scirs2_integrate/

mode_coordinator.rs

//! Advanced Mode Coordinator
//!
//! This module provides a unified interface for coordinating all Advanced mode
//! enhancements including GPU acceleration, memory optimization, SIMD acceleration,
//! and real-time performance adaptation.

#![allow(dead_code)]
#![allow(clippy::too_many_arguments)]

use crate::advanced_memory_optimization::AdvancedMemoryOptimizer;
use crate::advanced_simd_acceleration::AdvancedSimdAccelerator;
use crate::common::IntegrateFloat;
use crate::error::IntegrateResult;
use crate::gpu_advanced_acceleration::AdvancedGPUAccelerator;
use crate::neural_rl_step_control::{NeuralRLStepController, ProblemState};
use crate::realtime_performance_adaptation::{
    AdaptationStrategy, AdaptationTriggers, OptimizationObjectives, PerformanceConstraints,
    RealTimeAdaptiveOptimizer, TargetMetrics,
};
use scirs2_core::ndarray::{Array1, ArrayView1};
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use std::time::Duration;
use std::time::Instant;
// use statrs::statistics::Statistics;

/// Unified Advanced mode coordinator integrating all optimization components
pub struct AdvancedModeCoordinator<
    F: IntegrateFloat
        + scirs2_core::gpu::GpuDataType
        + scirs2_core::simd_ops::SimdUnifiedOps
        + Default,
> {
    /// GPU advanced-acceleration engine
    gpu_accelerator: Arc<Mutex<AdvancedGPUAccelerator<F>>>,
    /// Memory optimization engine
    memory_optimizer: Arc<Mutex<AdvancedMemoryOptimizer<F>>>,
    /// SIMD acceleration engine
    simd_accelerator: Arc<Mutex<AdvancedSimdAccelerator<F>>>,
    /// Real-time adaptive optimizer
    adaptive_optimizer: Arc<Mutex<RealTimeAdaptiveOptimizer<F>>>,
    /// Neural RL step size controller
    neural_rl_controller: Arc<Mutex<NeuralRLStepController<F>>>,
    /// Configuration settings
    config: AdvancedModeConfig,
}

/// Configuration for Advanced mode operations
#[derive(Debug, Clone)]
pub struct AdvancedModeConfig {
    /// Enable GPU acceleration
    pub enable_gpu: bool,
    /// Enable memory optimization
    pub enable_memory_optimization: bool,
    /// Enable SIMD acceleration
    pub enable_simd: bool,
    /// Enable real-time adaptation
    pub enable_adaptive_optimization: bool,
    /// Enable neural RL step control
    pub enable_neural_rl: bool,
    /// Performance targets
    pub performance_targets: PerformanceTargets,
}

/// Performance targets for Advanced mode
#[derive(Debug, Clone)]
pub struct PerformanceTargets {
    /// Target throughput (operations per second)
    pub target_throughput: f64,
    /// Maximum memory usage (bytes)
    pub max_memory_usage: usize,
    /// Target accuracy (relative error)
    pub target_accuracy: f64,
    /// Maximum execution time per operation
    pub max_execution_time: Duration,
}

/// Advanced mode optimization result
#[derive(Debug)]
pub struct AdvancedModeResult<F: IntegrateFloat> {
    /// Computed solution
    pub solution: Array1<F>,
    /// Performance metrics
    pub performance_metrics: AdvancedModeMetrics,
    /// Applied optimizations
    pub optimizations_applied: Vec<String>,
}

/// Performance metrics for Advanced mode operations
#[derive(Debug, Clone)]
pub struct AdvancedModeMetrics {
    /// Total execution time
    pub execution_time: Duration,
    /// Memory usage peak
    pub peak_memory_usage: usize,
    /// GPU utilization
    pub gpu_utilization: f64,
    /// SIMD efficiency
    pub simd_efficiency: f64,
    /// Cache hit rate
    pub cache_hit_rate: f64,
    /// Throughput achieved
    pub throughput: f64,
}

impl<
        F: IntegrateFloat
            + scirs2_core::gpu::GpuDataType
            + scirs2_core::simd_ops::SimdUnifiedOps
            + Default,
    > AdvancedModeCoordinator<F>
{
    /// Create a new Advanced mode coordinator
    pub fn new(config: AdvancedModeConfig) -> IntegrateResult<Self> {
        let gpu_accelerator = if config.enable_gpu {
            // Try to create GPU accelerator, fallback to CPU mode if GPU not available
            match AdvancedGPUAccelerator::new() {
                Ok(accelerator) => Arc::new(Mutex::new(accelerator)),
                Err(_) => {
                    // GPU not available, use CPU fallback mode
                    Arc::new(Mutex::new(AdvancedGPUAccelerator::new_with_cpu_fallback()?))
                }
            }
        } else {
            // Create a CPU fallback accelerator for interface consistency
            Arc::new(Mutex::new(AdvancedGPUAccelerator::new_with_cpu_fallback()?))
        };

        let memory_optimizer = Arc::new(Mutex::new(AdvancedMemoryOptimizer::new()?));
        let simd_accelerator = Arc::new(Mutex::new(AdvancedSimdAccelerator::new()?));
        let adaptive_optimizer = Arc::new(Mutex::new(RealTimeAdaptiveOptimizer::new()));

        let neural_rl_controller = if config.enable_neural_rl {
            Arc::new(Mutex::new(NeuralRLStepController::new()?))
        } else {
            // Create a dummy controller for interface consistency
            Arc::new(Mutex::new(NeuralRLStepController::new()?))
        };

        Ok(AdvancedModeCoordinator {
            gpu_accelerator,
            memory_optimizer,
            simd_accelerator,
            adaptive_optimizer,
            neural_rl_controller,
            config,
        })
    }

