilization: 0.85,
            },
            coalescing_analysis: CoalescingAnalysis {
                kernel_name: kernel_name.to_string(),
                coalescing_efficiency: 0.9,
                uncoalesced_regions: vec![],
                suggested_improvements: vec![],
            },
            cache_performance: CachePerformanceAnalysis {
                kernel_name: kernel_name.to_string(),
                l1_cache_hit_rate: 0.85,
                l2_cache_hit_rate: 0.70,
                texture_cache_hit_rate: 0.95,
                shared_memory_bank_conflicts: 0,
                cache_thrashing_detected: false,
                recommended_cache_optimizations: vec![],
            },
            memory_optimization_recommendations: vec![],
        })
    }
}

impl ComputeUtilizationAnalyzer {
    fn new() -> Result<Self> {
        Ok(Self {
            utilization_profiles: HashMap::new(),
            bottleneck_analysis: HashMap::new(),
            arithmetic_intensity_analyzer: ArithmeticIntensityAnalyzer::new()?,
            resource_balancer: ResourceBalancer::new()?,
        })
    }

    fn new_stub() -> Self {
        Self {
            utilization_profiles: HashMap::new(),
            bottleneck_analysis: HashMap::new(),
            arithmetic_intensity_analyzer: ArithmeticIntensityAnalyzer::new_stub(),
            resource_balancer: ResourceBalancer::new_stub(),
        }
    }

    fn analyze(
        &mut self,
        _kernel_name: &str,
        profile_data: &KernelProfileData,
    ) -> Result<Vec<KernelOptimization>> {
        // Real CPU-side compute-utilization analysis: place the kernel on the
        // roofline (arithmetic intensity vs ridge point) and classify the
        // bottleneck (memory-bound / compute-bound / latency-bound).
        Ok(analysis::analyze_compute_utilization(profile_data))
    }

    fn get_analysis(&self, kernel_name: &str) -> Result<ComputeAnalysisResult> {
        // Simplified implementation
        Ok(ComputeAnalysisResult {
            utilization_profile: ComputeUtilizationProfile {
                kernel_name: kernel_name.to_string(),
                arithmetic_intensity: 2.5,
                compute_throughput: 1000.0,
                memory_throughput: 800.0,
                compute_to_memory_ratio: 1.25,
                warp_execution_efficiency: 0.95,
                instruction_mix: InstructionMixAnalysis {
                    integer_ops_percentage: 20.0,
                    float_ops_percentage: 70.0,
                    double_ops_percentage: 5.0,
                    special_function_ops_percentage: 2.0,
                    memory_ops_percentage: 25.0,
                    control_flow_ops_percentage: 3.0,
                },
                resource_utilization: ResourceUtilizationMetrics {
                    register_utilization: 0.75,
                    shared_memory_utilization: 0.60,
                    constant_memory_utilization: 0.30,
                    texture_cache_utilization: 0.80,
                    compute_unit_utilization: 0.85,
                },
            },
            bottleneck_analysis: ComputeBottleneckAnalysis {
                kernel_name: kernel_name.to_string(),
                primary_bottleneck: ComputeBottleneckType::MemoryBandwidth,
                bottleneck_severity: 0.6,
                contributing_factors: vec![],
                optimization_opportunities: vec![],
            },
            arithmetic_intensity_analysis: ArithmeticIntensityProfile {
                kernel_name: kernel_name.to_string(),
                operations_per_byte: 2.5,
                compute_intensity: ComputeIntensityCategory::Balanced,
                memory_bound_ratio: 0.6,
                compute_bound_ratio: 0.4,
                roofline_position: RooflinePosition {
                    current_performance: 800.0,
                    theoretical_peak: 1000.0,
                    memory_bandwidth_limit: 900.0,
                    efficiency_percentage: 80.0,
                },
                optimization_direction: OptimizationDirection::IncreaseComputeIntensity,
            },
            resource_utilization_recommendations: vec![],
        })
    }
}

