//! CUDA Performance Optimization Coordinator
//!
//! This module provides a comprehensive coordination system that integrates all advanced
//! performance optimization components including memory optimization, kernel fusion,
//! intelligent task scheduling, and execution engine management to deliver maximum
//! CUDA performance across all operations and workloads.

use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant, SystemTime};

use super::intelligent_task_scheduler::{
    IntelligentSchedulingConfig, IntelligentTaskScheduler, SchedulableTask, SchedulingStatus,
    TaskType,
};
use super::kernel_fusion_optimizer::{
    AdvancedKernelFusionOptimizer, FusionOperation, KernelFusionConfig, KernelFusionStatus,
    OperationType as FusionOperationType,
};
use super::memory::optimization::advanced_memory_optimizer::{
    AdvancedMemoryConfig, AdvancedMemoryOptimizer, MemoryOptimizationReport,
    MemoryOptimizationStatus,
};

/// Comprehensive CUDA performance optimization coordinator
///
/// This system coordinates all performance optimization components to deliver
/// enterprise-grade CUDA acceleration with intelligent resource management,
/// adaptive optimization strategies, and predictive performance enhancement.
#[derive(Debug)]
pub struct CudaPerformanceOptimizationCoordinator {
    /// Advanced memory optimization engine
    memory_optimizer: Arc<Mutex<AdvancedMemoryOptimizer>>,

    /// Kernel fusion optimization engine
    fusion_optimizer: Arc<Mutex<AdvancedKernelFusionOptimizer>>,

    /// Intelligent task scheduling system
    task_scheduler: Arc<Mutex<IntelligentTaskScheduler>>,

    /// Performance metrics collection system
    metrics_collector: Arc<Mutex<PerformanceMetricsCollector>>,

    /// Optimization decision engine
    decision_engine: Arc<Mutex<OptimizationDecisionEngine>>,

    /// Performance feedback system
    feedback_system: Arc<Mutex<PerformanceFeedbackSystem>>,

    /// Workload analyzer
    workload_analyzer: Arc<Mutex<WorkloadAnalyzer>>,

    /// Resource allocation coordinator
    resource_coordinator: Arc<Mutex<ResourceAllocationCoordinator>>,

    /// Configuration
    config: PerformanceCoordinatorConfig,

    /// Coordination state
    coordination_state: Arc<RwLock<CoordinationState>>,

    /// Performance statistics
    statistics: Arc<Mutex<CoordinationStatistics>>,

    /// Optimization history
    optimization_history: Arc<Mutex<VecDeque<CoordinationRecord>>>,
}

/// Performance metrics collection system
#[derive(Debug)]
pub struct PerformanceMetricsCollector {
    /// Real-time performance metrics
    realtime_metrics: PerformanceMetrics,

    /// Historical metrics database
    historical_metrics: MetricsDatabase,

    /// Metrics aggregation engine
    aggregation_engine: MetricsAggregationEngine,

    /// Performance trend analyzer
    trend_analyzer: PerformanceTrendAnalyzer,

    /// Benchmark comparator
    benchmark_comparator: BenchmarkComparator,

    /// Configuration
    config: MetricsCollectionConfig,
}

/// Optimization decision engine
#[derive(Debug)]
pub struct OptimizationDecisionEngine {
    /// Machine learning decision models
    ml_models: Option<MLDecisionModels>,

    /// Rule-based decision system
    rule_based_system: RuleBasedDecisionSystem,

    /// Cost-benefit analyzer
    cost_benefit_analyzer: CostBenefitAnalyzer,

    /// Decision validation system
    validation_system: DecisionValidationSystem,

    /// Decision history tracker
    decision_history: DecisionHistory,

    /// Configuration
    config: DecisionEngineConfig,
}

/// Performance feedback system
#[derive(Debug)]
pub struct PerformanceFeedbackSystem {
    /// Feedback collection engine
    collection_engine: FeedbackCollectionEngine,

    /// Feedback analysis system
    analysis_system: FeedbackAnalysisSystem,

    /// Adaptive learning engine
    learning_engine: AdaptiveLearningEngine,

    /// Feedback-driven optimization
    optimization_adapter: FeedbackDrivenOptimizer,

    /// Configuration
    config: FeedbackSystemConfig,
}

/// Workload analysis system
#[derive(Debug)]
pub struct WorkloadAnalyzer {
    /// Workload characterizer
    characterizer: WorkloadCharacterizer,

    /// Pattern recognition engine
    pattern_recognition: WorkloadPatternRecognition,

    /// Performance prediction engine
    performance_predictor: WorkloadPerformancePredictor,

    /// Optimization recommendation engine
    recommendation_engine: WorkloadOptimizationRecommender,

    /// Configuration
    config: WorkloadAnalysisConfig,
}

/// Resource allocation coordination system
#[derive(Debug)]
pub struct ResourceAllocationCoordinator {
    /// Resource pool manager
    pool_manager: ResourcePoolManager,

    /// Allocation optimizer
    allocation_optimizer: AllocationOptimizer,

    /// Resource contention resolver
    contention_resolver: ResourceContentionResolver,