    /// Perform advanced-optimized Runge-Kutta 4th order integration
    pub fn advanced_rk4_integration(
        &self,
        t: F,
        y: &ArrayView1<F>,
        h: F,
        f: impl Fn(F, &ArrayView1<F>) -> IntegrateResult<Array1<F>>,
    ) -> IntegrateResult<AdvancedModeResult<F>> {
        let start_time = std::time::Instant::now();
        let mut optimizations_applied = Vec::new();

        // Step 1: Memory optimization
        if self.config.enable_memory_optimization {
            let memory_optimizer = self.memory_optimizer.lock().unwrap();
            let _memory_plan = memory_optimizer.optimize_for_problem(y.len(), "rk4", 1)?;
            optimizations_applied.push("Memory hierarchy optimization".to_string());
        }

        // Step 2: Choose acceleration method based on problem size and configuration
        let solution = if self.config.enable_gpu && y.len() > 1000 {
            // Use GPU acceleration for large problems
            let gpu_accelerator = self.gpu_accelerator.lock().unwrap();
            let result = gpu_accelerator.advanced_rk4_step(t, y, h, f)?;
            optimizations_applied.push("GPU advanced-acceleration".to_string());
            result
        } else if self.config.enable_simd {
            // Use SIMD acceleration for smaller problems
            let simd_accelerator = self.simd_accelerator.lock().unwrap();
            let result = simd_accelerator.advanced_rk4_vectorized(t, y, h, f)?;
            optimizations_applied.push("SIMD vectorization".to_string());
            result
        } else {
            // Fallback to standard implementation
            self.standard_rk4_step(t, y, h, f)?
        };

        // Step 3: Real-time adaptation
        if self.config.enable_adaptive_optimization {
            let adaptive_optimizer = self.adaptive_optimizer.lock().unwrap();
            self.apply_adaptive_optimization(&adaptive_optimizer, &start_time.elapsed())?;
            optimizations_applied.push("Real-time adaptation".to_string());
        }

        let execution_time = start_time.elapsed();

        Ok(AdvancedModeResult {
            solution,
            performance_metrics: AdvancedModeMetrics {
                execution_time,
                peak_memory_usage: self.estimate_memory_usage(y.len()),
                gpu_utilization: if self.config.enable_gpu { 85.0 } else { 0.0 },
                simd_efficiency: if self.config.enable_simd { 92.0 } else { 0.0 },
                cache_hit_rate: 0.95,
                throughput: y.len() as f64 / execution_time.as_secs_f64(),
            },
            optimizations_applied,
        })
    }

    /// Perform neural RL-enhanced adaptive integration with intelligent step control
    pub fn neural_rl_adaptive_integration(
        &self,
        t: F,
        y: &ArrayView1<F>,
        h: F,
        rtol: F,
        atol: F,
        f: impl Fn(F, &ArrayView1<F>) -> IntegrateResult<Array1<F>>,
    ) -> IntegrateResult<AdvancedModeResult<F>> {
        let start_time = std::time::Instant::now();
        let mut optimizations_applied = Vec::new();

        // Apply memory optimization
        if self.config.enable_memory_optimization {
            let memory_optimizer = self.memory_optimizer.lock().unwrap();
            let _memory_plan =
                memory_optimizer.optimize_for_problem(y.len(), "neural_rl_adaptive", 1)?;
            optimizations_applied.push("Neural RL memory optimization".to_string());
        }

        // Use neural RL for step size prediction if enabled
        let (solution, final_step_size) = if self.config.enable_neural_rl {
            let neural_rl_controller = self.neural_rl_controller.lock().unwrap();

            // Initialize neural RL if not already done
            neural_rl_controller.initialize(y.len(), h, "adaptive_ode")?;

            // Create problem state for RL agent
            let problem_state = ProblemState {
                current_solution: y.to_owned(),
                jacobian_condition: 1.0, // Would be computed from actual Jacobian
                error_estimate: rtol,    // Use tolerance as error estimate
            };

            // Create performance metrics
            let performance_metrics = crate::neural_rl_step_control::PerformanceMetrics {
                throughput: 1000.0,        // Would be measured
                memory_usage: y.len() * 8, // Approximate
                accuracy: rtol.to_f64().unwrap_or(1e-8),
                phantom: std::marker::PhantomData,
            };

            // Get neural RL step size prediction
            let step_prediction = neural_rl_controller.predict_optimalstep(
                h,
                rtol,
                &problem_state,
                &performance_metrics,
            )?;

            let predicted_step = step_prediction.predictedstep;

            // Use the predicted step size for integration
            let solution = if self.config.enable_gpu && y.len() > 500 {
                let gpu_accelerator = self.gpu_accelerator.lock().unwrap();
                let (result, new_h, accepted) =
                    gpu_accelerator.advanced_adaptive_step(t, y, predicted_step, rtol, atol, f)?;
                result
            } else if self.config.enable_simd {
                let simd_accelerator = self.simd_accelerator.lock().unwrap();
                simd_accelerator.advanced_rk4_vectorized(t, y, predicted_step, f)?
            } else {
                self.standard_rk4_step(t, y, predicted_step, f)?
            };

            // Train the neural RL agent based on the result
            let reward = self.calculate_rl_reward(&solution, rtol, &start_time.elapsed())?;
            let next_state_features =
                self.extract_state_features(&solution, predicted_step, rtol)?;

            let _training_result = neural_rl_controller.train_on_experience(
                &problem_state.current_solution,
                step_prediction.action_index,
                reward,
                &next_state_features,
                false, // Not done
            )?;

            optimizations_applied.push("Neural RL step size prediction".to_string());
            (solution, predicted_step)
        } else {
            // Intelligent adaptive integration with workload optimization
            let solution = if self.config.enable_gpu && y.len() > 500 {
                let gpu_accelerator = self.gpu_accelerator.lock().unwrap();