impl KernelFusionAnalyzer {
    fn new() -> Result<Self> {
        Ok(Self {
            fusion_opportunities: HashMap::new(),
            dependency_graph: KernelDependencyGraph::new(),
            fusion_templates: vec![],
            cost_benefit_analyzer: FusionCostBenefitAnalyzer::new()?,
        })
    }

    fn new_stub() -> Self {
        Self {
            fusion_opportunities: HashMap::new(),
            dependency_graph: KernelDependencyGraph::new(),
            fusion_templates: vec![],
            cost_benefit_analyzer: FusionCostBenefitAnalyzer::new_stub(),
        }
    }

    fn find_fusion_opportunities(
        &mut self,
        kernel_sequence: &[String],
    ) -> Result<Vec<FusionOpportunity>> {
        // Real CPU-side fusion detection: examine each adjacent producer→consumer
        // pair, classify the kernels, and model the memory-traffic speedup.
        let opportunities = analysis::find_fusion_opportunities(kernel_sequence);

        // Index opportunities by participating kernel so per-kernel reports can
        // surface them later.
        for opportunity in &opportunities {
            for kernel in &opportunity.kernel_group {
                self.fusion_opportunities
                    .entry(kernel.clone())
                    .or_default()
                    .push(opportunity.clone());
            }
        }

        Ok(opportunities)
    }

    fn get_opportunities_for_kernel(&self, kernel_name: &str) -> Result<Vec<FusionOpportunity>> {
        Ok(self.fusion_opportunities.get(kernel_name).cloned().unwrap_or_default())
    }
}

impl PerformanceRegressionDetector {
    fn new() -> Result<Self> {
        Ok(Self {
            baseline_profiles: HashMap::new(),
            regression_alerts: vec![],
            statistical_analyzer: StatisticalAnalyzer::new()?,
            alert_thresholds: RegressionThresholds {
                minor_threshold: 0.05,
                moderate_threshold: 0.15,
                major_threshold: 0.30,
                critical_threshold: 0.50,
                detection_window: Duration::from_secs(3600),
                confidence_level: 0.95,
            },
        })
    }

    fn new_stub() -> Self {
        Self {
            baseline_profiles: HashMap::new(),
            regression_alerts: vec![],
            statistical_analyzer: StatisticalAnalyzer::new_stub(),
            alert_thresholds: RegressionThresholds {
                minor_threshold: 0.05,
                moderate_threshold: 0.15,
                major_threshold: 0.30,
                critical_threshold: 0.50,
                detection_window: Duration::from_secs(3600),
                confidence_level: 0.95,
            },
        }
    }

    fn check_regression(
        &mut self,
        _kernel_name: &str,
        _profile_data: &KernelProfileData,
    ) -> Result<()> {
        // Simplified implementation - would perform statistical regression analysis
        Ok(())
    }

    fn get_status(&self, _kernel_name: &str) -> Result<RegressionStatus> {
        Ok(RegressionStatus {
            has_regression: false,
            regression_alerts: vec![],
            performance_trend: PerformanceTrend::Stable,
            baseline_comparison: BaselineComparison {
                current_vs_baseline: 0.0,
                statistical_significance: 0.95,
                confidence_interval: (-0.05, 0.05),
            },
        })
    }
}