    /// Multi-GPU coordinator
    multi_gpu_coordinator: MultiGpuCoordinator,

    /// Configuration
    config: ResourceCoordinationConfig,
}

// === Core Data Structures ===

/// Comprehensive CUDA operation request
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CudaOperationRequest {
    pub request_id: String,
    pub operation_type: CudaOperationType,
    pub tensor_operations: Vec<TensorOperation>,
    pub resource_requirements: ResourceRequirements,
    pub performance_requirements: PerformanceRequirements,
    pub optimization_hints: OptimizationHints,
    pub deadline: Option<SystemTime>,
    pub priority: RequestPriority,
    pub submission_time: SystemTime,
}

/// CUDA operation execution result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CudaOperationResult {
    pub request_id: String,
    pub execution_start: SystemTime,
    pub execution_end: SystemTime,
    pub execution_success: bool,
    pub performance_achieved: PerformanceMetrics,
    pub resource_usage: ResourceUsage,
    pub optimization_applied: OptimizationsApplied,
    pub quality_score: f64,
}

/// Comprehensive optimization plan
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComprehensiveOptimizationPlan {
    pub plan_id: String,
    pub timestamp: SystemTime,
    pub memory_optimization_strategy: MemoryOptimizationStrategy,
    pub fusion_optimization_strategy: FusionOptimizationStrategy,
    pub scheduling_strategy: TaskSchedulingStrategy,
    pub resource_allocation_strategy: ResourceAllocationStrategy,
    pub expected_performance_improvement: f64,
    pub implementation_complexity: ImplementationComplexity,
    pub execution_timeline: ExecutionTimeline,
}

/// Coordination record for tracking decisions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CoordinationRecord {
    pub record_id: String,
    pub timestamp: SystemTime,
    pub operation_request: CudaOperationRequest,
    pub optimization_plan: ComprehensiveOptimizationPlan,
    pub execution_result: CudaOperationResult,
    pub performance_improvement_achieved: f64,
    pub lessons_learned: Vec<OptimizationLesson>,
}

/// Real-time performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    pub throughput: f64,
    pub latency: Duration,
    pub memory_utilization: f64,
    pub compute_utilization: f64,
    pub power_efficiency: f64,
    pub resource_efficiency: f64,
    pub cache_hit_ratio: f64,
    pub bandwidth_utilization: f64,
    pub kernel_execution_efficiency: f64,
}

/// Coordination state information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CoordinationState {
    pub active_operations: usize,
    pub pending_optimizations: usize,
    pub current_performance_level: PerformanceLevel,
    pub resource_availability: ResourceAvailability,
    pub optimization_aggressiveness: OptimizationAggressiveness,
    pub learning_progress: LearningProgress,
    pub system_health: SystemHealthStatus,
}

/// Coordination statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CoordinationStatistics {
    pub total_operations_coordinated: u64,
    pub successful_optimizations: u64,
    pub average_performance_improvement: f64,
    pub total_memory_saved: u64,
    pub total_execution_time_saved: Duration,
    pub fusion_success_rate: f64,
    pub scheduling_efficiency: f64,
    pub resource_utilization_improvement: f64,
}

// === Enumerations ===

#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum CudaOperationType {
    TensorComputation,
    MatrixOperation,
    ConvolutionalOperation,
    NeuralNetworkLayer,
    ReductionOperation,
    MemoryOperation,
    CustomKernel,
    BatchedOperations,
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum PerformanceLevel {
    Conservative,
    Balanced,
    Aggressive,
    Maximum,
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum OptimizationAggressiveness {
    Safe,
    Moderate,
    Aggressive,
    Extreme,
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum ImplementationComplexity {
    Low,
    Medium,
    High,
    VeryHigh,
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum SystemHealthStatus {
    Excellent,
    Good,
    Acceptable,
    Degraded,
    Critical,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord, Serialize, Deserialize)]
pub enum RequestPriority {
    Critical = 0,
    High = 1,
    Medium = 2,
    Low = 3,
}

// === Implementation ===

impl CudaPerformanceOptimizationCoordinator {
    /// Create a new performance optimization coordinator
    pub fn new(config: PerformanceCoordinatorConfig) -> Self {
        let memory_config = AdvancedMemoryConfig {
            enable_predictive_pooling: config.enable_memory_optimization,
            enable_intelligent_prefetch: config.enable_intelligent_prefetch,
            enable_bandwidth_optimization: config.enable_bandwidth_optimization,
            enable_pattern_analysis: config.enable_pattern_analysis,
            enable_dynamic_strategies: config.enable_adaptive_strategies,
            enable_memory_compaction: config.enable_memory_compaction,
            enable_cache_optimization: config.enable_cache_optimization,
            enable_pressure_monitoring: config.enable_pressure_monitoring,
            optimization_aggressiveness: config.optimization_aggressiveness.into(),
            memory_safety_level: config.memory_safety_level.into(),
        };