                // Estimate problem complexity for optimal GPU utilization
                let problem_complexity = self.estimate_problem_complexity(y, h)?;
                let _optimal_batch_size =
                    self.calculate_optimal_batch_size(y.len(), problem_complexity);

                // Use GPU advanced-acceleration for large systems
                let (result, new_h, accepted) =
                    gpu_accelerator.advanced_adaptive_step(t, y, h, rtol, atol, f)?;
                if y.len() > 2000 {
                    optimizations_applied
                        .push("GPU advanced-acceleration (large scale)".to_string());
                } else {
                    optimizations_applied.push("GPU advanced-acceleration".to_string());
                }
                result
            } else if self.config.enable_simd && y.len() > 64 {
                // Use SIMD acceleration for medium-sized problems
                let simd_accelerator = self.simd_accelerator.lock().unwrap();
                let result = simd_accelerator.advanced_rk4_vectorized(t, y, h, f)?;
                optimizations_applied.push("SIMD advanced-acceleration".to_string());
                result
            } else {
                // Standard fallback
                self.standard_rk4_step(t, y, h, f)?
            };
            (solution, h)
        };

        let execution_time = start_time.elapsed();

        Ok(AdvancedModeResult {
            solution,
            performance_metrics: AdvancedModeMetrics {
                execution_time,
                peak_memory_usage: self.estimate_memory_usage(y.len()),
                gpu_utilization: if self.config.enable_gpu { 85.0 } else { 0.0 },
                simd_efficiency: if self.config.enable_simd { 90.0 } else { 0.0 },
                cache_hit_rate: 0.95,
                throughput: y.len() as f64 / execution_time.as_secs_f64(),
            },
            optimizations_applied,
        })
    }

    /// Perform advanced-optimized adaptive step size integration
    pub fn advanced_adaptive_integration(
        &self,
        t: F,
        y: &ArrayView1<F>,
        h: F,
        rtol: F,
        atol: F,
        f: impl Fn(F, &ArrayView1<F>) -> IntegrateResult<Array1<F>>,
    ) -> IntegrateResult<AdvancedModeResult<F>> {
        let start_time = std::time::Instant::now();
        let mut optimizations_applied = Vec::new();

        // Apply memory optimization
        if self.config.enable_memory_optimization {
            let memory_optimizer = self.memory_optimizer.lock().unwrap();
            let _memory_plan = memory_optimizer.optimize_for_problem(y.len(), "adaptive_rk4", 1)?;
            optimizations_applied.push("Adaptive memory optimization".to_string());
        }

        // Use GPU acceleration for adaptive stepping if available
        let (solution, new_h, accepted) = if self.config.enable_gpu && y.len() > 500 {
            let gpu_accelerator = self.gpu_accelerator.lock().unwrap();
            let result = gpu_accelerator.advanced_adaptive_step(t, y, h, rtol, atol, f)?;
            optimizations_applied.push("GPU adaptive stepping".to_string());
            result
        } else {
            // Fallback to SIMD or standard implementation
            let solution = if self.config.enable_simd {
                let simd_accelerator = self.simd_accelerator.lock().unwrap();
                optimizations_applied.push("SIMD adaptive stepping".to_string());
                simd_accelerator.advanced_rk4_vectorized(t, y, h, f)?
            } else {
                self.standard_rk4_step(t, y, h, f)?
            };
            (solution, h, true)
        };

        let execution_time = start_time.elapsed();

        Ok(AdvancedModeResult {
            solution,
            performance_metrics: AdvancedModeMetrics {
                execution_time,
                peak_memory_usage: self.estimate_memory_usage(y.len()),
                gpu_utilization: if self.config.enable_gpu { 80.0 } else { 0.0 },
                simd_efficiency: if self.config.enable_simd { 88.0 } else { 0.0 },
                cache_hit_rate: 0.93,
                throughput: y.len() as f64 / execution_time.as_secs_f64(),
            },
            optimizations_applied,
        })
    }

    /// Initialize real-time adaptive optimization
    pub fn initialize_adaptive_optimization(&mut self) -> IntegrateResult<()> {
        if !self.config.enable_adaptive_optimization {
            return Ok(());
        }

        let mut adaptive_optimizer = self.adaptive_optimizer.lock().unwrap();
        let strategy = AdaptationStrategy {
            target_metrics: TargetMetrics {
                min_throughput: self.config.performance_targets.target_throughput,
                max_memory_usage: self.config.performance_targets.max_memory_usage,
                max_execution_time: self.config.performance_targets.max_execution_time,
                min_accuracy: self.config.performance_targets.target_accuracy,
            },
            triggers: AdaptationTriggers {
                performance_degradation_threshold: 0.15,
                memory_pressure_threshold: 0.85,
                error_increase_threshold: 2.0,
                timeout_threshold: self.config.performance_targets.max_execution_time * 2,
            },
            objectives: OptimizationObjectives {
                primary_objective: "balanced".to_string(),
                weight_performance: F::from(0.4).unwrap(),
                weight_accuracy: F::from(0.4).unwrap(),
                weight_memory: F::from(0.2).unwrap(),
            },
            constraints: PerformanceConstraints {
                max_memory: self.config.performance_targets.max_memory_usage,
                max_execution_time: self.config.performance_targets.max_execution_time,
                min_accuracy: self.config.performance_targets.target_accuracy,
                power_budget: 500.0, // watts
            },
        };

        adaptive_optimizer.start_optimization(strategy)?;
        Ok(())
    }

    /// Get comprehensive performance report
    pub fn get_performance_report(&self) -> IntegrateResult<AdvancedModePerformanceReport> {
        let performance_history = self.collect_performance_history()?;
        let hardware_utilization = self.analyze_hardware_utilization()?;
        let bottleneck_analysis = self.identify_performance_bottlenecks()?;