// Implementation stubs for remaining analyzers

impl BankConflictAnalyzer {
    fn new() -> Result<Self> {
        Ok(Self {
            conflict_patterns: HashMap::new(),
            resolution_strategies: HashMap::new(),
        })
    }

    fn new_stub() -> Self {
        Self {
            conflict_patterns: HashMap::new(),
            resolution_strategies: HashMap::new(),
        }
    }
}

impl ArithmeticIntensityAnalyzer {
    fn new() -> Result<Self> {
        Ok(Self {
            intensity_profiles: HashMap::new(),
            roofline_models: HashMap::new(),
        })
    }

    fn new_stub() -> Self {
        Self {
            intensity_profiles: HashMap::new(),
            roofline_models: HashMap::new(),
        }
    }
}

impl ResourceBalancer {
    fn new() -> Result<Self> {
        Ok(Self {
            resource_profiles: HashMap::new(),
            balancing_strategies: HashMap::new(),
        })
    }

    fn new_stub() -> Self {
        Self {
            resource_profiles: HashMap::new(),
            balancing_strategies: HashMap::new(),
        }
    }
}

impl KernelDependencyGraph {
    fn new() -> Self {
        Self {
            nodes: HashMap::new(),
            edges: vec![],
            fusion_clusters: vec![],
        }
    }
}

impl FusionCostBenefitAnalyzer {
    fn new() -> Result<Self> {
        Ok(Self {
            cost_models: HashMap::new(),
            benefit_predictors: HashMap::new(),
        })
    }

    fn new_stub() -> Self {
        Self {
            cost_models: HashMap::new(),
            benefit_predictors: HashMap::new(),
        }
    }
}

impl StatisticalAnalyzer {
    fn new() -> Result<Self> {
        Ok(Self {
            sample_size_requirements: HashMap::new(),
            statistical_tests: vec![],
        })
    }

    fn new_stub() -> Self {
        Self {
            sample_size_requirements: HashMap::new(),
            statistical_tests: vec![],
        }
    }
}

/// Configuration for kernel optimization analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct KernelOptimizationConfig {
    /// Enable launch configuration optimization
    pub enable_launch_config_optimization: bool,
    /// Enable memory access optimization
    pub enable_memory_access_optimization: bool,
    /// Enable kernel fusion analysis
    pub enable_kernel_fusion: bool,
    /// Enable performance regression detection
    pub enable_regression_detection: bool,
    /// Maximum number of optimization suggestions per kernel
    pub max_optimization_suggestions: usize,
    /// Minimum performance improvement threshold (percentage)
    pub min_improvement_threshold: f64,
}

impl Default for KernelOptimizationConfig {
    fn default() -> Self {
        Self {
            enable_launch_config_optimization: true,
            enable_memory_access_optimization: true,
            enable_kernel_fusion: true,
            enable_regression_detection: true,
            max_optimization_suggestions: 10,
            min_improvement_threshold: 5.0,
        }
    }
}

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

    #[test]
    fn test_kernel_optimization_config_default() {
        let config = KernelOptimizationConfig::default();
        assert!(config.enable_launch_config_optimization);
        assert!(config.enable_memory_access_optimization);
        assert!(config.enable_kernel_fusion);
        assert!(config.enable_regression_detection);
        assert_eq!(config.max_optimization_suggestions, 10);
        assert!((config.min_improvement_threshold - 5.0).abs() < f64::EPSILON);
    }

    #[test]
    fn test_launch_config_search_space_creation() {
        let space = LaunchConfigSearchSpace {
            kernel_name: "matmul_kernel".to_string(),
            min_block_size: (1, 1, 1),
            max_block_size: (1024, 1024, 64),
            block_size_constraints: vec![
                BlockSizeConstraint::MultipleOf(32),
                BlockSizeConstraint::PowerOfTwo,
            ],
            shared_memory_constraints: MemoryConstraints {
                max_shared_memory_per_block: 49152,
                bank_conflict_aware: true,
                coalescing_optimization: true,
            },
            register_constraints: RegisterConstraints {
                max_registers_per_thread: 255,
                spill_threshold: 64,
                occupancy_impact_threshold: 0.5,
            },
            occupancy_targets: OccupancyTargets {
                minimum_occupancy: 0.25,
                target_occupancy: 0.75,
                theoretical_occupancy: 1.0,
            },
        };
        assert_eq!(space.kernel_name, "matmul_kernel");
        assert_eq!(space.block_size_constraints.len(), 2);
    }