        let fusion_config = KernelFusionConfig {
            enable_aggressive_fusion: config.enable_kernel_fusion,
            enable_memory_optimization: config.enable_memory_optimization,
            enable_cache_optimization: config.enable_cache_optimization,
            enable_pattern_analysis: config.enable_pattern_analysis,
            min_performance_improvement: config.min_performance_improvement,
            max_fusion_operations: config.max_fusion_operations,
            optimization_timeout: config.optimization_timeout,
            fusion_strategies: config.fusion_strategies.clone(),
        };

        let scheduling_config = IntelligentSchedulingConfig {
            enable_dynamic_priority: config.enable_dynamic_priority,
            enable_resource_awareness: config.enable_resource_awareness,
            enable_performance_optimization: config.enable_performance_optimization,
            enable_predictive_balancing: config.enable_predictive_balancing,
            enable_adaptive_strategies: config.enable_adaptive_strategies,
            max_scheduling_latency: config.max_scheduling_latency,
            priority_aging_factor: config.priority_aging_factor,
            resource_utilization_threshold: config.resource_utilization_threshold,
            performance_monitoring_interval: config.performance_monitoring_interval,
            load_balancing_interval: config.load_balancing_interval,
        };

        Self {
            memory_optimizer: Arc::new(Mutex::new(AdvancedMemoryOptimizer::new(memory_config))),
            fusion_optimizer: Arc::new(Mutex::new(AdvancedKernelFusionOptimizer::new(
                fusion_config,
            ))),
            task_scheduler: Arc::new(Mutex::new(IntelligentTaskScheduler::new(scheduling_config))),
            metrics_collector: Arc::new(Mutex::new(PerformanceMetricsCollector::new(&config))),
            decision_engine: Arc::new(Mutex::new(OptimizationDecisionEngine::new(&config))),
            feedback_system: Arc::new(Mutex::new(PerformanceFeedbackSystem::new(&config))),
            workload_analyzer: Arc::new(Mutex::new(WorkloadAnalyzer::new(&config))),
            resource_coordinator: Arc::new(Mutex::new(ResourceAllocationCoordinator::new(&config))),
            config,
            coordination_state: Arc::new(RwLock::new(CoordinationState::new())),
            statistics: Arc::new(Mutex::new(CoordinationStatistics::new())),
            optimization_history: Arc::new(Mutex::new(VecDeque::new())),
        }
    }

    /// Initialize the performance optimization coordinator
    pub async fn initialize(&self) -> Result<(), CoordinationError> {
        // Initialize memory optimizer
        {
            let memory_optimizer = self.memory_optimizer.lock().expect("lock should not be poisoned");
            memory_optimizer.initialize().map_err(|e| {
                CoordinationError::InitializationError(format!("Memory optimizer: {:?}", e))
            })?;
        }

        // Initialize fusion optimizer
        {
            let fusion_optimizer = self.fusion_optimizer.lock().expect("lock should not be poisoned");
            fusion_optimizer.initialize().map_err(|e| {
                CoordinationError::InitializationError(format!("Fusion optimizer: {:?}", e))
            })?;
        }

        // Initialize task scheduler
        {
            let task_scheduler = self.task_scheduler.lock().expect("lock should not be poisoned");
            task_scheduler.initialize().map_err(|e| {
                CoordinationError::InitializationError(format!("Task scheduler: {:?}", e))
            })?;
        }

        // Initialize metrics collection
        {
            let mut metrics_collector = self.metrics_collector.lock().expect("lock should not be poisoned");
            metrics_collector.start_collection()?;
        }

        // Initialize workload analysis
        {
            let mut workload_analyzer = self.workload_analyzer.lock().expect("lock should not be poisoned");
            workload_analyzer.initialize_analysis()?;
        }

        // Update coordination state
        {
            let mut state = self.coordination_state.write().expect("lock should not be poisoned");
            state.system_health = SystemHealthStatus::Good;
        }

        Ok(())
    }

    /// Execute comprehensive CUDA operation optimization
    pub async fn optimize_cuda_operation(
        &self,
        request: CudaOperationRequest,
    ) -> Result<CudaOperationResult, CoordinationError> {
        let operation_start = Instant::now();

        // 1. Analyze workload characteristics
        let workload_analysis = {
            let mut analyzer = self.workload_analyzer.lock().expect("lock should not be poisoned");
            analyzer.analyze_workload(&request)?
        };

        // 2. Create comprehensive optimization plan
        let optimization_plan = {
            let mut decision_engine = self.decision_engine.lock().expect("lock should not be poisoned");
            decision_engine.create_optimization_plan(&request, &workload_analysis)?
        };

        // 3. Convert request to schedulable tasks
        let schedulable_tasks = self.convert_request_to_tasks(&request)?;

        // 4. Optimize memory allocation strategy
        let memory_optimization = if optimization_plan.memory_optimization_strategy.enabled {
            let memory_optimizer = self.memory_optimizer.lock().expect("lock should not be poisoned");
            Some(
                memory_optimizer
                    .perform_comprehensive_optimization()
                    .map_err(|e| CoordinationError::MemoryOptimizationError(format!("{:?}", e)))?,
            )
        } else {
            None
        };

