//! advanced Advanced Parallel Processing System
//!
//! Next-generation parallel processing framework with machine learning-based
//! optimization, predictive work scheduling, advanced memory-aware parallelization,
//! cross-NUMA optimization, and real-time performance adaptation for maximum
//! efficiency on large-scale statistical computing workloads.

use crate::error::StatsResult;
use scirs2_core::ndarray::{Array2, ArrayBase, Data, Ix1};
use scirs2_core::numeric::{Float, NumCast, Zero};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, RwLock};
use std::time::{Duration, Instant, SystemTime};

/// advanced Parallel Configuration with ML-based Optimization
#[derive(Debug, Clone)]
pub struct AdvancedParallelConfig {
    /// Enable machine learning-based optimization
    pub enable_ml_optimization: bool,
    /// Enable predictive work scheduling
    pub enable_predictive_scheduling: bool,
    /// Enable cross-NUMA optimization
    pub enable_cross_numa_optimization: bool,
    /// Enable real-time performance monitoring
    pub enable_realtime_monitoring: bool,
    /// Memory awareness level
    pub memory_awareness_level: MemoryAwarenessLevel,
    /// Thread pool management strategy
    pub thread_pool_strategy: ThreadPoolStrategy,
    /// Performance prediction model
    pub prediction_model: PredictionModelType,
    /// Load balancing intelligence level
    pub load_balancing_intelligence: LoadBalancingIntelligence,
    /// NUMA topology awareness
    pub numa_topology_awareness: NumaTopologyAwareness,
}

impl Default for AdvancedParallelConfig {
    fn default() -> Self {
        Self {
            enable_ml_optimization: true,
            enable_predictive_scheduling: true,
            enable_cross_numa_optimization: true,
            enable_realtime_monitoring: true,
            memory_awareness_level: MemoryAwarenessLevel::Advanced,
            thread_pool_strategy: ThreadPoolStrategy::Adaptive,
            prediction_model: PredictionModelType::LinearRegression,
            load_balancing_intelligence: LoadBalancingIntelligence::MachineLearning,
            numa_topology_awareness: NumaTopologyAwareness::Full,
        }
    }
}

/// Memory awareness levels for parallel operations
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum MemoryAwarenessLevel {
    Basic,    // Basic memory size checks
    Standard, // Memory bandwidth awareness
    Advanced, // Cache hierarchy awareness
    Expert,   // Full memory subsystem optimization
}

/// Thread pool management strategies
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum ThreadPoolStrategy {
    Static,           // Fixed thread count
    Dynamic,          // Dynamic thread scaling
    Adaptive,         // ML-based adaptive scaling
    WorkStealing,     // Work-stealing thread pool
    HierarchicalNuma, // NUMA-hierarchical thread management
}

/// Prediction model types for performance optimization
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum PredictionModelType {
    LinearRegression,
    PolynomialRegression,
    RandomForest,
    NeuralNetwork,
    EnsembleModel,
}

/// Load balancing intelligence levels
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum LoadBalancingIntelligence {
    Static,                // Fixed chunk sizes
    Heuristic,             // Rule-based adaptation
    MachineLearning,       // ML-based optimization
    ReinforcementLearning, // RL-based continuous improvement
}

/// NUMA topology awareness levels
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum NumaTopologyAwareness {
    None,     // No NUMA awareness
    Basic,    // Basic NUMA node detection
    Advanced, // NUMA distance matrix awareness
    Full,     // Complete memory locality optimization
}

/// advanced Parallel Processor with Advanced Intelligence
pub struct AdvancedParallelProcessor {
    config: AdvancedParallelConfig,
    performance_predictor: Arc<RwLock<PerformancePredictor>>,
    load_balancer: Arc<RwLock<IntelligentLoadBalancer>>,
    numa_optimizer: Arc<RwLock<NumaOptimizer>>,
    memory_manager: Arc<RwLock<AdvancedMemoryManager>>,
    performance_monitor: Arc<RwLock<RealTimePerformanceMonitor>>,
    thread_pool_manager: Arc<RwLock<ThreadPoolManager>>,
}

impl AdvancedParallelProcessor {
    /// Create new advanced parallel processor
    pub fn new(config: AdvancedParallelConfig) -> Self {
        let numa_topology = detect_numa_topology();
        let memory_hierarchy = detect_memory_hierarchy();

        Self {
            performance_predictor: Arc::new(RwLock::new(PerformancePredictor::new(&_config))),
            load_balancer: Arc::new(RwLock::new(IntelligentLoadBalancer::new(
                &_config,
                &numa_topology,
            ))),
            numa_optimizer: Arc::new(RwLock::new(NumaOptimizer::new(numa_topology))),
            memory_manager: Arc::new(RwLock::new(AdvancedMemoryManager::new(
                &_config,
                memory_hierarchy,
            ))),
            performance_monitor: Arc::new(RwLock::new(RealTimePerformanceMonitor::new())),
            thread_pool_manager: Arc::new(RwLock::new(ThreadPoolManager::new(&_config))),
            config,
        }
    }

    /// Advanced-optimized parallel statistical operations
    pub fn advanced_parallel_statistics<F, D>(
        &self,
        data: &ArrayBase<D, Ix1>,
        operations: &[StatisticalOperation],
    ) -> StatsResult<AdvancedParallelStatisticsResult<F>>
    where
        F: Float + NumCast + Send + Sync + Copy + 'static,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        let start_time = Instant::now();
        let data_characteristics = self.analyzedata_characteristics(data);

        // Predict optimal execution strategy
        let execution_strategy = if self.config.enable_ml_optimization {
            self.performance_predictor
                .read()
                .unwrap()
                .predict_optimal_strategy(&data_characteristics, operations)?
        } else {
            ExecutionStrategy::default()
        };

        // Optimize memory layout and NUMA placement
        let memory_layout = if self.config.enable_cross_numa_optimization {
            self.numa_optimizer
                .read()
                .unwrap()
                .optimizedata_placement(&data_characteristics)?
        } else {
            MemoryLayout::default()
        };

        // Configure intelligent load balancing
        let load_balancing_config = self
            .load_balancer
            .read()
            .unwrap()
            .generate_load_balancing_config(&execution_strategy, &data_characteristics)?;

