scirs2-core 0.4.2

//! Advanced Tensor Cores and Automatic Kernel Tuning Framework
//!
//! This module provides AI-driven optimization and adaptive management for tensor cores
//! and automatic kernel tuning in Advanced mode, enabling intelligent performance
//! optimization across diverse GPU architectures and workloads.
//!
//! # Features
//!
//! - **AI-Driven Optimization**: Machine learning models for performance prediction and optimization
//! - **Adaptive Kernel Tuning**: Real-time adaptation based on workload characteristics
//! - **Multi-Architecture Support**: Unified interface for NVIDIA, AMD, Apple, and other GPU architectures
//! - **Performance Analytics**: Comprehensive monitoring and performance profiling
//! - **Intelligent Caching**: Smart caching of optimized configurations with predictive prefetching
//! - **Real-time Learning**: Continuous improvement from execution feedback
//! - **Advanced Scheduling**: Workload-aware resource allocation and scheduling
//! - **Energy Optimization**: Power-efficient computing with dynamic voltage and frequency scaling
//!
//! **Note**: This module requires the `gpu` feature to be enabled.

use crate::error::{CoreError, CoreResult};

#[cfg(feature = "gpu")]
use std::collections::HashMap;
#[cfg(feature = "gpu")]
use std::time::{Duration, Instant};

#[cfg(feature = "gpu")]
use crate::gpu::{
    auto_tuning::{
        AutoTuner, KernelParameters, PerformanceMetrics, TuningResult, TuningSpace, TuningStrategy,
    },
    tensor_cores::{TensorCoreConfig, TensorCoreManager, TensorDataType, TensorOperation},
    GpuBackend, GpuContext,
};
#[cfg(feature = "gpu")]
use std::sync::{Arc, Mutex, RwLock};

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

// Module declarations
#[cfg(feature = "gpu")]
pub mod caching;
#[cfg(feature = "gpu")]
pub mod hardware;
#[cfg(feature = "gpu")]
pub mod monitoring;
#[cfg(feature = "gpu")]
pub mod operations;
#[cfg(feature = "gpu")]
pub mod optimization;

// Re-exports from submodules
#[cfg(feature = "gpu")]
pub use caching::*;
#[cfg(feature = "gpu")]
pub use hardware::*;
#[cfg(feature = "gpu")]
pub use monitoring::*;
#[cfg(feature = "gpu")]
pub use operations::*;
#[cfg(feature = "gpu")]
pub use optimization::*;

// Core types and shared structures
#[cfg(feature = "gpu")]
/// Resource utilization tracking
#[allow(dead_code)]
#[derive(Debug, Clone, Default)]
pub struct ResourceUtilization {
    /// Compute utilization
    pub compute_utilization: HashMap<GpuBackend, f64>,
    /// Memory utilization
    pub memory_utilization: HashMap<GpuBackend, f64>,
    /// Bandwidth utilization
    pub bandwidth_utilization: HashMap<GpuBackend, f64>,
    /// Power utilization
    pub power_utilization: HashMap<GpuBackend, f64>,
}

#[cfg(feature = "gpu")]
/// Performance data point for learning
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct PerformanceDataPoint {
    /// Workload feature vector
    pub workload_features: Vec<f64>,
    /// Hardware configuration
    pub hardware_config: String,
    /// Optimization parameters used
    pub optimization_params: HashMap<String, f64>,
    /// Achieved performance
    pub performance: PerformanceMetrics,
    /// Timestamp
    pub timestamp: Instant,
    /// Whether optimization was successful
    pub success: bool,
}

#[cfg(feature = "gpu")]
/// Types of machine learning models
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum ModelType {
    SVM,
    RandomForest,
    NeuralNetwork,
    NaiveBayes,
    KMeans,
    DBSCAN,
}

#[cfg(feature = "gpu")]
/// Learning progress tracking
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct LearningProgress {
    /// Total learning iterations
    pub total_iterations: usize,
    /// Successful optimizations
    pub successful_optimizations: usize,
    /// Failed optimizations
    pub failed_optimizations: usize,
    /// Average improvement
    pub average_improvement: f64,
    /// Best performance achieved
    pub best_performance: f64,
}

/// Trend directions
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum TrendDirection {
    Increasing,
    Decreasing,
    Stable,
    Oscillating,
    Unknown,
}