    #[test]
    fn test_stride_analysis_result_creation() {
        let result = StrideAnalysisResult {
            kernel_name: "conv_kernel".to_string(),
            detected_strides: vec![DetectedStride {
                stride_bytes: 4,
                frequency: 1000,
                memory_region: "global".to_string(),
                performance_impact: StrideImpact::Optimal,
            }],
            access_pattern_classification: AccessPatternType::Sequential,
            optimization_potential: 0.3,
            recommended_optimizations: vec![],
        };
        assert_eq!(result.detected_strides.len(), 1);
        assert!(matches!(
            result.access_pattern_classification,
            AccessPatternType::Sequential
        ));
    }

    #[test]
    fn test_bank_conflict_pattern_creation() {
        let pattern = BankConflictPattern {
            kernel_name: "shared_mem_kernel".to_string(),
            conflict_count: 50,
            conflict_severity: ConflictSeverity::Medium,
            conflicting_addresses: vec![ConflictingAccess {
                address_pattern: "stride_4".to_string(),
                conflict_degree: 4,
                access_frequency: 100,
                performance_penalty: 0.15,
            }],
            bank_utilization: vec![0.8, 0.7, 0.9, 0.6],
        };
        assert_eq!(pattern.conflict_count, 50);
        assert!(matches!(
            pattern.conflict_severity,
            ConflictSeverity::Medium
        ));
    }

    #[test]
    fn test_conflict_resolution_strategy_creation() {
        let strategy = ConflictResolutionStrategy {
            strategy_type: ConflictResolutionType::ArrayPadding,
            description: "Add padding to shared memory arrays".to_string(),
            expected_speedup: 1.3,
            implementation_steps: vec![
                "Identify conflicting arrays".to_string(),
                "Add padding to array declarations".to_string(),
            ],
        };
        assert!(matches!(
            strategy.strategy_type,
            ConflictResolutionType::ArrayPadding
        ));
        assert!(strategy.expected_speedup > 1.0);
    }

    #[test]
    fn test_arithmetic_intensity_profile() {
        let profile = ArithmeticIntensityProfile {
            kernel_name: "gemm".to_string(),
            operations_per_byte: 50.0,
            compute_intensity: ComputeIntensityCategory::ComputeBound,
            memory_bound_ratio: 0.2,
            compute_bound_ratio: 0.8,
            roofline_position: RooflinePosition {
                current_performance: 500.0,
                theoretical_peak: 1000.0,
                memory_bandwidth_limit: 900.0,
                efficiency_percentage: 50.0,
            },
            optimization_direction: OptimizationDirection::IncreaseComputeIntensity,
        };
        assert!(matches!(
            profile.compute_intensity,
            ComputeIntensityCategory::ComputeBound
        ));
        assert!((profile.roofline_position.efficiency_percentage - 50.0).abs() < f64::EPSILON);
    }

    #[test]
    fn test_roofline_model() {
        let model = RooflineModel {
            device_id: 0,
            peak_compute_performance: 10000.0,
            peak_memory_bandwidth: 900.0,
            cache_hierarchy: CacheHierarchy {
                l1_cache_bandwidth: 12000.0,
                l2_cache_bandwidth: 3000.0,
                shared_memory_bandwidth: 6000.0,
                texture_cache_bandwidth: 2000.0,
                constant_cache_bandwidth: 8000.0,
            },
            compute_capabilities: ComputeCapabilities {
                fp32_performance: 10000.0,
                fp16_performance: 20000.0,
                int32_performance: 5000.0,
                tensor_performance: 100000.0,
                special_function_performance: 2500.0,
            },
        };
        assert!(model.peak_compute_performance > 0.0);
        assert!(
            model.cache_hierarchy.l1_cache_bandwidth > model.cache_hierarchy.l2_cache_bandwidth
        );
    }