        // Execute parallel operations with real-time monitoring
        let result = self.execute_parallel_operations(
            data,
            operations,
            &execution_strategy,
            &memory_layout,
            &load_balancing_config,
        )?;

        let execution_time = start_time.elapsed();

        // Update performance models with execution results
        if self.config.enable_ml_optimization {
            self.update_performance_models(
                &data_characteristics,
                &execution_strategy,
                execution_time,
                &result.performance_metrics,
            )?;
        }

        Ok(result)
    }

    /// Advanced parallel matrix operations with cross-NUMA optimization
    pub fn advanced_parallel_matrix_operations<F>(
        &self,
        matrices: &[Array2<F>],
        operation: MatrixOperationType,
    ) -> StatsResult<AdvancedParallelMatrixResult<F>>
    where
        F: Float + NumCast + Send + Sync + Copy + 'static
        + std::fmt::Display,
    {
        let start_time = Instant::now();

        // Analyze matrix characteristics
        let matrix_characteristics = self.analyze_matrix_characteristics(matrices);

        // Predict optimal execution strategy for matrices
        let execution_strategy = if self.config.enable_ml_optimization {
            self.performance_predictor
                .read()
                .unwrap()
                .predict_matrix_strategy(&matrix_characteristics, &operation)?
        } else {
            MatrixExecutionStrategy::default()
        };

        // Optimize matrix memory layout across NUMA nodes
        let numa_layout = if self.config.enable_cross_numa_optimization {
            self.numa_optimizer
                .read()
                .unwrap()
                .optimize_matrix_placement(&matrix_characteristics)?
        } else {
            NumaMatrixLayout::default()
        };

        // Execute matrix operations with advanced parallelization
        let result = self.execute_parallel_matrix_operations(
            matrices,
            &operation,
            &execution_strategy,
            &numa_layout,
        )?;

        let execution_time = start_time.elapsed();

        // Update matrix operation performance models
        if self.config.enable_ml_optimization {
            self.update_matrix_performance_models(
                &matrix_characteristics,
                &execution_strategy,
                execution_time,
                &result.performance_metrics,
            )?;
        }

        Ok(result)
    }

    /// Intelligent streaming parallel processing
    pub fn advanced_parallel_streaming<F, D>(
        &self,
        data_stream: &mut dyn Iterator<Item = ArrayBase<D, Ix1>>,
        windowsize: usize,
        operations: &[StreamingOperation],
    ) -> StatsResult<AdvancedParallelStreamingResult<F>>
    where
        F: Float + NumCast + Send + Sync + Copy + 'static,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        let mut streaming_buffer = StreamingBuffer::new(windowsize);
        let mut results = Vec::new();
        let start_time = Instant::now();

        // Initialize streaming performance predictor
        let mut streaming_predictor = StreamingPerformancePredictor::new(&self.config);

        for (chunk_index, chunk) in data_stream.enumerate() {
            streaming_buffer.push(chunk);

            if streaming_buffer.is_ready() {
                // Predict optimal processing strategy for current chunk
                let chunk_characteristics = self.analyze_chunk_characteristics(&streaming_buffer);
                let processing_strategy = streaming_predictor
                    .predict_chunk_strategy(&chunk_characteristics, operations)?;

                // Process chunk with optimized parallelization
                let chunk_result = self.process_streaming_chunk(
                    &streaming_buffer,
                    operations,
                    &processing_strategy,
                )?;

                results.push(chunk_result);

                // Update streaming performance model
                streaming_predictor.update_model(&chunk_characteristics, &processing_strategy);
            }
        }

        let total_execution_time = start_time.elapsed();

        Ok(AdvancedParallelStreamingResult {
            chunk_results: results,
            total_execution_time,
            streaming_efficiency: streaming_predictor.calculate_efficiency(),
            adaptive_improvements: streaming_predictor.get_improvements(),
        })
    }

    /// Batch processing with predictive optimization
    pub fn advanced_parallel_batch_processing<F, D>(
        &self,
        batches: &[ArrayBase<D, Ix1>],
        operations: &[BatchOperation],
    ) -> StatsResult<AdvancedParallelBatchResult<F>>
    where
        F: Float + NumCast + Send + Sync + Copy + 'static,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        let start_time = Instant::now();

        // Analyze batch characteristics
        let batch_characteristics = self.analyze_batch_characteristics(batches);

        // Predict optimal batch processing strategy
        let batch_strategy = if self.config.enable_predictive_scheduling {
            self.performance_predictor
                .read()
                .unwrap()
                .predict_batch_strategy(&batch_characteristics, operations)?
        } else {
            BatchProcessingStrategy::default()
        };

        // Schedule batches across NUMA nodes
        let numa_schedule = if self.config.enable_cross_numa_optimization {
            self.numa_optimizer
                .read()
                .unwrap()
                .schedule_batches(&batch_characteristics, &batch_strategy)?
        } else {
            NumaBatchSchedule::default()
        };

        // Execute batch processing with real-time monitoring
        let results =
            self.execute_batch_processing(batches, operations, &batch_strategy, &numa_schedule)?;

        let execution_time = start_time.elapsed();

        // Update batch processing performance models
        if self.config.enable_ml_optimization {
            self.update_batch_performance_models(
                &batch_characteristics,
                &batch_strategy,
                execution_time,
                &results,
            )?;
        }

        Ok(AdvancedParallelBatchResult {
            batch_results: results,
            execution_time,
            parallel_efficiency: self.calculate_batch_efficiency(&results, execution_time),
            numa_efficiency: self.calculate_numa_efficiency(&numa_schedule),
            adaptive_recommendations: self.generate_adaptive_recommendations(&results),
        })
    }

    // Helper methods for data analysis and characteristics detection

    fn analyzedata_characteristics<F, D>(&self, data: &ArrayBase<D, Ix1>) -> DataCharacteristics
    where
        F: Float + NumCast + Send + Sync + Copy,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        DataCharacteristics {
            size: data.len(),
            memory_footprint: data.len() * std::mem::size_of::<F>(),
            data_distribution: self.detectdata_distribution(data),
            access_pattern: AccessPattern::Sequential, // Default for 1D arrays
            cache_efficiency_estimate: self.estimate_cache_efficiency(data.len()),
            numa_locality_potential: self.estimate_numa_locality(data.len()),
        }
    }

    fn detectdata_distribution<F, D>(&self, data: &ArrayBase<D, Ix1>) -> DataDistribution
    where
        F: Float + NumCast + Send + Sync + Copy,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        // Simplified distribution detection
        if data.len() < 100 {
            return DataDistribution::Unknown;
        }