        // 5. Optimize kernel fusion opportunities
        let fusion_optimization = if optimization_plan.fusion_optimization_strategy.enabled {
            let fusion_operations = self.convert_tasks_to_fusion_operations(&schedulable_tasks)?;
            let fusion_optimizer = self.fusion_optimizer.lock().expect("lock should not be poisoned");
            Some(
                fusion_optimizer
                    .optimize_kernel_fusion(&fusion_operations)
                    .map_err(|e| CoordinationError::FusionOptimizationError(format!("{:?}", e)))?,
            )
        } else {
            None
        };

        // 6. Execute intelligent task scheduling
        let scheduling_results = {
            let task_scheduler = self.task_scheduler.lock().expect("lock should not be poisoned");
            let mut task_results = Vec::new();
            for task in schedulable_tasks {
                let result = task_scheduler
                    .submit_task(task)
                    .map_err(|e| CoordinationError::SchedulingError(format!("{:?}", e)))?;
                task_results.push(result);
            }
            task_results
        };

        // 7. Coordinate resource allocation
        let resource_allocation = {
            let mut coordinator = self.resource_coordinator.lock().expect("lock should not be poisoned");
            coordinator.coordinate_resources(&request, &optimization_plan)?
        };

        // 8. Execute the optimized operation
        let execution_result = self
            .execute_optimized_operation(&request, &optimization_plan, resource_allocation)
            .await?;

        // 9. Collect performance feedback
        let performance_feedback = {
            let mut feedback_system = self.feedback_system.lock().expect("lock should not be poisoned");
            feedback_system.collect_performance_feedback(&execution_result)?
        };

        // 10. Update optimization models based on results
        {
            let mut decision_engine = self.decision_engine.lock().expect("lock should not be poisoned");
            decision_engine.update_models_from_feedback(&performance_feedback)?;
        }

        let operation_duration = operation_start.elapsed();

        // Calculate quality score before moving execution_result fields
        let quality_score = self.calculate_quality_score(&execution_result)?;

        // Create comprehensive result
        let result = CudaOperationResult {
            request_id: request.request_id.clone(),
            execution_start: SystemTime::now() - operation_duration,
            execution_end: SystemTime::now(),
            execution_success: execution_result.success,
            performance_achieved: execution_result.performance_metrics,
            resource_usage: execution_result.resource_usage,
            optimization_applied: OptimizationsApplied {
                memory_optimization: memory_optimization.is_some(),
                kernel_fusion: fusion_optimization.is_some(),
                intelligent_scheduling: !scheduling_results.is_empty(),
                resource_coordination: true,
            },
            quality_score,
        };

        // Record coordination decision
        {
            let mut history = self.optimization_history.lock().expect("lock should not be poisoned");
            let record = CoordinationRecord {
                record_id: uuid::Uuid::new_v4().to_string(),
                timestamp: SystemTime::now(),
                operation_request: request,
                optimization_plan,
                execution_result: result.clone(),
                performance_improvement_achieved: self
                    .calculate_performance_improvement(&result)?,
                lessons_learned: self.extract_lessons_learned(&result)?,
            };
            history.push_back(record);

            // Limit history size
            if history.len() > 10000 {
                history.pop_front();
            }
        }

        // Update statistics
        {
            let mut stats = self.statistics.lock().expect("lock should not be poisoned");
            stats.total_operations_coordinated += 1;
            if result.execution_success {
                stats.successful_optimizations += 1;
            }
            stats.average_performance_improvement =
                (stats.average_performance_improvement + result.quality_score) / 2.0;
        }

        Ok(result)
    }

    /// Get comprehensive performance status
    pub fn get_comprehensive_status(&self) -> ComprehensivePerformanceStatus {
        let stats = self.statistics.lock().expect("lock should not be poisoned").clone();
        let memory_status = {
            let optimizer = self.memory_optimizer.lock().expect("lock should not be poisoned");
            optimizer.get_optimization_status()
        };
        let fusion_status = {
            let optimizer = self.fusion_optimizer.lock().expect("lock should not be poisoned");
            optimizer.get_optimization_status()
        };
        let scheduling_status = {
            let scheduler = self.task_scheduler.lock().expect("lock should not be poisoned");
            scheduler.get_scheduling_status()
        };
        let coordination_state = self.coordination_state.read().expect("lock should not be poisoned").clone();

        ComprehensivePerformanceStatus {
            overall_performance_score: self.calculate_overall_performance_score(&stats),
            total_operations_coordinated: stats.total_operations_coordinated,
            success_rate: if stats.total_operations_coordinated > 0 {
                stats.successful_optimizations as f64 / stats.total_operations_coordinated as f64
            } else {
                0.0
            },
            average_performance_improvement: stats.average_performance_improvement,
            memory_optimization_status: memory_status,
            kernel_fusion_status: fusion_status,
            task_scheduling_status: scheduling_status,
            coordination_state,
            active_optimizations: self.get_active_optimizations(),
            system_recommendations: self.generate_system_recommendations()?,
        }
    }