    #[test]
    fn test_resource_profile() {
        let profile = ResourceProfile {
            kernel_name: "attention_kernel".to_string(),
            register_pressure: ResourcePressure::High,
            shared_memory_pressure: ResourcePressure::Medium,
            occupancy_limiting_factor: OccupancyLimitingFactor::RegisterCount,
            resource_utilization_efficiency: 0.65,
        };
        assert!(matches!(profile.register_pressure, ResourcePressure::High));
        assert!(matches!(
            profile.occupancy_limiting_factor,
            OccupancyLimitingFactor::RegisterCount
        ));
    }

    #[test]
    fn test_balancing_strategy() {
        let strategy = BalancingStrategy {
            strategy_type: BalancingStrategyType::RegisterOptimization,
            description: "Reduce register usage per thread".to_string(),
            expected_occupancy_improvement: 0.15,
            performance_impact: 0.10,
        };
        assert!(strategy.expected_occupancy_improvement > 0.0);
    }

    #[test]
    fn test_fusion_opportunity() {
        let opportunity = FusionOpportunity {
            opportunity_id: Uuid::new_v4(),
            kernel_group: vec!["bias_add".to_string(), "relu".to_string()],
            fusion_type: FusionType::ElementwiseFusion,
            data_dependencies: vec![DataDependency {
                source_kernel: "bias_add".to_string(),
                target_kernel: "relu".to_string(),
                dependency_type: DependencyType::ReadAfterWrite,
                data_size: 4096,
                access_pattern: "sequential".to_string(),
            }],
            expected_speedup: 1.5,
            memory_savings: 4096,
            implementation_complexity: ImplementationDifficulty::Easy,
            fusion_feasibility: FusionFeasibility {
                resource_constraints_satisfied: true,
                register_usage_feasible: true,
                shared_memory_feasible: true,
                synchronization_complexity: SynchronizationComplexity::None,
                fusion_confidence: 0.95,
            },
        };
        assert_eq!(opportunity.kernel_group.len(), 2);
        assert!(matches!(
            opportunity.fusion_type,
            FusionType::ElementwiseFusion
        ));
        assert!(opportunity.fusion_feasibility.resource_constraints_satisfied);
    }

    #[test]
    fn test_fusion_cost_benefit_analyzer_new_stub() {
        let analyzer = FusionCostBenefitAnalyzer::new_stub();
        assert!(analyzer.cost_models.is_empty());
    }

    #[test]
    fn test_statistical_analyzer_new_stub() {
        let analyzer = StatisticalAnalyzer::new_stub();
        assert!(analyzer.sample_size_requirements.is_empty());
    }

    #[test]
    fn test_stride_impact_variants() {
        let impacts = [
            StrideImpact::Optimal,
            StrideImpact::Good,
            StrideImpact::Moderate,
            StrideImpact::Poor,
            StrideImpact::Critical,
        ];
        assert_eq!(impacts.len(), 5);
    }

    #[test]
    fn test_access_pattern_type_variants() {
        let patterns = [
            AccessPatternType::Sequential,
            AccessPatternType::Strided,
            AccessPatternType::Random,
            AccessPatternType::Blocked,
            AccessPatternType::Sparse,
            AccessPatternType::Irregular,
        ];
        assert_eq!(patterns.len(), 6);
    }

    #[test]
    fn test_stride_optimization() {
        let opt = StrideOptimization {
            optimization_type: StrideOptimizationType::TilingStrategy,
            description: "Apply loop tiling for better cache utilization".to_string(),
            expected_improvement: 0.25,
            implementation_complexity: ImplementationDifficulty::Moderate,
        };
        assert!(matches!(
            opt.optimization_type,
            StrideOptimizationType::TilingStrategy
        ));
    }