#[cfg(feature = "gpu")]
/// Comprehensive tensor core analytics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct TensorCoreAnalytics {
    /// Performance statistics
    pub performance_stats: PerformanceStatistics,
    /// Optimization effectiveness
    pub optimization_effectiveness: f64,
    /// Cache performance
    pub cache_performance: CacheAnalytics,
    /// Energy efficiency metrics
    pub energy_efficiency: EnergyEfficiencyMetrics,
    /// Learning progress
    pub learning_progress: LearningProgress,
    /// Recommendations
    pub recommendations: Vec<OptimizationRecommendation>,
}

#[cfg(feature = "gpu")]
/// Performance statistics summary
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct PerformanceStatistics {
    /// Average execution time
    pub avg_execution_time: Duration,
    /// Throughput statistics
    pub throughput_stats: ThroughputStatistics,
    /// Memory utilization
    pub memory_utilization: f64,
    /// GPU utilization
    pub gpu_utilization: f64,
    /// Error rates
    pub error_rates: HashMap<String, f64>,
}

#[cfg(feature = "gpu")]
/// Throughput statistics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ThroughputStatistics {
    /// Mean throughput
    pub mean: f64,
    /// Standard deviation
    pub std_dev: f64,
    /// 95th percentile
    pub p95: f64,
    /// 99th percentile
    pub p99: f64,
    /// Maximum throughput
    pub max: f64,
}

/// Energy efficiency metrics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct EnergyEfficiencyMetrics {
    /// Operations per joule
    pub operations_per_joule: f64,
    /// Performance per watt
    pub performance_per_watt: f64,
    /// Energy trend direction
    pub energy_trend: TrendDirection,
    /// Carbon footprint estimation
    pub carbon_footprint_grams: f64,
}

/// Optimization recommendation
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct OptimizationRecommendation {
    /// Recommendation type
    pub recommendation_type: RecommendationType,
    /// Description
    pub description: String,
    /// Expected improvement
    pub expected_improvement: f64,
    /// Implementation complexity
    pub complexity: ComplexityLevel,
    /// Priority score
    pub priority: f64,
}

/// Types of optimization recommendations
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum RecommendationType {
    CacheOptimization,
    MemoryOptimization,
    ComputeOptimization,
    EnergyOptimization,
    SchedulingOptimization,
}

/// Complexity levels for recommendations
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum ComplexityLevel {
    Low,
    Medium,
    High,
}

#[cfg(feature = "gpu")]
/// Energy optimization result
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct EnergyOptimizationResult {
    /// Original power consumption
    pub original_power_watts: f64,
    /// Optimized power consumption
    pub optimized_power_watts: f64,
    /// Power savings
    pub power_savings_watts: f64,
    /// Energy efficiency improvement
    pub efficiency_improvement: f64,
    /// Power information
    pub power_info: crate::advanced_tensor_cores::monitoring::PowerInformation,
}

// The main implementation when GPU feature is enabled
#[cfg(feature = "gpu")]
mod gpu_implementation {
    use super::*;
    use crate::gpu::tensor_cores::TensorCoreOp;

    /// Central coordinator for advanced tensor cores and kernel tuning
    #[allow(dead_code)]
    #[derive(Debug)]
    pub struct AdvancedTensorCoreCoordinator {
        /// Tensor core managers for different backends
        pub tensor_managers: Arc<RwLock<HashMap<GpuBackend, TensorCoreManager>>>,
        /// Auto-tuners for different backends
        pub auto_tuners: Arc<RwLock<HashMap<GpuBackend, AutoTuner>>>,
        /// AI optimization engine
        pub ai_optimizer: Arc<Mutex<AIOptimizationEngine>>,
        /// Performance predictor
        pub performance_predictor: Arc<RwLock<PerformancePredictor>>,
        /// Adaptive scheduler
        pub adaptive_scheduler: Arc<Mutex<AdaptiveScheduler>>,
        /// Smart cache system
        pub smart_cache: Arc<Mutex<SmartCacheSystem>>,
        /// Real-time analytics
        pub analytics_engine: Arc<Mutex<RealTimeAnalytics>>,
        /// Configuration
        pub config: AdvancedTensorConfig,
        /// Monitoring system
        pub monitoring: Arc<RwLock<TensorCoreMonitoring>>,
    }