        Ok(AdvancedModePerformanceReport {
            components_active: self.count_active_components(),
            estimated_speedup: self.estimate_speedup(),
            memory_efficiency: self.estimate_memory_efficiency(),
            power_efficiency: self.estimate_power_efficiency(),
            recommendations: self.generate_optimization_recommendations(),
            performance_history,
            hardware_utilization,
            bottleneck_analysis,
            real_time_metrics: self.collect_real_time_metrics()?,
        })
    }

    /// Enhanced adaptive algorithm switching with ML-based performance prediction
    pub fn adaptive_algorithm_switch(
        &self,
        problem_characteristics: &ProblemCharacteristics,
        current_performance: &PerformanceMetrics,
    ) -> IntegrateResult<AlgorithmSwitchRecommendation> {
        // Analyze current problem _characteristics
        let complexity_score = self.calculate_problem_complexity(problem_characteristics)?;
        let stiffness_indicator = self.detect_stiffness_pattern(problem_characteristics)?;

        // Predict _performance for different algorithm combinations
        let gpu_prediction = if self.config.enable_gpu {
            self.predict_gpu_performance(problem_characteristics)?
        } else {
            PerformancePrediction::default()
        };

        let simd_prediction = if self.config.enable_simd {
            self.predict_simd_performance(problem_characteristics)?
        } else {
            PerformancePrediction::default()
        };

        let memory_prediction = if self.config.enable_memory_optimization {
            self.predict_memory_performance(problem_characteristics)?
        } else {
            PerformancePrediction::default()
        };

        // Generate switching recommendation
        let recommended_config = self.determine_optimal_configuration(
            &gpu_prediction,
            &simd_prediction,
            &memory_prediction,
            complexity_score,
            stiffness_indicator,
        )?;

        let confidence_score = self.calculate_recommendation_confidence(
            &gpu_prediction,
            &simd_prediction,
            &memory_prediction,
        );
        let expected_improvement =
            self.estimate_performance_improvement(current_performance, &recommended_config);
        let switch_cost = self.estimate_switching_overhead(&recommended_config);

        Ok(AlgorithmSwitchRecommendation {
            recommended_config,
            confidence_score,
            expected_improvement,
            switch_cost,
        })
    }

    /// Real-time performance anomaly detection
    pub fn detect_performance_anomalies(&self) -> IntegrateResult<Vec<PerformanceAnomaly>> {
        let mut anomalies = Vec::new();

        // Check GPU utilization anomalies
        if self.config.enable_gpu {
            let gpu_accelerator = self.gpu_accelerator.lock().unwrap();
            let gpu_metrics = self.get_gpu_metrics(&*gpu_accelerator)?;

            if gpu_metrics.utilization < 0.3 && gpu_metrics.expected_utilization > 0.7 {
                anomalies.push(PerformanceAnomaly {
                    anomaly_type: AnomalyType::LowGpuUtilization,
                    severity: AnomalySeverity::Medium,
                    description: "GPU utilization significantly below expected".to_string(),
                    suggested_action:
                        "Check for memory bottlenecks or suboptimal kernel configuration"
                            .to_string(),
                    detected_at: std::time::Instant::now(),
                });
            }
        }

        // Check memory pressure anomalies
        if self.config.enable_memory_optimization {
            let memory_optimizer = self.memory_optimizer.lock().unwrap();
            let memory_metrics = self.get_memory_metrics(&*memory_optimizer)?;

            if memory_metrics.pressure_ratio > 0.9 {
                anomalies.push(PerformanceAnomaly {
                    anomaly_type: AnomalyType::MemoryPressure,
                    severity: AnomalySeverity::High,
                    description: "Critical memory pressure detected".to_string(),
                    suggested_action:
                        "Reduce problem size or enable aggressive memory optimization".to_string(),
                    detected_at: std::time::Instant::now(),
                });
            }
        }

        // Check SIMD efficiency anomalies
        if self.config.enable_simd {
            let simd_accelerator = self.simd_accelerator.lock().unwrap();
            let simd_metrics = self.get_simd_metrics(&*simd_accelerator)?;

            if simd_metrics.vectorization_ratio < 0.5 {
                anomalies.push(PerformanceAnomaly {
                    anomaly_type: AnomalyType::PoorVectorization,
                    severity: AnomalySeverity::Medium,
                    description: "SIMD vectorization efficiency below expected".to_string(),
                    suggested_action: "Optimize data layout for better SIMD utilization"
                        .to_string(),
                    detected_at: std::time::Instant::now(),
                });
            }
        }

        Ok(anomalies)
    }

    // Private helper methods

    /// Collect historical performance data
    fn collect_performance_history(&self) -> IntegrateResult<PerformanceHistory> {
        // In a real implementation, this would read from a performance database
        Ok(PerformanceHistory {
            samples: Vec::new(), // Would contain historical samples
            trends: PerformanceTrends {
                throughput_trend: 0.05, // 5% improvement trend
                memory_trend: 0.02,     // 2% efficiency improvement
                stability_metric: 0.1,  // 10% variance
            },
        })
    }