        // Calculate basic statistics to infer distribution
        let mean = data.iter().fold(F::zero(), |acc, &x| acc + x) / F::from(data.len()).unwrap();
        let variance = data
            .iter()
            .map(|&x| {
                let diff = x - mean;
                diff * diff
            })
            .fold(F::zero(), |acc, x| acc + x)
            / F::from(data.len() - 1).unwrap();

        if variance < F::from(0.1).unwrap() {
            DataDistribution::LowVariance
        } else if variance > F::from(10.0).unwrap() {
            DataDistribution::HighVariance
        } else {
            DataDistribution::Normal
        }
    }

    fn estimate_cache_efficiency(&self, datasize: usize) -> f64 {
        // Simplified cache efficiency estimation based on data size
        let l1_cachesize = 32 * 1024; // 32KB typical L1 cache
        let l2_cachesize = 256 * 1024; // 256KB typical L2 cache
        let l3_cachesize = 8 * 1024 * 1024; // 8MB typical L3 cache

        if datasize * 8 <= l1_cachesize {
            0.95 // Excellent L1 cache fit
        } else if datasize * 8 <= l2_cachesize {
            0.85 // Good L2 cache fit
        } else if datasize * 8 <= l3_cachesize {
            0.70 // Reasonable L3 cache fit
        } else {
            0.40 // Poor cache efficiency
        }
    }

    fn estimate_numa_locality(&self, datasize: usize) -> f64 {
        // Estimate NUMA locality potential based on data size
        let numa_node_memory = 64 * 1024 * 1024 * 1024; // 64GB per NUMA node typical

        if datasize * 8 <= numa_node_memory {
            0.90 // Excellent NUMA locality potential
        } else {
            0.50 // May require cross-NUMA access
        }
    }

    // Placeholder implementations for complex analysis methods

    fn analyze_matrix_characteristics<F>(&self, matrices: &[Array2<F>]) -> MatrixCharacteristics
    where
        F: Float + NumCast + Send + Sync + Copy
        + std::fmt::Display,
    {
        MatrixCharacteristics {
            total_elements: matrices.iter().map(|m| m.len()).sum(),
            max_dimensions: matrices
                .iter()
                .map(|m| (m.nrows(), m.ncols()))
                .max_by_key(|(r, c)| r * c)
                .unwrap_or((0, 0)),
            memory_pattern: MemoryPattern::RowMajor, // Default for ndarray
            sparsity_estimate: 0.0,                  // Assume dense matrices
            numerical_stability: NumericalStability::Good,
        }
    }

    fn analyze_chunk_characteristics<D>(&self, buffer: &StreamingBuffer<D>) -> ChunkCharacteristics
    where
        D: Send + Sync,
    {
        ChunkCharacteristics {
            chunksize: buffer.currentsize(),
            temporal_locality: 0.8, // Assume good temporal locality in streaming
            processing_complexity: ProcessingComplexity::Medium,
            memory_requirements: buffer.memory_footprint(),
        }
    }

    fn analyze_batch_characteristics<F, D>(
        &self,
        batches: &[ArrayBase<D, Ix1>],
    ) -> BatchCharacteristics
    where
        F: Float + NumCast + Send + Sync + Copy,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        BatchCharacteristics {
            batch_count: batches.len(),
            total_elements: batches.iter().map(|b| b.len()).sum(),
            size_variance: self.calculate_batchsize_variance(batches),
            memory_distribution: MemoryDistribution::Uniform, // Simplified
            interdependency: BatchInterdependency::Independent,
        }
    }

    fn calculate_batchsize_variance<F, D>(&self, batches: &[ArrayBase<D, Ix1>]) -> f64
    where
        F: Float + NumCast + Send + Sync + Copy,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        if batches.is_empty() {
            return 0.0;
        }

        let sizes: Vec<f64> = batches.iter().map(|b| b.len() as f64).collect();
        let mean = sizes.iter().sum::<f64>() / sizes.len() as f64;
        let variance =
            sizes.iter().map(|&size| (size - mean).powi(2)).sum::<f64>() / sizes.len() as f64;

        variance.sqrt() / mean // Coefficient of variation
    }

    // Placeholder execution methods

    fn execute_parallel_operations<F, D>(
        &self, &ArrayBase<D, Ix1>, _operations: &[StatisticalOperation], _strategy: &ExecutionStrategy_memory, layout: &MemoryLayout_load, _config: &LoadBalancingConfig,
    ) -> StatsResult<AdvancedParallelStatisticsResult<F>>
    where
        F: Float + NumCast + Send + Sync + Copy,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        // Placeholder implementation
        Ok(AdvancedParallelStatisticsResult {
            statistics: HashMap::new(),
            performance_metrics: PerformanceMetrics::default(),
            execution_strategy_used: ExecutionStrategy::default(),
            numa_efficiency: 0.85,
        })
    }

    fn execute_parallel_matrix_operations<F>(
        &self, _metrics: &[Array2<F>], _operation: &MatrixOperationType, strategy: &MatrixExecutionStrategy_numa, _layout: &NumaMatrixLayout,
    ) -> StatsResult<AdvancedParallelMatrixResult<F>>
    where
        F: Float + NumCast + Send + Sync + Copy
        + std::fmt::Display,
    {
        // Placeholder implementation
        Ok(AdvancedParallelMatrixResult {
            result_matrices: Vec::new(),
            performance_metrics: MatrixPerformanceMetrics::default(),
            numa_layout_efficiency: 0.90,
            memory_bandwidth_utilization: 0.75,
        })
    }

    fn process_streaming_chunk<F, D>(
        &self, &StreamingBuffer<D>, _operations: &[StreamingOperation], _strategy: &StreamingProcessingStrategy,
    ) -> StatsResult<StreamingChunkResult<F>>
    where
        F: Float + NumCast + Send + Sync + Copy,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        // Placeholder implementation
        Ok(StreamingChunkResult {
            chunk_statistics: HashMap::new(),
            processing_time: Duration::from_millis(10),
            memory_efficiency: 0.80,
        })
    }

    fn execute_batch_processing<F, D>(
        &self, _metrics: &[ArrayBase<D, Ix1>], _operations: &[BatchOperation], _strategy: &BatchProcessingStrategy_numa, schedule: &NumaBatchSchedule,
    ) -> StatsResult<Vec<BatchProcessingResult<F>>>
    where
        F: Float + NumCast + Send + Sync + Copy,
        D: Data<Elem = F> + Sync
        + std::fmt::Display,
    {
        // Placeholder implementation
        Ok(vec![BatchProcessingResult {
            batch_id: 0,
            results: HashMap::new(),
            execution_time: Duration::from_millis(5),
            numa_node_used: 0,
        }])
    }