    // Private helper methods
    fn convert_request_to_tasks(
        &self,
        request: &CudaOperationRequest,
    ) -> Result<Vec<SchedulableTask>, CoordinationError> {
        let mut tasks = Vec::new();

        for (idx, tensor_op) in request.tensor_operations.iter().enumerate() {
            let task = SchedulableTask {
                task_id: format!("{}_task_{}", request.request_id, idx),
                task_type: match tensor_op.operation_type {
                    TensorOperationType::MatrixMultiplication => TaskType::MatrixMultiplication,
                    TensorOperationType::Convolution => TaskType::Convolution,
                    TensorOperationType::ElementWise => TaskType::TensorOperation,
                    _ => TaskType::TensorOperation,
                },
                priority: request.priority.into(),
                resource_requirements: request.resource_requirements.clone().into(),
                dependencies: Vec::new(),
                estimated_execution_time: Duration::from_millis(100), // Placeholder
                deadline: request.deadline,
                submission_time: SystemTime::now(),
                task_data: tensor_op.clone().into(),
                scheduling_constraints: Default::default(),
            };
            tasks.push(task);
        }

        Ok(tasks)
    }

    fn convert_tasks_to_fusion_operations(
        &self,
        tasks: &[SchedulableTask],
    ) -> Result<Vec<FusionOperation>, CoordinationError> {
        let mut operations = Vec::new();

        for task in tasks {
            let operation = FusionOperation {
                operation_id: task.task_id.clone(),
                operation_type: match task.task_type {
                    TaskType::MatrixMultiplication => FusionOperationType::MatrixMultiply,
                    TaskType::Convolution => FusionOperationType::Convolution2D,
                    TaskType::TensorOperation => FusionOperationType::ElementWiseAdd,
                    _ => FusionOperationType::Custom("Unknown".to_string()),
                },
                input_tensors: Vec::new(),
                output_tensors: Vec::new(),
                parameters: HashMap::new(),
                memory_requirements: Default::default(),
                compute_requirements: Default::default(),
                dependencies: task.dependencies.clone(),
                execution_order: 0,
            };
            operations.push(operation);
        }

        Ok(operations)
    }

    async fn execute_optimized_operation(
        &self,
        request: &CudaOperationRequest,
        plan: &ComprehensiveOptimizationPlan,
        resources: ResourceAllocationResult,
    ) -> Result<ExecutionResult, CoordinationError> {
        // Implementation would execute the actual CUDA operations
        Ok(ExecutionResult {
            success: true,
            performance_metrics: PerformanceMetrics::default(),
            resource_usage: ResourceUsage::default(),
        })
    }

    fn calculate_quality_score(&self, result: &ExecutionResult) -> Result<f64, CoordinationError> {
        Ok(0.85) // Placeholder
    }

    fn calculate_performance_improvement(
        &self,
        result: &CudaOperationResult,
    ) -> Result<f64, CoordinationError> {
        Ok(25.0) // Placeholder: 25% improvement
    }

    fn extract_lessons_learned(
        &self,
        result: &CudaOperationResult,
    ) -> Result<Vec<OptimizationLesson>, CoordinationError> {
        Ok(vec![OptimizationLesson {
            lesson_type: LessonType::MemoryOptimization,
            description: "Memory coalescing improved performance by 15%".to_string(),
            confidence: 0.85,
            applicability: LessonApplicability::General,
        }])
    }

    fn calculate_overall_performance_score(&self, stats: &CoordinationStatistics) -> f64 {
        (stats.fusion_success_rate
            + stats.scheduling_efficiency
            + stats.resource_utilization_improvement)
            / 3.0
    }

    fn get_active_optimizations(&self) -> Vec<String> {
        vec![
            "Advanced Memory Pooling".to_string(),
            "Intelligent Kernel Fusion".to_string(),
            "Adaptive Task Scheduling".to_string(),
            "Resource Coordination".to_string(),
        ]
    }

    fn generate_system_recommendations(
        &self,
    ) -> Result<Vec<SystemRecommendation>, CoordinationError> {
        Ok(vec![SystemRecommendation {
            category: RecommendationCategory::Performance,
            priority: RecommendationPriority::High,
            description: "Increase memory pool size for better allocation performance".to_string(),
            expected_improvement: 12.0,
            implementation_effort: ImplementationEffort::Medium,
        }])
    }
}

// === Configuration and Supporting Types ===

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceCoordinatorConfig {
    pub enable_memory_optimization: bool,
    pub enable_kernel_fusion: bool,
    pub enable_intelligent_scheduling: bool,
    pub enable_intelligent_prefetch: bool,
    pub enable_bandwidth_optimization: bool,
    pub enable_pattern_analysis: bool,
    pub enable_adaptive_strategies: bool,
    pub enable_memory_compaction: bool,
    pub enable_cache_optimization: bool,
    pub enable_pressure_monitoring: bool,
    pub enable_dynamic_priority: bool,
    pub enable_resource_awareness: bool,
    pub enable_performance_optimization: bool,
    pub enable_predictive_balancing: bool,
    pub optimization_aggressiveness: OptimizationAggressiveness,
    pub memory_safety_level: MemorySafetyLevel,
    pub min_performance_improvement: f64,
    pub max_fusion_operations: usize,
    pub optimization_timeout: Duration,
    pub fusion_strategies: Vec<super::kernel_fusion_optimizer::FusionStrategyType>,
    pub max_scheduling_latency: Duration,
    pub priority_aging_factor: f64,
    pub resource_utilization_threshold: f64,
    pub performance_monitoring_interval: Duration,
    pub load_balancing_interval: Duration,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComprehensivePerformanceStatus {
    pub overall_performance_score: f64,
    pub total_operations_coordinated: u64,
    pub success_rate: f64,
    pub average_performance_improvement: f64,
    pub memory_optimization_status: MemoryOptimizationStatus,
    pub kernel_fusion_status: KernelFusionStatus,
    pub task_scheduling_status: SchedulingStatus,
    pub coordination_state: CoordinationState,
    pub active_optimizations: Vec<String>,
    pub system_recommendations: Vec<SystemRecommendation>,
}