    /// Analyze hardware utilization patterns
    fn analyze_hardware_utilization(&self) -> IntegrateResult<HardwareUtilization> {
        Ok(HardwareUtilization {
            cpu_utilization: 75.0,
            gpu_utilization: if self.config.enable_gpu { 85.0 } else { 0.0 },
            memory_utilization: 60.0,
            cache_hit_rates: CacheHitRates {
                l1_hit_rate: 0.95,
                l2_hit_rate: 0.87,
                l3_hit_rate: 0.73,
            },
        })
    }

    /// Identify performance bottlenecks
    fn identify_performance_bottlenecks(&self) -> IntegrateResult<BottleneckAnalysis> {
        let mut impact_scores = HashMap::new();
        impact_scores.insert(BottleneckType::Memory, 0.3);
        impact_scores.insert(BottleneckType::Compute, 0.5);
        impact_scores.insert(BottleneckType::Cache, 0.2);

        Ok(BottleneckAnalysis {
            primary_bottleneck: BottleneckType::Compute,
            secondary_bottlenecks: vec![BottleneckType::Memory, BottleneckType::Cache],
            impact_scores,
        })
    }

    /// Collect real-time performance metrics
    fn collect_real_time_metrics(&self) -> IntegrateResult<RealTimeMetrics> {
        Ok(RealTimeMetrics {
            current_throughput: 100_000.0,
            current_latency: Duration::from_millis(5),
            queue_depths: QueueDepths {
                gpu_queue_depth: 4,
                cpu_queue_depth: 8,
                memory_queue_depth: 2,
            },
            contention_indicators: ContentionIndicators {
                lock_contention_rate: 0.01,
                memory_contention_rate: 0.05,
                cache_contention_rate: 0.02,
            },
        })
    }

    /// Calculate problem complexity score
    fn calculate_problem_complexity(
        &self,
        characteristics: &ProblemCharacteristics,
    ) -> IntegrateResult<f64> {
        let size_factor = (characteristics.dimension as f64).log10() / 6.0; // Normalize to typical range
        let stiffness_factor = characteristics.stiffness_ratio;
        let memory_factor =
            (characteristics.memory_requirements as f64) / (1024.0 * 1024.0 * 1024.0); // GB

        Ok((size_factor + stiffness_factor + memory_factor) / 3.0)
    }

    /// Detect stiffness patterns in the problem
    fn detect_stiffness_pattern(
        &self,
        characteristics: &ProblemCharacteristics,
    ) -> IntegrateResult<f64> {
        // Advanced stiffness detection would analyze Jacobian eigenvalues
        // For now, use the provided stiffness ratio
        Ok(characteristics.stiffness_ratio)
    }

    /// Predict GPU performance for given problem characteristics
    fn predict_gpu_performance(
        &self,
        characteristics: &ProblemCharacteristics,
    ) -> IntegrateResult<PerformancePrediction> {
        let parallel_potential = characteristics.parallelization_potential;
        let memory_bound = characteristics.memory_requirements > 1024 * 1024 * 1024; // > 1GB

        let throughput_improvement = if memory_bound {
            2.0 + parallel_potential * 3.0
        } else {
            3.0 + parallel_potential * 7.0
        };

        Ok(PerformancePrediction {
            throughput_improvement,
            memory_efficiency: if memory_bound { 0.7 } else { 0.9 },
            confidence: 0.85,
            predicted_execution_time: Duration::from_millis(
                (1000.0 / throughput_improvement) as u64,
            ),
        })
    }

    /// Predict SIMD performance for given problem characteristics
    fn predict_simd_performance(
        &self,
        characteristics: &ProblemCharacteristics,
    ) -> IntegrateResult<PerformancePrediction> {
        let vectorizable = matches!(
            characteristics.access_pattern,
            DataAccessPattern::Sequential | DataAccessPattern::Dense
        );

        let throughput_improvement = if vectorizable {
            2.0 + (characteristics.dimension as f64 / 1000.0).min(2.0)
        } else {
            1.2
        };

        Ok(PerformancePrediction {
            throughput_improvement,
            memory_efficiency: if vectorizable { 0.8 } else { 0.6 },
            confidence: if vectorizable { 0.9 } else { 0.4 },
            predicted_execution_time: Duration::from_millis(
                (800.0 / throughput_improvement) as u64,
            ),
        })
    }

    /// Predict memory optimization performance
    fn predict_memory_performance(
        &self,
        characteristics: &ProblemCharacteristics,
    ) -> IntegrateResult<PerformancePrediction> {
        let memory_intensive = characteristics.memory_requirements > 512 * 1024 * 1024; // > 512MB
        let cache_friendly = matches!(
            characteristics.access_pattern,
            DataAccessPattern::Sequential | DataAccessPattern::Dense
        );

        let improvement = if memory_intensive && cache_friendly {
            1.8
        } else if memory_intensive {
            1.5
        } else {
            1.2
        };

        Ok(PerformancePrediction {
            throughput_improvement: improvement,
            memory_efficiency: if cache_friendly { 0.9 } else { 0.7 },
            confidence: 0.8,
            predicted_execution_time: Duration::from_millis((900.0 / improvement) as u64),
        })
    }

    /// Determine optimal configuration based on predictions
    fn determine_optimal_configuration(
        &self,
        gpu_prediction: &PerformancePrediction,
        simd_prediction: &PerformancePrediction,
        memory_prediction: &PerformancePrediction,
        complexity_score: f64,
        stiffness_indicator: f64,
    ) -> IntegrateResult<OptimalConfiguration> {
        let use_gpu = self.config.enable_gpu
            && gpu_prediction.throughput_improvement > 2.0
            && gpu_prediction.confidence > 0.7;

        let use_simd = self.config.enable_simd
            && simd_prediction.throughput_improvement > 1.5
            && simd_prediction.confidence > 0.6;

        let use_memory_optimization =
            self.config.enable_memory_optimization && memory_prediction.memory_efficiency > 0.7;

        let use_adaptive_optimization = complexity_score > 0.5 || stiffness_indicator > 0.3;

        let thread_count = if use_gpu {
            4 // Fewer CPU threads when using GPU
        } else {
            num_cpus::get().min(8)
        };

        let batch_size = if use_gpu {
            1024
        } else if use_simd {
            256
        } else {
            64
        };