    // Performance update methods

    fn update_performance_models(
        &self, &DataCharacteristics, _strategy: &ExecutionStrategy_execution, time: Duration, _metrics: &PerformanceMetrics,
    ) -> StatsResult<()> {
        // Placeholder for ML model updates
        Ok(())
    }

    fn update_matrix_performance_models(
        &self, &MatrixCharacteristics, _strategy: &MatrixExecutionStrategy_execution, time: Duration, _metrics: &MatrixPerformanceMetrics,
    ) -> StatsResult<()> {
        // Placeholder for matrix ML model updates
        Ok(())
    }

    fn update_batch_performance_models(
        &self, &BatchCharacteristics, _strategy: &BatchProcessingStrategy_execution, time: Duration, _results: &[BatchProcessingResult<f64>],
    ) -> StatsResult<()> {
        // Placeholder for batch ML model updates
        Ok(())
    }

    // Efficiency calculation methods

    fn calculate_batch_efficiency(
        &self, _metrics: &[BatchProcessingResult<f64>], _total_time: Duration,
    ) -> f64 {
        0.85 // Placeholder
    }

    fn calculate_numa_efficiency(&self, &NumaBatchSchedule) -> f64 {
        0.90 // Placeholder
    }

    fn generate_adaptive_recommendations(
        &self, _metrics: &[BatchProcessingResult<f64>],
    ) -> Vec<AdaptiveRecommendation> {
        vec![] // Placeholder
    }
}

// Supporting structures and types

#[derive(Debug, Clone)]
pub struct DataCharacteristics {
    pub size: usize,
    pub memory_footprint: usize,
    pub data_distribution: DataDistribution,
    pub access_pattern: AccessPattern,
    pub cache_efficiency_estimate: f64,
    pub numa_locality_potential: f64,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum DataDistribution {
    Unknown,
    Normal,
    LowVariance,
    HighVariance,
    Sparse,
    Dense,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum AccessPattern {
    Sequential,
    Random,
    Strided(usize),
    Blocked,
}

#[derive(Debug, Clone)]
pub struct MatrixCharacteristics {
    pub total_elements: usize,
    pub max_dimensions: (usize, usize),
    pub memory_pattern: MemoryPattern,
    pub sparsity_estimate: f64,
    pub numerical_stability: NumericalStability,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum MemoryPattern {
    RowMajor,
    ColumnMajor,
    Blocked,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum NumericalStability {
    Excellent,
    Good,
    Fair,
    Poor,
}

#[derive(Debug, Clone)]
pub struct ChunkCharacteristics {
    pub chunksize: usize,
    pub temporal_locality: f64,
    pub processing_complexity: ProcessingComplexity,
    pub memory_requirements: usize,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum ProcessingComplexity {
    Low,
    Medium,
    High,
    VeryHigh,
}

#[derive(Debug, Clone)]
pub struct BatchCharacteristics {
    pub batch_count: usize,
    pub total_elements: usize,
    pub size_variance: f64,
    pub memory_distribution: MemoryDistribution,
    pub interdependency: BatchInterdependency,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum MemoryDistribution {
    Uniform,
    Skewed,
    Bimodal,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum BatchInterdependency {
    Independent,
    Sequential,
    Hierarchical,
}

// Operation types

#[derive(Debug, Clone, PartialEq)]
pub enum StatisticalOperation {
    Mean,
    Variance,
    StandardDeviation,
    Correlation,
    Covariance,
    Quantiles(Vec<f64>),
    Moments(usize),
}

#[derive(Debug, Clone, PartialEq)]
pub enum MatrixOperationType {
    Multiplication,
    Decomposition,
    Eigenvalues,
    Inversion,
    Transpose,
}

#[derive(Debug, Clone, PartialEq)]
pub enum StreamingOperation {
    MovingAverage(usize),
    MovingVariance(usize),
    TrendDetection,
    AnomalyDetection,
}

#[derive(Debug, Clone, PartialEq)]
pub enum BatchOperation {
    CrossValidation,
    BootstrapResampling,
    PermutationTest,
    MonteCarloSimulation,
}

// Strategy and configuration types

#[derive(Debug, Clone)]
pub struct ExecutionStrategy {
    pub thread_count: usize,
    pub chunksize: usize,
    pub memory_strategy: MemoryStrategy,
    pub load_balancing: LoadBalancingStrategy,
}

impl Default for ExecutionStrategy {
    fn default() -> Self {
        Self {
            thread_count: num_threads(),
            chunksize: 1000,
            memory_strategy: MemoryStrategy::Standard,
            load_balancing: LoadBalancingStrategy::Dynamic,
        }
    }
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum MemoryStrategy {
    Standard,
    CacheOptimized,
    NumaOptimized,
    MemoryMapped,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum LoadBalancingStrategy {
    Static,
    Dynamic,
    WorkStealing,
    Guided,
}

#[derive(Debug, Clone)]
pub struct MemoryLayout {
    pub numa_placement: NumaPlacement,
    pub cache_optimization: CacheOptimization,
    pub alignment: usize,
}

impl Default for MemoryLayout {
    fn default() -> Self {
        Self {
            numa_placement: NumaPlacement::FirstAvailable,
            cache_optimization: CacheOptimization::None,
            alignment: 64, // Cache line alignment
        }
    }
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum NumaPlacement {
    FirstAvailable,
    RoundRobin,
    LocalFirst,
    Optimized,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum CacheOptimization {
    None,
    L1Blocking,
    L2Blocking,
    L3Blocking,
    Hierarchical,
}