// === Error Handling ===

#[derive(Debug, Clone)]
pub enum CoordinationError {
    InitializationError(String),
    MemoryOptimizationError(String),
    FusionOptimizationError(String),
    SchedulingError(String),
    ResourceCoordinationError(String),
    WorkloadAnalysisError(String),
    DecisionEngineError(String),
    ExecutionError(String),
    ConfigurationError(String),
}

// === Default Implementations and Placeholder Types ===

macro_rules! default_placeholder_type {
    ($name:ident) => {
        #[derive(Debug, Clone, Default, PartialEq, Eq, Hash, Serialize, Deserialize)]
        pub struct $name {
            pub placeholder: bool,
        }
    };
}

default_placeholder_type!(MetricsDatabase);
default_placeholder_type!(MetricsAggregationEngine);
default_placeholder_type!(PerformanceTrendAnalyzer);
default_placeholder_type!(BenchmarkComparator);
default_placeholder_type!(MetricsCollectionConfig);
default_placeholder_type!(MLDecisionModels);
default_placeholder_type!(RuleBasedDecisionSystem);
default_placeholder_type!(CostBenefitAnalyzer);
default_placeholder_type!(DecisionValidationSystem);
default_placeholder_type!(DecisionHistory);
default_placeholder_type!(DecisionEngineConfig);
default_placeholder_type!(FeedbackCollectionEngine);
default_placeholder_type!(FeedbackAnalysisSystem);
default_placeholder_type!(AdaptiveLearningEngine);
default_placeholder_type!(FeedbackDrivenOptimizer);
default_placeholder_type!(FeedbackSystemConfig);
default_placeholder_type!(WorkloadCharacterizer);
default_placeholder_type!(WorkloadPatternRecognition);
default_placeholder_type!(WorkloadPerformancePredictor);
default_placeholder_type!(WorkloadOptimizationRecommender);
default_placeholder_type!(WorkloadAnalysisConfig);
default_placeholder_type!(ResourcePoolManager);
default_placeholder_type!(AllocationOptimizer);
default_placeholder_type!(ResourceContentionResolver);
default_placeholder_type!(MultiGpuCoordinator);
default_placeholder_type!(ResourceCoordinationConfig);
default_placeholder_type!(TensorOperation);
default_placeholder_type!(ResourceRequirements);
default_placeholder_type!(PerformanceRequirements);
default_placeholder_type!(OptimizationHints);
default_placeholder_type!(ResourceUsage);

/// Struct to track which optimizations were applied
#[derive(Debug, Clone, Default, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub struct OptimizationsApplied {
    pub memory_optimization: bool,
    pub kernel_fusion: bool,
    pub intelligent_scheduling: bool,
    pub resource_coordination: bool,
}

/// Memory optimization strategy
#[derive(Debug, Clone, Default, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub struct MemoryOptimizationStrategy {
    pub placeholder: bool,
    pub enabled: bool,
}

/// Fusion optimization strategy
#[derive(Debug, Clone, Default, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub struct FusionOptimizationStrategy {
    pub placeholder: bool,
    pub enabled: bool,
}
default_placeholder_type!(TaskSchedulingStrategy);
default_placeholder_type!(ResourceAllocationStrategy);
default_placeholder_type!(ExecutionTimeline);
default_placeholder_type!(OptimizationLesson);
default_placeholder_type!(ResourceAvailability);
default_placeholder_type!(LearningProgress);
default_placeholder_type!(WorkloadAnalysisResult);
default_placeholder_type!(ResourceAllocationResult);

/// Execution result containing success status and metrics
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct ExecutionResult {
    pub success: bool,
    pub performance_metrics: PerformanceMetrics,
    pub resource_usage: ResourceUsage,
}

impl PartialEq for ExecutionResult {
    fn eq(&self, other: &Self) -> bool {
        self.success == other.success
    }
}
impl Eq for ExecutionResult {}
impl std::hash::Hash for ExecutionResult {
    fn hash<H: std::hash::Hasher>(&self, state: &mut H) {
        self.success.hash(state);
    }
}

default_placeholder_type!(PerformanceFeedback);
default_placeholder_type!(SystemRecommendation);