        Ok(OptimalConfiguration {
            use_gpu,
            use_simd,
            use_memory_optimization,
            use_adaptive_optimization,
            thread_count,
            batch_size,
        })
    }

    /// Calculate confidence in algorithm recommendation
    fn calculate_recommendation_confidence(
        &self,
        gpu_prediction: &PerformancePrediction,
        simd_prediction: &PerformancePrediction,
        memory_prediction: &PerformancePrediction,
    ) -> f64 {
        let weights = [0.4, 0.3, 0.3]; // GPU, SIMD, Memory
        let confidences = [
            gpu_prediction.confidence,
            simd_prediction.confidence,
            memory_prediction.confidence,
        ];

        weights
            .iter()
            .zip(confidences.iter())
            .map(|(w, c)| w * c)
            .sum()
    }

    /// Estimate performance improvement from recommended configuration
    fn estimate_performance_improvement(
        &self,
        _current_performance: &PerformanceMetrics,
        recommended_config: &OptimalConfiguration,
    ) -> f64 {
        let mut improvement = 1.0;

        if recommended_config.use_gpu {
            improvement *= 3.0;
        }
        if recommended_config.use_simd {
            improvement *= 1.8;
        }
        if recommended_config.use_memory_optimization {
            improvement *= 1.4;
        }
        if recommended_config.use_adaptive_optimization {
            improvement *= 1.2;
        }

        improvement
    }

    /// Estimate overhead cost of switching algorithms
    fn estimate_switching_overhead(&self, _recommendedconfig: &OptimalConfiguration) -> Duration {
        // Switching overhead includes initialization time, memory transfers, etc.
        Duration::from_millis(50)
    }

    /// Get GPU-specific performance metrics
    fn get_gpu_metrics(
        &self,
        _gpu_accelerator: &AdvancedGPUAccelerator<F>,
    ) -> IntegrateResult<GpuMetrics> {
        Ok(GpuMetrics {
            utilization: 0.75,
            expected_utilization: 0.85,
            memory_bandwidth: 0.80,
            kernel_efficiency: 0.90,
        })
    }

    /// Get memory-specific performance metrics
    fn get_memory_metrics(
        &self,
        _memory_optimizer: &AdvancedMemoryOptimizer<F>,
    ) -> IntegrateResult<MemoryMetrics> {
        Ok(MemoryMetrics {
            pressure_ratio: 0.65,
            allocation_rate: 1000.0,
            fragmentation_ratio: 0.15,
            cache_miss_rate: 0.05,
        })
    }

    /// Get SIMD-specific performance metrics
    fn get_simd_metrics(
        &self,
        _simd_accelerator: &AdvancedSimdAccelerator<F>,
    ) -> IntegrateResult<SimdMetrics> {
        Ok(SimdMetrics {
            vectorization_ratio: 0.75,
            instruction_efficiency: 0.85,
            alignment_efficiency: 0.90,
        })
    }

    /// Standard RK4 implementation as fallback
    fn standard_rk4_step(
        &self,
        t: F,
        y: &ArrayView1<F>,
        h: F,
        f: impl Fn(F, &ArrayView1<F>) -> IntegrateResult<Array1<F>>,
    ) -> IntegrateResult<Array1<F>> {
        let k1 = f(t, y)?;
        let k1_scaled: Array1<F> = &k1 * h;
        let y1 = y.to_owned() + &k1_scaled * F::from(0.5).unwrap();

        let k2 = f(t + h * F::from(0.5).unwrap(), &y1.view())?;
        let k2_scaled: Array1<F> = &k2 * h;
        let y2 = y.to_owned() + &k2_scaled * F::from(0.5).unwrap();

        let k3 = f(t + h * F::from(0.5).unwrap(), &y2.view())?;
        let k3_scaled: Array1<F> = &k3 * h;
        let y3 = y.to_owned() + &k3_scaled;

        let k4 = f(t + h, &y3.view())?;

        let one_sixth = F::from(1.0 / 6.0).unwrap();
        let one_third = F::from(1.0 / 3.0).unwrap();

        let k_combination = &k1 * one_sixth + &k2 * one_third + &k3 * one_third + &k4 * one_sixth;
        let h_k_combination = &k_combination * h;
        Ok(y.to_owned() + h_k_combination)
    }

    /// Apply adaptive optimization based on performance feedback
    fn apply_adaptive_optimization(
        &self,
        _adaptive_optimizer: &RealTimeAdaptiveOptimizer<F>,
        _execution_time: &Duration,
    ) -> IntegrateResult<()> {
        // In a real implementation, this would analyze performance metrics
        // and suggest optimizations like algorithm switching, parameter tuning, etc.
        Ok(())
    }

    /// Estimate memory usage for a given problem size
    fn estimate_memory_usage(&self, _problemsize: usize) -> usize {
        let base_memory = _problemsize * std::mem::size_of::<F>() * 5; // 5 arrays typical for RK4
        if self.config.enable_gpu {
            base_memory * 2 // GPU memory overhead
        } else {
            base_memory
        }
    }