#[derive(Debug, Clone)]
pub struct LoadBalancingConfig {
    pub strategy: LoadBalancingStrategy,
    pub chunksize_min: usize,
    pub chunksize_max: usize,
    pub load_threshold: f64,
}

#[derive(Debug, Clone)]
pub struct MatrixExecutionStrategy {
    pub blocking_strategy: MatrixBlockingStrategy,
    pub numa_strategy: MatrixNumaStrategy,
    pub parallelization_level: ParallelizationLevel,
}

impl Default for MatrixExecutionStrategy {
    fn default() -> Self {
        Self {
            blocking_strategy: MatrixBlockingStrategy::Adaptive,
            numa_strategy: MatrixNumaStrategy::Balanced,
            parallelization_level: ParallelizationLevel::Medium,
        }
    }
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum MatrixBlockingStrategy {
    None,
    Fixed(usize),
    Adaptive,
    Hierarchical,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum MatrixNumaStrategy {
    LocalOnly,
    Balanced,
    CrossNuma,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum ParallelizationLevel {
    Low,
    Medium,
    High,
    Maximum,
}

#[derive(Debug, Clone)]
pub struct NumaMatrixLayout {
    pub node_distribution: Vec<usize>,
    pub memory_bandwidth_optimization: bool,
    pub cache_coherency_optimization: bool,
}

impl Default for NumaMatrixLayout {
    fn default() -> Self {
        Self {
            node_distribution: vec![0], // Single NUMA node
            memory_bandwidth_optimization: true,
            cache_coherency_optimization: true,
        }
    }
}

#[derive(Debug, Clone)]
pub struct StreamingProcessingStrategy {
    pub buffer_strategy: BufferStrategy,
    pub processing_overlap: bool,
    pub prefetch_strategy: PrefetchStrategy,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum BufferStrategy {
    SingleBuffer,
    DoubleBuffer,
    RingBuffer,
    AdaptiveBuffer,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum PrefetchStrategy {
    None,
    Sequential,
    Predictive,
    Adaptive,
}

#[derive(Debug, Clone)]
pub struct BatchProcessingStrategy {
    pub scheduling_strategy: BatchSchedulingStrategy,
    pub memory_pooling: bool,
    pub cross_batch_optimization: bool,
}

impl Default for BatchProcessingStrategy {
    fn default() -> Self {
        Self {
            scheduling_strategy: BatchSchedulingStrategy::FIFO,
            memory_pooling: true,
            cross_batch_optimization: false,
        }
    }
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum BatchSchedulingStrategy {
    FIFO,
    SJF, // Shortest Job First
    Priority,
    Adaptive,
}

#[derive(Debug, Clone)]
pub struct NumaBatchSchedule {
    pub node_assignments: HashMap<usize, usize>, // batch_id -> numa_node
    pub load_balancing: NumaLoadBalancing,
    pub memory_locality_score: f64,
}

impl Default for NumaBatchSchedule {
    fn default() -> Self {
        Self {
            node_assignments: HashMap::new(),
            load_balancing: NumaLoadBalancing::RoundRobin,
            memory_locality_score: 0.8,
        }
    }
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum NumaLoadBalancing {
    RoundRobin,
    LoadBased,
    MemoryBased,
    Adaptive,
}

// Result types

#[derive(Debug, Clone)]
pub struct AdvancedParallelStatisticsResult<F> {
    pub statistics: HashMap<String, F>,
    pub performance_metrics: PerformanceMetrics,
    pub execution_strategy_used: ExecutionStrategy,
    pub numa_efficiency: f64,
}

#[derive(Debug, Clone)]
pub struct AdvancedParallelMatrixResult<F> {
    pub result_matrices: Vec<Array2<F>>,
    pub performance_metrics: MatrixPerformanceMetrics,
    pub numa_layout_efficiency: f64,
    pub memory_bandwidth_utilization: f64,
}

#[derive(Debug, Clone)]
pub struct AdvancedParallelStreamingResult<F> {
    pub chunk_results: Vec<StreamingChunkResult<F>>,
    pub total_execution_time: Duration,
    pub streaming_efficiency: f64,
    pub adaptive_improvements: Vec<StreamingImprovement>,
}

#[derive(Debug, Clone)]
pub struct AdvancedParallelBatchResult<F> {
    pub batch_results: Vec<BatchProcessingResult<F>>,
    pub execution_time: Duration,
    pub parallel_efficiency: f64,
    pub numa_efficiency: f64,
    pub adaptive_recommendations: Vec<AdaptiveRecommendation>,
}

#[derive(Debug, Clone)]
pub struct StreamingChunkResult<F> {
    pub chunk_statistics: HashMap<String, F>,
    pub processing_time: Duration,
    pub memory_efficiency: f64,
}

#[derive(Debug, Clone)]
pub struct BatchProcessingResult<F> {
    pub batch_id: usize,
    pub results: HashMap<String, F>,
    pub execution_time: Duration,
    pub numa_node_used: usize,
}

#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    pub throughput: f64,
    pub latency: Duration,
    pub memory_usage: usize,
    pub cache_hit_ratio: f64,
    pub numa_efficiency: f64,
}

impl Default for PerformanceMetrics {
    fn default() -> Self {
        Self {
            throughput: 0.0,
            latency: Duration::from_millis(0),
            memory_usage: 0,
            cache_hit_ratio: 0.8,
            numa_efficiency: 0.9,
        }
    }
}

#[derive(Debug, Clone)]
pub struct MatrixPerformanceMetrics {
    pub flops: f64,
    pub memory_bandwidth_utilization: f64,
    pub cache_efficiency: f64,
    pub numa_communication_overhead: f64,
}

impl Default for MatrixPerformanceMetrics {
    fn default() -> Self {
        Self {
            flops: 0.0,
            memory_bandwidth_utilization: 0.7,
            cache_efficiency: 0.8,
            numa_communication_overhead: 0.1,
        }
    }
}

#[derive(Debug, Clone)]
pub struct StreamingImprovement {
    pub improvement_type: ImprovementType,
    pub description: String,
    pub expected_benefit: f64,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum ImprovementType {
    BufferOptimization,
    ThreadPoolAdjustment,
    MemoryLayout,
    PrefetchStrategy,
}