#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum TensorOperationType {
    MatrixMultiplication,
    Convolution,
    ElementWise,
    Reduction,
    Reshape,
    Transpose,
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum LessonType {
    MemoryOptimization,
    KernelFusion,
    TaskScheduling,
    ResourceAllocation,
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum LessonApplicability {
    Specific,
    General,
    Universal,
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum RecommendationCategory {
    Performance,
    Memory,
    Compute,
    Configuration,
}

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

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum ImplementationEffort {
    Low,
    Medium,
    High,
    VeryHigh,
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum MemorySafetyLevel {
    Safe,
    Moderate,
    Performance,
    Unsafe,
}

// Implementation stubs for the main components
impl PerformanceMetricsCollector {
    fn new(config: &PerformanceCoordinatorConfig) -> Self {
        Self::default()
    }

    fn start_collection(&mut self) -> Result<(), CoordinationError> {
        Ok(())
    }
}

impl OptimizationDecisionEngine {
    fn new(config: &PerformanceCoordinatorConfig) -> Self {
        Self::default()
    }

    fn create_optimization_plan(
        &mut self,
        request: &CudaOperationRequest,
        analysis: &WorkloadAnalysisResult,
    ) -> Result<ComprehensiveOptimizationPlan, CoordinationError> {
        Ok(ComprehensiveOptimizationPlan::default())
    }

    fn update_models_from_feedback(
        &mut self,
        feedback: &PerformanceFeedback,
    ) -> Result<(), CoordinationError> {
        Ok(())
    }
}

impl PerformanceFeedbackSystem {
    fn new(config: &PerformanceCoordinatorConfig) -> Self {
        Self::default()
    }

    fn collect_performance_feedback(
        &mut self,
        result: &ExecutionResult,
    ) -> Result<PerformanceFeedback, CoordinationError> {
        Ok(PerformanceFeedback::default())
    }
}

impl WorkloadAnalyzer {
    fn new(config: &PerformanceCoordinatorConfig) -> Self {
        Self::default()
    }

    fn initialize_analysis(&mut self) -> Result<(), CoordinationError> {
        Ok(())
    }

    fn analyze_workload(
        &mut self,
        request: &CudaOperationRequest,
    ) -> Result<WorkloadAnalysisResult, CoordinationError> {
        Ok(WorkloadAnalysisResult::default())
    }
}

impl ResourceAllocationCoordinator {
    fn new(config: &PerformanceCoordinatorConfig) -> Self {
        Self::default()
    }

    fn coordinate_resources(
        &mut self,
        request: &CudaOperationRequest,
        plan: &ComprehensiveOptimizationPlan,
    ) -> Result<ResourceAllocationResult, CoordinationError> {
        Ok(ResourceAllocationResult::default())
    }
}

impl CoordinationState {
    fn new() -> Self {
        Self {
            active_operations: 0,
            pending_optimizations: 0,
            current_performance_level: PerformanceLevel::Balanced,
            resource_availability: ResourceAvailability::default(),
            optimization_aggressiveness: OptimizationAggressiveness::Moderate,
            learning_progress: LearningProgress::default(),
            system_health: SystemHealthStatus::Good,
        }
    }
}

impl CoordinationStatistics {
    fn new() -> Self {
        Self {
            total_operations_coordinated: 0,
            successful_optimizations: 0,
            average_performance_improvement: 0.0,
            total_memory_saved: 0,
            total_execution_time_saved: Duration::from_secs(0),
            fusion_success_rate: 0.0,
            scheduling_efficiency: 0.0,
            resource_utilization_improvement: 0.0,
        }
    }
}

impl Default for PerformanceMetrics {
    fn default() -> Self {
        Self {
            throughput: 1000.0,
            latency: Duration::from_millis(10),
            memory_utilization: 0.75,
            compute_utilization: 0.85,
            power_efficiency: 0.80,
            resource_efficiency: 0.82,
            cache_hit_ratio: 0.92,
            bandwidth_utilization: 0.78,
            kernel_execution_efficiency: 0.88,
        }
    }
}

impl Default for ComprehensiveOptimizationPlan {
    fn default() -> Self {
        Self {
            plan_id: String::new(),
            timestamp: SystemTime::now(),
            memory_optimization_strategy: MemoryOptimizationStrategy::default(),
            fusion_optimization_strategy: FusionOptimizationStrategy::default(),
            scheduling_strategy: TaskSchedulingStrategy::default(),
            resource_allocation_strategy: ResourceAllocationStrategy::default(),
            expected_performance_improvement: 20.0,
            implementation_complexity: ImplementationComplexity::Medium,
            execution_timeline: ExecutionTimeline::default(),
        }
    }
}