    #[test]
    fn test_occupancy_targets() {
        let targets = OccupancyTargets {
            minimum_occupancy: 0.25,
            target_occupancy: 0.75,
            theoretical_occupancy: 1.0,
        };
        assert!(targets.minimum_occupancy < targets.target_occupancy);
        assert!(targets.target_occupancy <= targets.theoretical_occupancy);
    }

    #[test]
    fn test_memory_constraints() {
        let constraints = MemoryConstraints {
            max_shared_memory_per_block: 49152,
            bank_conflict_aware: true,
            coalescing_optimization: true,
        };
        assert!(constraints.bank_conflict_aware);
        assert_eq!(constraints.max_shared_memory_per_block, 49152);
    }

    #[test]
    fn test_compute_capabilities() {
        let caps = ComputeCapabilities {
            fp32_performance: 10000.0,
            fp16_performance: 20000.0,
            int32_performance: 5000.0,
            tensor_performance: 100000.0,
            special_function_performance: 2500.0,
        };
        assert!(caps.fp16_performance > caps.fp32_performance);
        assert!(caps.tensor_performance > caps.fp16_performance);
    }

    #[test]
    fn test_fusion_cost_benefit_analyzer_new() {
        let result = FusionCostBenefitAnalyzer::new();
        assert!(result.is_ok());
    }

    #[test]
    fn test_statistical_analyzer_new() {
        let result = StatisticalAnalyzer::new();
        assert!(result.is_ok());
    }

    #[test]
    fn test_fusion_type_variants() {
        let types = [
            FusionType::ElementwiseFusion,
            FusionType::ProducerConsumerFusion,
            FusionType::LoopFusion,
            FusionType::ReductionFusion,
            FusionType::ConvolutionFusion,
            FusionType::AttentionFusion,
        ];
        assert_eq!(types.len(), 6);
    }

    #[test]
    fn test_dependency_type_variants() {
        let types = [
            DependencyType::ReadAfterWrite,
            DependencyType::WriteAfterRead,
            DependencyType::WriteAfterWrite,
            DependencyType::Reduction,
            DependencyType::Broadcast,
        ];
        assert_eq!(types.len(), 5);
    }

    #[test]
    fn test_data_dependency_creation() {
        let dep = DataDependency {
            source_kernel: "conv1".to_string(),
            target_kernel: "relu1".to_string(),
            dependency_type: DependencyType::ReadAfterWrite,
            data_size: 8192,
            access_pattern: "contiguous".to_string(),
        };
        assert_eq!(dep.source_kernel, "conv1");
        assert_eq!(dep.data_size, 8192);
    }

    #[test]
    fn test_fusion_feasibility_creation() {
        let feasibility = FusionFeasibility {
            resource_constraints_satisfied: true,
            register_usage_feasible: true,
            shared_memory_feasible: false,
            synchronization_complexity: SynchronizationComplexity::None,
            fusion_confidence: 0.7,
        };
        assert!(feasibility.resource_constraints_satisfied);
        assert!(!feasibility.shared_memory_feasible);
    }

    #[test]
    fn test_optimization_direction_variants() {
        let dirs = [
            OptimizationDirection::IncreaseComputeIntensity,
            OptimizationDirection::ImproveMemoryEfficiency,
            OptimizationDirection::BalanceComputeMemory,
            OptimizationDirection::OptimizeForLatency,
        ];
        assert_eq!(dirs.len(), 4);
    }

    #[test]
    fn test_block_size_constraint_variants() {
        let constraints = [
            BlockSizeConstraint::MultipleOf(32),
            BlockSizeConstraint::PowerOfTwo,
            BlockSizeConstraint::MaxThreadsPerBlock(1024),
            BlockSizeConstraint::SharedMemoryLimit(49152),
            BlockSizeConstraint::RegisterLimit(255),
        ];
        assert_eq!(constraints.len(), 5);
    }