#[derive(Debug, Clone)]
pub struct AdaptiveRecommendation {
    pub recommendation: String,
    pub confidence: f64,
    pub expected_improvement: f64,
    pub implementation_complexity: ImplementationComplexity,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum ImplementationComplexity {
    Low,
    Medium,
    High,
    Expert,
}

// Performance optimization components

pub struct PerformancePredictor {
    models: HashMap<String, PredictionModel>,
    feature_cache: HashMap<String, Vec<f64>>,
}

impl PerformancePredictor {
    pub fn new(config: &AdvancedParallelConfig) -> Self {
        Self {
            models: HashMap::new(),
            feature_cache: HashMap::new(),
        }
    }

    pub fn predict_optimal_strategy(
        &self, &DataCharacteristics, _operations: &[StatisticalOperation],
    ) -> StatsResult<ExecutionStrategy> {
        // Placeholder implementation
        Ok(ExecutionStrategy::default())
    }

    pub fn predict_matrix_strategy(
        &self, &MatrixCharacteristics, _operation: &MatrixOperationType,
    ) -> StatsResult<MatrixExecutionStrategy> {
        // Placeholder implementation
        Ok(MatrixExecutionStrategy::default())
    }

    pub fn predict_batch_strategy(
        &self, &BatchCharacteristics, _operations: &[BatchOperation],
    ) -> StatsResult<BatchProcessingStrategy> {
        // Placeholder implementation
        Ok(BatchProcessingStrategy::default())
    }
}

#[derive(Debug, Clone)]
pub struct PredictionModel {
    pub model_type: PredictionModelType,
    pub accuracy: f64,
    pub last_updated: SystemTime,
}

pub struct IntelligentLoadBalancer {
    strategy: LoadBalancingIntelligence,
    numa_topology: NumaTopology,
    performance_history: VecDeque<LoadBalancingMetrics>,
}

impl IntelligentLoadBalancer {
    pub fn new(_config: &AdvancedParallelConfig, numatopology: &NumaTopology) -> Self {
        Self {
            strategy: LoadBalancingIntelligence::MachineLearning,
            numa_topology: numa_topology.clone(),
            performance_history: VecDeque::new(),
        }
    }

    pub fn generate_load_balancing_config(
        &self, &ExecutionStrategy, _characteristics: &DataCharacteristics,
    ) -> StatsResult<LoadBalancingConfig> {
        // Placeholder implementation
        Ok(LoadBalancingConfig {
            _strategy: LoadBalancingStrategy::Dynamic,
            chunksize_min: 100,
            chunksize_max: 10000,
            load_threshold: 0.8,
        })
    }
}

#[derive(Debug, Clone)]
pub struct LoadBalancingMetrics {
    pub efficiency: f64,
    pub load_variance: f64,
    pub communication_overhead: f64,
}

pub struct NumaOptimizer {
    topology: NumaTopology,
    placement_history: HashMap<String, NumaPlacementResult>,
}

impl NumaOptimizer {
    pub fn new(topology: NumaTopology) -> Self {
        Self {
            topology,
            placement_history: HashMap::new(),
        }
    }

    pub fn optimizedata_placement(
        &self, &DataCharacteristics,
    ) -> StatsResult<MemoryLayout> {
        // Placeholder implementation
        Ok(MemoryLayout::default())
    }

    pub fn optimize_matrix_placement(
        &self, &MatrixCharacteristics,
    ) -> StatsResult<NumaMatrixLayout> {
        // Placeholder implementation
        Ok(NumaMatrixLayout::default())
    }

    pub fn schedule_batches(
        &self, &BatchCharacteristics, _strategy: &BatchProcessingStrategy,
    ) -> StatsResult<NumaBatchSchedule> {
        // Placeholder implementation
        Ok(NumaBatchSchedule::default())
    }
}

#[derive(Debug, Clone)]
pub struct NumaTopology {
    pub nodes: Vec<NumaNode>,
    pub distance_matrix: Array2<u32>,
    pub memory_bandwidth: HashMap<usize, f64>,
}

#[derive(Debug, Clone)]
pub struct NumaNode {
    pub id: usize,
    pub cpu_cores: Vec<usize>,
    pub memorysize_gb: f64,
    pub local_bandwidth_gbps: f64,
}

#[derive(Debug, Clone)]
pub struct NumaPlacementResult {
    pub efficiency: f64,
    pub memory_locality: f64,
    pub communication_overhead: f64,
}

pub struct AdvancedMemoryManager {
    memory_hierarchy: MemoryHierarchy,
    allocation_strategy: AllocationStrategy,
    usage_tracking: HashMap<String, MemoryUsageMetrics>,
}

impl AdvancedMemoryManager {
    pub fn new(_config: &AdvancedParallelConfig, memoryhierarchy: MemoryHierarchy) -> Self {
        Self {
            memory_hierarchy,
            allocation_strategy: AllocationStrategy::NumaLocal,
            usage_tracking: HashMap::new(),
        }
    }
}

#[derive(Debug, Clone)]
pub struct MemoryHierarchy {
    pub l1_cache_kb: usize,
    pub l2_cache_kb: usize,
    pub l3_cache_mb: usize,
    pub memory_channels: usize,
    pub memory_bandwidth_gbps: f64,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum AllocationStrategy {
    Standard,
    NumaLocal,
    NumaInterleaved,
    HugePages,
    MemoryMapped,
}

#[derive(Debug, Clone)]
pub struct MemoryUsageMetrics {
    pub allocated_bytes: usize,
    pub peak_usage: usize,
    pub cache_efficiency: f64,
    pub numa_distribution: HashMap<usize, usize>,
}

pub struct RealTimePerformanceMonitor {
    metrics_history: VecDeque<RealTimeMetrics>,
    alert_thresholds: PerformanceThresholds,
}

impl RealTimePerformanceMonitor {
    pub fn new() -> Self {
        Self {
            metrics_history: VecDeque::new(),
            alert_thresholds: PerformanceThresholds::default(),
        }
    }
}

#[derive(Debug, Clone)]
pub struct RealTimeMetrics {
    pub timestamp: Instant,
    pub cpu_utilization: f64,
    pub memory_utilization: f64,
    pub cache_hit_ratio: f64,
    pub numa_balance: f64,
}