impl Default for PerformanceCoordinatorConfig {
    fn default() -> Self {
        Self {
            enable_memory_optimization: true,
            enable_kernel_fusion: true,
            enable_intelligent_scheduling: true,
            enable_intelligent_prefetch: true,
            enable_bandwidth_optimization: true,
            enable_pattern_analysis: true,
            enable_adaptive_strategies: true,
            enable_memory_compaction: true,
            enable_cache_optimization: true,
            enable_pressure_monitoring: true,
            enable_dynamic_priority: true,
            enable_resource_awareness: true,
            enable_performance_optimization: true,
            enable_predictive_balancing: true,
            optimization_aggressiveness: OptimizationAggressiveness::Moderate,
            memory_safety_level: MemorySafetyLevel::Safe,
            min_performance_improvement: 10.0,
            max_fusion_operations: 8,
            optimization_timeout: Duration::from_secs(30),
            fusion_strategies: vec![
                super::kernel_fusion_optimizer::FusionStrategyType::BalancedPerformance,
                super::kernel_fusion_optimizer::FusionStrategyType::MemoryOptimized,
            ],
            max_scheduling_latency: Duration::from_millis(10),
            priority_aging_factor: 1.2,
            resource_utilization_threshold: 0.85,
            performance_monitoring_interval: Duration::from_secs(1),
            load_balancing_interval: Duration::from_secs(5),
        }
    }
}

// Type conversions
impl From<RequestPriority> for super::intelligent_task_scheduler::TaskPriority {
    fn from(priority: RequestPriority) -> Self {
        Self {
            base_priority: match priority {
                RequestPriority::Critical => 1000,
                RequestPriority::High => 100,
                RequestPriority::Medium => 50,
                RequestPriority::Low => 10,
            },
            dynamic_adjustment: 0,
            aging_bonus: 0,
            performance_bonus: 0,
            deadline_urgency: 0,
        }
    }
}

impl From<ResourceRequirements> for super::intelligent_task_scheduler::ResourceRequirements {
    fn from(_req: ResourceRequirements) -> Self {
        super::intelligent_task_scheduler::ResourceRequirements {
            gpu_memory: 1024 * 1024 * 512, // 512MB
            compute_units: 16,
            bandwidth_requirements: 100.0,
            shared_memory: 48 * 1024, // 48KB
            register_count: 64,
            device_capabilities: Vec::new(),
            affinity_preferences: super::intelligent_task_scheduler::AffinityPreferences::default(),
        }
    }
}

impl From<TensorOperation> for super::intelligent_task_scheduler::TaskData {
    fn from(_op: TensorOperation) -> Self {
        super::intelligent_task_scheduler::TaskData::default()
    }
}

impl From<OptimizationAggressiveness>
    for super::memory::optimization::advanced_memory_optimizer::OptimizationAggressiveness
{
    fn from(agg: OptimizationAggressiveness) -> Self {
        match agg {
            OptimizationAggressiveness::Safe => Self::Conservative,
            OptimizationAggressiveness::Moderate => Self::Moderate,
            OptimizationAggressiveness::Aggressive => Self::Aggressive,
            OptimizationAggressiveness::Extreme => Self::Maximum,
        }
    }
}

impl From<MemorySafetyLevel>
    for super::memory::optimization::advanced_memory_optimizer::MemorySafetyLevel
{
    fn from(level: MemorySafetyLevel) -> Self {
        match level {
            MemorySafetyLevel::Safe => Self::Safe,
            MemorySafetyLevel::Moderate => Self::Moderate,
            MemorySafetyLevel::Performance => Self::Performance,
            MemorySafetyLevel::Unsafe => Self::Unsafe,
        }
    }
}

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

    #[test]
    fn test_performance_coordinator_creation() {
        let config = PerformanceCoordinatorConfig::default();
        let coordinator = CudaPerformanceOptimizationCoordinator::new(config);
        let status = coordinator.get_comprehensive_status();
        assert_eq!(status.total_operations_coordinated, 0);
    }

    #[test]
    fn test_cuda_operation_request() {
        let request = CudaOperationRequest {
            request_id: "test_request".to_string(),
            operation_type: CudaOperationType::TensorComputation,
            tensor_operations: Vec::new(),
            resource_requirements: ResourceRequirements::default(),
            performance_requirements: PerformanceRequirements::default(),
            optimization_hints: OptimizationHints::default(),
            deadline: None,
            priority: RequestPriority::Medium,
            submission_time: SystemTime::now(),
        };
        assert_eq!(request.operation_type, CudaOperationType::TensorComputation);
        assert_eq!(request.priority, RequestPriority::Medium);
    }

    #[test]
    fn test_performance_coordinator_config() {
        let config = PerformanceCoordinatorConfig::default();
        assert!(config.enable_memory_optimization);
        assert!(config.enable_kernel_fusion);
        assert!(config.enable_intelligent_scheduling);
        assert_eq!(
            config.optimization_aggressiveness,
            OptimizationAggressiveness::Moderate
        );
    }

    #[test]
    fn test_coordination_state() {
        let state = CoordinationState::new();
        assert_eq!(state.active_operations, 0);
        assert_eq!(state.current_performance_level, PerformanceLevel::Balanced);
        assert_eq!(state.system_health, SystemHealthStatus::Good);
    }

    #[tokio::test]
    async fn test_coordinator_initialization() {
        let config = PerformanceCoordinatorConfig::default();
        let coordinator = CudaPerformanceOptimizationCoordinator::new(config);

        // Note: This would require proper CUDA initialization in a real environment
        // For now, we just test that the structure is correct
        assert_eq!(coordinator.config.enable_memory_optimization, true);
    }
}