    #[test]
    fn test_register_constraints_creation() {
        let constraints = RegisterConstraints {
            max_registers_per_thread: 255,
            spill_threshold: 64,
            occupancy_impact_threshold: 0.5,
        };
        assert_eq!(constraints.max_registers_per_thread, 255);
        assert!((constraints.occupancy_impact_threshold - 0.5).abs() < f64::EPSILON);
    }

    fn low_occupancy_matmul_profile() -> KernelProfileData {
        KernelProfileData {
            execution_time: Duration::from_micros(250),
            grid_size: (4096, 1, 1),
            block_size: (256, 1, 1),
            shared_memory_bytes: 0,
            registers_per_thread: 128, // register-limited → low occupancy
            occupancy: 0.33,
            compute_utilization: 0.65,
            memory_bandwidth_utilization: 0.45,
            warp_efficiency: 0.92,
            memory_efficiency: 0.88,
        }
    }

    #[test]
    fn test_analyze_kernel_returns_real_optimizations() {
        let mut analyzer =
            KernelOptimizationAnalyzer::new().expect("analyzer construction should succeed");
        let opts = analyzer
            .analyze_kernel("matmul_tile", low_occupancy_matmul_profile())
            .expect("analysis should succeed");
        assert!(
            !opts.is_empty(),
            "low-occupancy register-limited kernel must yield optimizations"
        );
        // Results are ranked by performance gain (descending) and within range.
        for window in opts.windows(2) {
            assert!(
                window[0].expected_improvement.performance_gain_percentage
                    >= window[1].expected_improvement.performance_gain_percentage
            );
        }
        for opt in &opts {
            assert!((0.0..=1.0).contains(&opt.confidence));
            assert!((0.0..=95.0).contains(&opt.expected_improvement.performance_gain_percentage));
        }
    }

    #[test]
    fn test_analyze_memory_bound_kernel() {
        let mut analyzer =
            KernelOptimizationAnalyzer::new().expect("analyzer construction should succeed");
        let profile = KernelProfileData {
            execution_time: Duration::from_micros(80),
            grid_size: (8192, 1, 1),
            block_size: (256, 1, 1),
            shared_memory_bytes: 0,
            registers_per_thread: 32,
            occupancy: 0.55,
            compute_utilization: 0.15,
            memory_bandwidth_utilization: 0.9,
            warp_efficiency: 0.6,
            memory_efficiency: 0.4,
        };
        let opts = analyzer.analyze_kernel("gemv", profile).expect("analysis should succeed");
        assert!(
            !opts.is_empty(),
            "memory-bound kernel must yield optimizations"
        );
        assert!(opts.iter().any(|o| matches!(
            o.optimization_type,
            crate::advanced_gpu_profiler::OptimizationType::MemoryCoalescing
                | crate::advanced_gpu_profiler::OptimizationType::ComputeIntensityBalance
        )));
    }

    #[test]
    fn test_analyze_fusion_opportunities_public_api() {
        let mut analyzer =
            KernelOptimizationAnalyzer::new().expect("analyzer construction should succeed");
        let sequence = vec![
            "matmul_qk".to_string(),
            "softmax".to_string(),
            "matmul_v".to_string(),
            "bias_add".to_string(),
            "gelu".to_string(),
        ];
        let opportunities = analyzer
            .analyze_fusion_opportunities(&sequence)
            .expect("fusion analysis should succeed");
        assert!(
            !opportunities.is_empty(),
            "an attention-style kernel chain must expose fusion opportunities"
        );
        for opp in &opportunities {
            assert!(opp.expected_speedup > 1.0);
            assert_eq!(opp.kernel_group.len(), 2);
            assert!(opp.memory_savings > 0);
            assert!((0.0..=1.0).contains(&opp.fusion_feasibility.fusion_confidence));
        }
        // Opportunities are indexed per participating kernel.
        let report = analyzer.fusion_analyzer.get_opportunities_for_kernel("softmax");
        assert!(report.is_ok());
        assert!(!report.expect("indexed opportunities").is_empty());
    }
}