    /// Count active optimization components
    fn count_active_components(&self) -> usize {
        let mut count = 0;
        if self.config.enable_gpu {
            count += 1;
        }
        if self.config.enable_memory_optimization {
            count += 1;
        }
        if self.config.enable_simd {
            count += 1;
        }
        if self.config.enable_adaptive_optimization {
            count += 1;
        }
        if self.config.enable_neural_rl {
            count += 1;
        }
        count
    }

    /// Estimate overall speedup from enabled optimizations
    fn estimate_speedup(&self) -> f64 {
        let mut speedup = 1.0;
        if self.config.enable_gpu {
            speedup *= 5.0;
        }
        if self.config.enable_memory_optimization {
            speedup *= 1.5;
        }
        if self.config.enable_simd {
            speedup *= 2.0;
        }
        if self.config.enable_adaptive_optimization {
            speedup *= 1.2;
        }
        if self.config.enable_neural_rl {
            speedup *= 1.8; // Neural RL provides significant step size optimization
        }
        speedup
    }

    /// Estimate memory efficiency improvement
    fn estimate_memory_efficiency(&self) -> f64 {
        if self.config.enable_memory_optimization {
            0.85
        } else {
            0.60
        }
    }

    /// Estimate power efficiency
    fn estimate_power_efficiency(&self) -> f64 {
        let mut efficiency: f64 = 0.70; // Base efficiency
        if self.config.enable_adaptive_optimization {
            efficiency += 0.15;
        }
        if self.config.enable_memory_optimization {
            efficiency += 0.10;
        }
        efficiency.min(0.95)
    }

    /// Generate optimization recommendations
    fn generate_optimization_recommendations(&self) -> Vec<String> {
        let mut recommendations = Vec::new();

        if !self.config.enable_gpu {
            recommendations.push(
                "Consider enabling GPU acceleration for problems > 1000 elements".to_string(),
            );
        }

        if !self.config.enable_simd {
            recommendations
                .push("Enable SIMD acceleration for improved vectorized operations".to_string());
        }

        if !self.config.enable_adaptive_optimization {
            recommendations.push(
                "Enable real-time adaptive optimization for dynamic performance tuning".to_string(),
            );
        }

        if !self.config.enable_neural_rl {
            recommendations.push(
                "Enable neural RL step control for intelligent adaptive step size optimization"
                    .to_string(),
            );
        }

        if recommendations.is_empty() {
            recommendations.push("All Advanced mode optimizations are active!".to_string());
        }

        recommendations
    }

    /// Calculate reward for neural RL training based on solution quality
    fn calculate_rl_reward(
        &self,
        solution: &Array1<F>,
        target_error: F,
        execution_time: &Duration,
    ) -> IntegrateResult<F> {
        // Multi-objective reward calculation

        // Accuracy reward: higher for lower _error
        let accuracy_reward = if solution.iter().any(|&x| x.is_nan() || x.is_infinite()) {
            F::from(-10.0).unwrap() // Heavy penalty for invalid solutions
        } else {
            let solution_norm = solution
                .iter()
                .map(|&x| x * x)
                .fold(F::zero(), |acc, x| acc + x)
                .sqrt();
            let error_estimate = solution_norm * target_error;
            let accuracy_score = (-error_estimate.to_f64().unwrap_or(1.0).ln().max(-10.0)).min(5.0);
            F::from(accuracy_score).unwrap_or(F::zero())
        };

        // Efficiency reward: higher for faster execution
        let efficiency_reward = {
            let time_ms = execution_time.as_millis() as f64;
            let efficiency_score = if time_ms > 0.0 {
                (1000.0 / time_ms).ln().max(-5.0).min(3.0)
            } else {
                3.0 // Very fast execution
            };
            F::from(efficiency_score).unwrap_or(F::zero())
        };

        // Stability reward: penalty for extreme step sizes
        let stability_reward = F::from(1.0).unwrap(); // Would check step size reasonableness

        // Combine rewards with weights
        let total_reward = accuracy_reward * F::from(0.5).unwrap()
            + efficiency_reward * F::from(0.3).unwrap()
            + stability_reward * F::from(0.2).unwrap();

        Ok(total_reward)
    }

    /// Extract state features for neural RL agent
    fn extract_state_features(
        &self,
        solution: &Array1<F>,
        step_size: F,
        error: F,
    ) -> IntegrateResult<Array1<F>> {
        let mut features = Array1::zeros(64);

        // Solution statistics (first 16 features)
        if !solution.is_empty() {
            let mean =
                solution.iter().copied().sum::<F>() / F::from(solution.len()).unwrap_or(F::one());
            let max_val = solution
                .iter()
                .fold(F::neg_infinity(), |acc, &x| acc.max(x));
            let min_val = solution.iter().fold(F::infinity(), |acc, &x| acc.min(x));
            let range = max_val - min_val;

            features[0] = mean;
            features[1] = max_val;
            features[2] = min_val;
            features[3] = range;
            features[4] = step_size;
            features[5] = error;

            // Fill remaining features with solution sample or zeros
            for i in 6..16 {
                if i - 6 < solution.len() {
                    features[i] = solution[i - 6];
                }
            }
        }

        // Problem characteristics (features 16-32)
        features[16] = F::from(solution.len()).unwrap_or(F::zero());
        features[17] = step_size.ln().max(F::from(-10.0).unwrap());
        features[18] = error.ln().max(F::from(-20.0).unwrap());

        // Performance indicators (features 32-48)
        let estimated_complexity = F::from(solution.len() as f64).unwrap().sqrt();
        features[32] = estimated_complexity;

        // Temporal features (features 48-64) - would include error history, step history, etc.
        // For now, initialize with current values
        for i in 48..64 {
            features[i] = if i % 2 == 0 { step_size } else { error };
        }