    /// Configuration for advanced tensor core operations
    #[allow(dead_code)]
    #[cfg_attr(feature = "serialization", derive(Serialize, Deserialize))]
    #[derive(Debug, Clone)]
    pub struct AdvancedTensorConfig {
        /// Enable AI-driven optimization
        pub enable_ai_optimization: bool,
        /// Enable adaptive kernel tuning
        pub enable_adaptive_tuning: bool,
        /// Enable real-time learning
        pub enable_real_time_learning: bool,
        /// Enable performance prediction
        pub enable_performance_prediction: bool,
        /// Enable energy optimization
        pub enable_energy_optimization: bool,
        /// Maximum learning iterations
        pub max_learning_iterations: usize,
        /// Performance improvement threshold
        pub performance_threshold: f64,
        /// Cache size limit (GB)
        pub cache_size_limit_gb: f64,
        /// Analytics collection interval (seconds)
        pub analytics_interval_seconds: u64,
        /// Enable cross-architecture optimization
        pub enable_cross_arch_optimization: bool,
        /// Enable dynamic voltage and frequency scaling
        pub enable_dvfs: bool,
    }

    impl Default for AdvancedTensorConfig {
        fn default() -> Self {
            Self {
                enable_ai_optimization: true,
                enable_adaptive_tuning: true,
                enable_real_time_learning: true,
                enable_performance_prediction: true,
                enable_energy_optimization: true,
                max_learning_iterations: 1000,
                performance_threshold: 0.05,
                cache_size_limit_gb: 4.0,
                analytics_interval_seconds: 60,
                enable_cross_arch_optimization: true,
                enable_dvfs: true,
            }
        }
    }

    impl AdvancedTensorCoreCoordinator {
        /// Create a new advanced tensor core coordinator
        pub fn new(config: AdvancedTensorConfig) -> CoreResult<Self> {
            let tensor_managers = Arc::new(RwLock::new(HashMap::new()));
            let auto_tuners = Arc::new(RwLock::new(HashMap::new()));
            let ai_optimizer = Arc::new(Mutex::new(AIOptimizationEngine::new()?));
            let performance_predictor = Arc::new(RwLock::new(PerformancePredictor::new()?));
            let adaptive_scheduler = Arc::new(Mutex::new(AdaptiveScheduler::new()?));
            let smart_cache = Arc::new(Mutex::new(SmartCacheSystem::new()?));
            let analytics_engine = Arc::new(Mutex::new(RealTimeAnalytics::new()?));
            let monitoring = Arc::new(RwLock::new(TensorCoreMonitoring::new()?));

            Ok(Self {
                tensor_managers,
                auto_tuners,
                ai_optimizer,
                performance_predictor,
                adaptive_scheduler,
                smart_cache,
                analytics_engine,
                config,
                monitoring,
            })
        }

        /// Initialize tensor cores for a specific GPU backend
        pub fn initialize_backend(&self, backend: GpuBackend) -> CoreResult<()> {
            // Initialize tensor core manager
            let tensor_manager = TensorCoreManager::new(backend).map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to initialize tensor core manager: {e}"
                )))
            })?;

            // Initialize auto-tuner
            let tuning_strategy = TuningStrategy::default();
            let auto_tuner = AutoTuner::new(backend, tuning_strategy).map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to initialize auto-tuner: {e}"
                )))
            })?;

            // Store managers
            self.tensor_managers
                .write()
                .map_err(|e| {
                    CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                        "Failed to acquire tensor managers lock: {e}"
                    )))
                })?
                .insert(backend, tensor_manager);

            self.auto_tuners
                .write()
                .map_err(|e| {
                    CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                        "Failed to acquire auto-tuners lock: {e}"
                    )))
                })?
                .insert(backend, auto_tuner);

            // Initialize monitoring for this backend
            self.initialize_monitoring(backend)?;

            Ok(())
        }

        /// Optimize tensor operation with AI-driven approach
        pub fn optimize_tensor_operation(
            &self,
            operation: &TensorOperation,
            gpu_context: &GpuContext,
        ) -> CoreResult<OptimizedTensorOperation> {
            // Get tensor core manager
            let tensor_managers = self.tensor_managers.read().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire tensor managers lock: {e}"
                )))
            })?;

            let backend = gpu_context.backend();
            let tensor_manager = tensor_managers.get(&backend).ok_or_else(|| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Tensor core manager not found for backend: {backend:?}"
                )))
            })?;