#[derive(Debug, Clone)]
pub struct PerformanceThresholds {
    pub cpu_warning: f64,
    pub memory_warning: f64,
    pub cache_warning: f64,
    pub numa_warning: f64,
}

impl Default for PerformanceThresholds {
    fn default() -> Self {
        Self {
            cpu_warning: 0.85,
            memory_warning: 0.90,
            cache_warning: 0.50,
            numa_warning: 0.60,
        }
    }
}

pub struct ThreadPoolManager {
    strategy: ThreadPoolStrategy,
    active_pools: HashMap<String, ThreadPool>,
    performance_metrics: HashMap<String, ThreadPoolMetrics>,
}

impl ThreadPoolManager {
    pub fn new(config: &AdvancedParallelConfig) -> Self {
        Self {
            strategy: ThreadPoolStrategy::Adaptive,
            active_pools: HashMap::new(),
            performance_metrics: HashMap::new(),
        }
    }
}

#[derive(Debug, Clone)]
pub struct ThreadPool {
    pub thread_count: usize,
    pub work_queuesize: usize,
    pub numa_affinity: Option<usize>,
}

#[derive(Debug, Clone)]
pub struct ThreadPoolMetrics {
    pub utilization: f64,
    pub queue_length: usize,
    pub throughput: f64,
    pub context_switches: usize,
}

pub struct StreamingPerformancePredictor {
    prediction_model: StreamingPredictionModel,
    adaptation_history: VecDeque<StreamingAdaptation>,
}

impl StreamingPerformancePredictor {
    pub fn new(config: &AdvancedParallelConfig) -> Self {
        Self {
            prediction_model: StreamingPredictionModel::default(),
            adaptation_history: VecDeque::new(),
        }
    }

    pub fn predict_chunk_strategy(
        &self, &ChunkCharacteristics, _operations: &[StreamingOperation],
    ) -> StatsResult<StreamingProcessingStrategy> {
        // Placeholder implementation
        Ok(StreamingProcessingStrategy {
            buffer_strategy: BufferStrategy::DoubleBuffer,
            processing_overlap: true,
            prefetch_strategy: PrefetchStrategy::Sequential,
        })
    }

    pub fn update_model(
        &mut self_characteristics: &ChunkCharacteristics, _strategy: &StreamingProcessingStrategy,
    ) {
        // Placeholder for model updates
    }

    pub fn calculate_efficiency(&self) -> f64 {
        0.85 // Placeholder
    }

    pub fn get_improvements(&self) -> Vec<StreamingImprovement> {
        vec![] // Placeholder
    }
}

#[derive(Debug, Clone)]
pub struct StreamingPredictionModel {
    pub throughput_predictor: ThroughputPredictor,
    pub latency_predictor: LatencyPredictor,
    pub buffer_optimizer: BufferOptimizer,
}

impl Default for StreamingPredictionModel {
    fn default() -> Self {
        Self {
            throughput_predictor: ThroughputPredictor::default(),
            latency_predictor: LatencyPredictor::default(),
            buffer_optimizer: BufferOptimizer::default(),
        }
    }
}

#[derive(Debug, Clone)]
pub struct ThroughputPredictor {
    pub model_coefficients: Vec<f64>,
    pub accuracy: f64,
}

impl Default for ThroughputPredictor {
    fn default() -> Self {
        Self {
            model_coefficients: vec![1.0, 0.5, 0.2],
            accuracy: 0.85,
        }
    }
}

#[derive(Debug, Clone)]
pub struct LatencyPredictor {
    pub model_coefficients: Vec<f64>,
    pub accuracy: f64,
}

impl Default for LatencyPredictor {
    fn default() -> Self {
        Self {
            model_coefficients: vec![0.1, 0.05, 0.02],
            accuracy: 0.80,
        }
    }
}

#[derive(Debug, Clone)]
pub struct BufferOptimizer {
    pub optimalsizes: HashMap<String, usize>,
    pub performance_history: VecDeque<BufferPerformance>,
}

impl Default for BufferOptimizer {
    fn default() -> Self {
        Self {
            optimalsizes: HashMap::new(),
            performance_history: VecDeque::new(),
        }
    }
}

#[derive(Debug, Clone)]
pub struct BufferPerformance {
    pub buffersize: usize,
    pub throughput: f64,
    pub memory_efficiency: f64,
}

#[derive(Debug, Clone)]
pub struct StreamingAdaptation {
    pub adaptation_type: AdaptationType,
    pub performance_improvement: f64,
    pub timestamp: Instant,
}

#[derive(Debug, Clone, Copy, PartialEq)]
pub enum AdaptationType {
    BufferSizeIncrease,
    BufferSizeDecrease,
    ThreadCountAdjustment,
    PrefetchOptimization,
}

pub struct StreamingBuffer<D> {
    data: VecDeque<D>,
    maxsize: usize,
    current_memory_footprint: usize,
}

impl<D> StreamingBuffer<D> {
    pub fn new(_maxsize: usize) -> Self {
        Self {
            data: VecDeque::with_capacity(_maxsize),
            maxsize,
            current_memory_footprint: 0,
        }
    }

    pub fn push(&mut self, item: D) {
        if self.data.len() >= self.maxsize {
            self.data.pop_front();
        }
        self.data.push_back(item);
    }

    pub fn is_ready(&self) -> bool {
        self.data.len() >= self.maxsize
    }

    pub fn currentsize(&self) -> usize {
        self.data.len()
    }

    pub fn memory_footprint(&self) -> usize {
        self.current_memory_footprint
    }
}

// System detection functions

#[allow(dead_code)]
fn detect_numa_topology() -> NumaTopology {
    // Placeholder implementation - in reality would query system NUMA topology
    NumaTopology {
        nodes: vec![NumaNode {
            id: 0,
            cpu_cores: (0..8).collect(),
            memorysize_gb: 64.0,
            local_bandwidth_gbps: 100.0,
        }],
        distance_matrix: Array2::eye(1),
        memory_bandwidth: [(0, 100.0)].iter().cloned().collect(),
    }
}

#[allow(dead_code)]
fn detect_memory_hierarchy() -> MemoryHierarchy {
    // Placeholder implementation - in reality would query system memory hierarchy
    MemoryHierarchy {
        l1_cache_kb: 32,
        l2_cache_kb: 256,
        l3_cache_mb: 8,
        memory_channels: 4,
        memory_bandwidth_gbps: 100.0,
    }
}

// Factory functions for easy creation

/// Create default advanced parallel processor
#[allow(dead_code)]
pub fn create_advanced_think_parallel_processor() -> AdvancedParallelProcessor {
    AdvancedParallelProcessor::new(AdvancedParallelConfig::default())
}

/// Create configured advanced parallel processor
#[allow(dead_code)]
pub fn create_configured_advanced_think_parallel_processor(
    config: AdvancedParallelConfig,
) -> AdvancedParallelProcessor {
    AdvancedParallelProcessor::new(config)
}

/// Create high-performance parallel processor optimized for large datasets
#[allow(dead_code)]
pub fn create_largedataset_parallel_processor() -> AdvancedParallelProcessor {
    let config = AdvancedParallelConfig {
        enable_ml_optimization: true,
        enable_predictive_scheduling: true,
        enable_cross_numa_optimization: true,
        enable_realtime_monitoring: true,
        memory_awareness_level: MemoryAwarenessLevel::Expert,
        thread_pool_strategy: ThreadPoolStrategy::HierarchicalNuma,
        prediction_model: PredictionModelType::EnsembleModel,
        load_balancing_intelligence: LoadBalancingIntelligence::ReinforcementLearning,
        numa_topology_awareness: NumaTopologyAwareness::Full,
    };
    AdvancedParallelProcessor::new(config)
}

/// Create streaming-optimized parallel processor
#[allow(dead_code)]
pub fn create_streaming_parallel_processor() -> AdvancedParallelProcessor {
    let config = AdvancedParallelConfig {
        enable_ml_optimization: true,
        enable_predictive_scheduling: true,
        enable_cross_numa_optimization: false, // Less important for streaming
        enable_realtime_monitoring: true,
        memory_awareness_level: MemoryAwarenessLevel::Advanced,
        thread_pool_strategy: ThreadPoolStrategy::WorkStealing,
        prediction_model: PredictionModelType::NeuralNetwork,
        load_balancing_intelligence: LoadBalancingIntelligence::MachineLearning,
        numa_topology_awareness: NumaTopologyAwareness::Basic,
    };
    AdvancedParallelProcessor::new(config)
}

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

    #[test]
    #[ignore = "timeout"]
    fn test_advanced_think_parallel_processor_creation() {
        let processor = create_advanced_think_parallel_processor();
        assert!(processor.config.enable_ml_optimization);
    }

    #[test]
    fn testdata_characteristics_analysis() {
        let processor = create_advanced_think_parallel_processor();
        let data = Array1::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0]);
        let characteristics = processor.analyzedata_characteristics(&data.view());

        assert_eq!(characteristics.size, 5);
        assert!(characteristics.cache_efficiency_estimate > 0.8);
    }

    #[test]
    #[ignore = "timeout"]
    fn test_batchsize_variance_calculation() {
        let processor = create_advanced_think_parallel_processor();
        let batch1 = Array1::from_vec(vec![1.0, 2.0, 3.0]);
        let batch2 = Array1::from_vec(vec![4.0, 5.0, 6.0, 7.0]);
        let batch3 = Array1::from_vec(vec![8.0, 9.0]);

        let batches = vec![batch1.view(), batch2.view(), batch3.view()];
        let variance = processor.calculate_batchsize_variance(&batches);

        assert!(variance > 0.0);
    }

    #[test]
    #[ignore = "timeout"]
    fn test_memory_hierarchy_detection() {
        let hierarchy = detect_memory_hierarchy();
        assert!(hierarchy.l1_cache_kb > 0);
        assert!(hierarchy.l2_cache_kb > hierarchy.l1_cache_kb);
        assert!(hierarchy.l3_cache_mb * 1024 > hierarchy.l2_cache_kb);
    }

    #[test]
    #[ignore = "timeout"]
    fn test_numa_topology_detection() {
        let topology = detect_numa_topology();
        assert!(!topology.nodes.is_empty());
        assert!(!topology.nodes[0].cpu_cores.is_empty());
    }

    #[test]
    fn testdata_distribution_detection() {
        let processor = create_advanced_think_parallel_processor();

        // Test low variance data
        let low_vardata = Array1::from_vec(vec![1.0; 100]);
        let distribution = processor.detectdata_distribution(&low_vardata.view());
        assert_eq!(distribution, DataDistribution::LowVariance);

        // Test normal variance data
        let normaldata = Array1::from_vec((0..100).map(|i| i as f64).collect());
        let distribution = processor.detectdata_distribution(&normaldata.view());
        assert_eq!(distribution, DataDistribution::HighVariance);
    }

    #[test]
    #[ignore = "timeout"]
    fn test_cache_efficiency_estimation() {
        let processor = create_advanced_think_parallel_processor();

        // Small data should have high cache efficiency
        let small_efficiency = processor.estimate_cache_efficiency(100);
        assert!(small_efficiency > 0.9);

        // Large data should have lower cache efficiency
        let large_efficiency = processor.estimate_cache_efficiency(10_000_000);
        assert!(large_efficiency < 0.7);
    }

    #[test]
    #[ignore = "timeout"]
    fn test_streaming_buffer() {
        let mut buffer = StreamingBuffer::new(3);
        assert!(!buffer.is_ready());

        buffer.push(1);
        buffer.push(2);
        assert!(!buffer.is_ready());

        buffer.push(3);
        assert!(buffer.is_ready());
        assert_eq!(buffer.currentsize(), 3);

        buffer.push(4);
        assert_eq!(buffer.currentsize(), 3); // Should maintain max size
    }

    #[test]
    #[ignore = "timeout"]
    fn test_specialized_processor_creation() {
        let largedataset_processor = create_largedataset_parallel_processor();
        assert_eq!(
            largedataset_processor.config.memory_awareness_level,
            MemoryAwarenessLevel::Expert
        );

        let streaming_processor = create_streaming_parallel_processor();
        assert_eq!(
            streaming_processor.config.thread_pool_strategy,
            ThreadPoolStrategy::WorkStealing
        );
    }
}