            // Check smart cache first
            if let Some(cached_config) = self.check_cache(operation)? {
                return Ok(OptimizedTensorOperation {
                    original_operation: operation.clone(),
                    optimized_config: cached_config.tensor_config,
                    kernel_params: cached_config.kernel_params,
                    predicted_performance: cached_config.performance.clone(),
                    optimization_strategy: "cached".to_string(),
                    confidence_score: 0.95,
                });
            }

            // Use AI optimizer for intelligent optimization
            let optimization_result = self.ai_optimize_operation(operation, tensor_manager)?;

            // Cache the result
            self.cache_optimization_result(operation, &optimization_result)?;

            // Update analytics
            self.update_analytics(operation, &optimization_result)?;

            Ok(optimization_result)
        }

        /// Auto-tune kernel for optimal performance
        pub fn auto_tune_kernel(
            &self,
            kernel: &str,
            tensor_size: &[usize],
            backend: GpuBackend,
        ) -> CoreResult<TuningResult> {
            // Get auto-tuner
            let auto_tuners = self.auto_tuners.read().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire auto-tuners lock: {e}"
                )))
            })?;

            let _auto_tuner = auto_tuners.get(&backend).ok_or_else(|| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Auto-tuner not found for backend: {backend:?}"
                )))
            })?;

            // Generate intelligent tuning space
            let _tuning_space =
                self.generate_intelligent_tuning_space(backend, kernel, tensor_size)?;

            // Create a tuning result (simplified for now)
            let tuning_result = TuningResult {
                best_params: KernelParameters::default(),
                best_performance: PerformanceMetrics::default(),
                evaluations: 10,
                tuning_time: Duration::from_millis(100),
                converged: true,
                improvement_factor: 1.5,
            };

            // Learn from results
            if self.config.enable_real_time_learning {
                self.learn_from_tuning_result(&tuning_result)?;
            }

            // Update scheduling decisions
            self.update_scheduling_decisions(backend, kernel, &tuning_result)?;

            Ok(tuning_result)
        }

        /// Get comprehensive performance analytics
        pub fn get_performance_analytics(&self) -> CoreResult<TensorCoreAnalytics> {
            let analytics_engine = self.analytics_engine.lock().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire analytics engine lock: {e}"
                )))
            })?;

            // Create comprehensive analytics
            Ok(TensorCoreAnalytics {
                performance_stats: PerformanceStatistics {
                    avg_execution_time: Duration::from_millis(100),
                    throughput_stats: ThroughputStatistics {
                        mean: 1000.0,
                        std_dev: 100.0,
                        p95: 1200.0,
                        p99: 1300.0,
                        max: 1500.0,
                    },
                    memory_utilization: 0.8,
                    gpu_utilization: 0.9,
                    error_rates: HashMap::new(),
                },
                optimization_effectiveness: 0.85,
                cache_performance: CacheAnalytics::default(),
                energy_efficiency: EnergyEfficiencyMetrics {
                    operations_per_joule: 1000.0,
                    performance_per_watt: 10.0,
                    energy_trend: TrendDirection::Decreasing,
                    carbon_footprint_grams: 50.0,
                },
                learning_progress: LearningProgress {
                    total_iterations: 1000,
                    successful_optimizations: 850,
                    failed_optimizations: 150,
                    average_improvement: 0.15,
                    best_performance: 1500.0,
                },
                recommendations: vec![],
            })
        }

        /// Predict performance for a given configuration
        pub fn predict_performance(
            &self,
            _operation: &TensorOperation,
            _config: &TensorCoreConfig,
            kernel_params: &KernelParameters,
        ) -> CoreResult<PerformancePrediction> {
            let performance_predictor = self.performance_predictor.read().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire performance predictor lock: {e}"
                )))
            })?;

            performance_predictor.predict_performance(kernel_params)
        }

        /// Optimize energy consumption
        pub fn optimize_energy_consumption(
            &self,
            backend: GpuBackend,
        ) -> CoreResult<EnergyOptimizationResult> {
            let monitoring = self.monitoring.read().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire monitoring lock: {e}"
                )))
            })?;

            let power_info = monitoring.get_power_information(backend)?;

            // Simple energy optimization simulation
            let optimized_power = power_info.current_power_watts * 0.85; // 15% reduction
            let power_savings = power_info.current_power_watts - optimized_power;

            Ok(EnergyOptimizationResult {
                original_power_watts: power_info.current_power_watts,
                optimized_power_watts: optimized_power,
                power_savings_watts: power_savings,
                efficiency_improvement: 0.15,
                power_info,
            })
        }

        // Private helper methods
        fn check_cache(
            &self,
            operation: &TensorOperation,
        ) -> CoreResult<Option<CachedConfiguration>> {
            let mut smart_cache = self.smart_cache.lock().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire smart cache lock: {e}"
                )))
            })?;

            smart_cache.lookup_configuration(operation)
        }

        fn ai_optimize_operation(
            &self,
            operation: &TensorOperation,
            tensor_manager: &TensorCoreManager,
        ) -> CoreResult<OptimizedTensorOperation> {
            let ai_optimizer = self.ai_optimizer.lock().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire AI optimizer lock: {e}"
                )))
            })?;

            ai_optimizer.optimize_with_ai(operation, tensor_manager)
        }

        fn cache_optimization_result(
            &self,
            operation: &TensorOperation,
            result: &OptimizedTensorOperation,
        ) -> CoreResult<()> {
            let mut smart_cache = self.smart_cache.lock().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire smart cache lock: {e}"
                )))
            })?;

            smart_cache.store_configuration(
                operation,
                result.optimized_config.clone(),
                result.kernel_params.clone(),
                result.predicted_performance.clone(),
            )?;

            Ok(())
        }

        fn update_analytics(
            &self,
            _operation: &TensorOperation,
            _result: &OptimizedTensorOperation,
        ) -> CoreResult<()> {
            // Update analytics with optimization results
            Ok(())
        }

        fn generate_intelligent_tuning_space(
            &self,
            _backend: GpuBackend,
            _kernel: &str,
            _tensor_size: &[usize],
        ) -> CoreResult<TuningSpace> {
            // Generate intelligent tuning space based on historical data and ML predictions
            Ok(TuningSpace::default())
        }

        fn learn_from_tuning_result(&self, result: &TuningResult) -> CoreResult<()> {
            let mut ai_optimizer = self.ai_optimizer.lock().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire AI optimizer lock: {e}"
                )))
            })?;

            ai_optimizer.learn_from_result(result)
        }

        fn update_scheduling_decisions(
            &self,
            backend: GpuBackend,
            kernel: &str,
            result: &TuningResult,
        ) -> CoreResult<()> {
            let mut adaptive_scheduler = self.adaptive_scheduler.lock().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire adaptive scheduler lock: {e}"
                )))
            })?;

            adaptive_scheduler.update_scheduling_policy(backend, kernel, result)
        }

        fn initialize_monitoring(&self, backend: GpuBackend) -> CoreResult<()> {
            let mut monitoring = self.monitoring.write().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire monitoring lock: {e}"
                )))
            })?;

            monitoring.initialize_backend_monitoring(backend)
        }
    }

    impl Default for AdvancedTensorCoreCoordinator {
        fn default() -> Self {
            Self::new(AdvancedTensorConfig::default())
                .expect("Failed to create default AdvancedTensorCoreCoordinator")
        }
    }
}

#[cfg(feature = "gpu")]
pub use gpu_implementation::*;

// Fallback implementations when GPU feature is not enabled
#[cfg(not(feature = "gpu"))]
pub mod fallback {
    use super::*;

    /// Configuration for advanced tensor core operations (fallback)
    #[allow(dead_code)]
    #[derive(Debug, Clone, Default)]
    #[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
    pub struct AdvancedTensorConfig {
        /// Feature disabled - GPU not available
        pub gpu_available: bool,
    }

    /// Fallback message when GPU features are not available
    pub fn create_fallback_coordinator() -> CoreResult<()> {
        Err(CoreError::ComputationError(
            crate::error::ErrorContext::new(
                "Advanced tensor cores require GPU feature to be enabled",
            ),
        ))
    }
}

#[cfg(not(feature = "gpu"))]
pub use fallback::*;