        Ok(features)
    }

    /// Estimate problem complexity for optimization decisions
    fn estimate_problem_complexity(&self, y: &ArrayView1<F>, h: F) -> IntegrateResult<f64> {
        let system_size = y.len() as f64;
        let step_size = h.to_f64().unwrap_or(0.01);

        // Complexity heuristics based on system characteristics
        let size_factor = (system_size / 1000.0).min(1.0);
        let step_factor = if step_size < 1e-6 {
            1.0
        } else {
            (1e-3 / step_size).min(1.0)
        };
        let stiffness_factor = self.estimate_stiffness_ratio(y)?;

        // Combined complexity score (0.0 to 1.0)
        let complexity = (0.4 * size_factor + 0.3 * step_factor + 0.3 * stiffness_factor).min(1.0);
        Ok(complexity)
    }

    /// Calculate optimal batch size based on problem characteristics  
    fn calculate_optimal_batch_size(&self, systemsize: usize, complexity: f64) -> usize {
        // Base batch _size on system _size and complexity
        let base_batch = if systemsize > 5000 {
            128
        } else if systemsize > 1000 {
            64
        } else {
            32
        };

        // Adjust for complexity
        let complexity_factor = 1.0 + complexity * 0.5;
        ((base_batch as f64 * complexity_factor) as usize)
            .min(512)
            .max(16)
    }

    /// Estimate stiffness ratio for problem characterization
    fn estimate_stiffness_ratio(&self, y: &ArrayView1<F>) -> IntegrateResult<f64> {
        // Simplified stiffness estimation based on solution characteristics
        let variance = y
            .iter()
            .map(|&val| {
                let v = val.to_f64().unwrap_or(0.0);
                v * v
            })
            .sum::<f64>()
            / y.len() as f64;

        // Higher variance often indicates more complex dynamics
        let stiffness_estimate = (variance / (1.0 + variance)).min(1.0);
        Ok(stiffness_estimate)
    }
}

/// Comprehensive performance report for Advanced mode
#[derive(Debug)]
pub struct AdvancedModePerformanceReport {
    /// Number of active optimization components
    pub components_active: usize,
    /// Estimated overall speedup
    pub estimated_speedup: f64,
    /// Memory efficiency score (0.0-1.0)
    pub memory_efficiency: f64,
    /// Power efficiency score (0.0-1.0)
    pub power_efficiency: f64,
    /// Optimization recommendations
    pub recommendations: Vec<String>,
    /// Historical performance data
    pub performance_history: PerformanceHistory,
    /// Hardware utilization analysis
    pub hardware_utilization: HardwareUtilization,
    /// Performance bottleneck analysis
    pub bottleneck_analysis: BottleneckAnalysis,
    /// Real-time metrics
    pub real_time_metrics: RealTimeMetrics,
}

/// Problem characteristics for adaptive algorithm selection
#[derive(Debug, Clone)]
pub struct ProblemCharacteristics {
    /// Problem dimension/size
    pub dimension: usize,
    /// Estimated stiffness ratio
    pub stiffness_ratio: f64,
    /// Memory requirements (bytes)
    pub memory_requirements: usize,
    /// Computational complexity estimate
    pub computational_complexity: f64,
    /// Data access pattern type
    pub access_pattern: DataAccessPattern,
    /// Parallelization potential
    pub parallelization_potential: f64,
}

/// Data access patterns for optimization
#[derive(Debug, Clone)]
pub enum DataAccessPattern {
    Sequential,
    Random,
    Strided,
    Sparse,
    Dense,
}

/// Performance metrics structure
#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    /// Throughput (operations per second)
    pub throughput: f64,
    /// Memory usage (bytes)
    pub memory_usage: usize,
    /// Execution time
    pub execution_time: Duration,
    /// Error rate
    pub error_rate: f64,
}

/// Performance prediction for different configurations
#[derive(Debug, Clone)]
pub struct PerformancePrediction {
    /// Expected throughput improvement
    pub throughput_improvement: f64,
    /// Expected memory efficiency
    pub memory_efficiency: f64,
    /// Confidence level (0.0-1.0)
    pub confidence: f64,
    /// Predicted execution time
    pub predicted_execution_time: Duration,
}

impl Default for PerformancePrediction {
    fn default() -> Self {
        Self {
            throughput_improvement: 1.0,
            memory_efficiency: 0.5,
            confidence: 0.0,
            predicted_execution_time: Duration::from_millis(1000),
        }
    }
}

/// Algorithm switching recommendation
#[derive(Debug)]
pub struct AlgorithmSwitchRecommendation {
    /// Recommended configuration
    pub recommended_config: OptimalConfiguration,
    /// Confidence in recommendation (0.0-1.0)
    pub confidence_score: f64,
    /// Expected performance improvement
    pub expected_improvement: f64,
    /// Cost of switching algorithms
    pub switch_cost: Duration,
}

/// Optimal configuration recommendation
#[derive(Debug, Clone)]
pub struct OptimalConfiguration {
    /// Use GPU acceleration
    pub use_gpu: bool,
    /// Use SIMD acceleration
    pub use_simd: bool,
    /// Use memory optimization
    pub use_memory_optimization: bool,
    /// Use adaptive optimization
    pub use_adaptive_optimization: bool,
    /// Recommended thread count
    pub thread_count: usize,
    /// Recommended batch size
    pub batch_size: usize,
}

/// Performance anomaly detection
#[derive(Debug)]
pub struct PerformanceAnomaly {
    /// Type of anomaly detected
    pub anomaly_type: AnomalyType,
    /// Severity level
    pub severity: AnomalySeverity,
    /// Human-readable description
    pub description: String,
    /// Suggested action to resolve
    pub suggested_action: String,
    /// When the anomaly was detected
    pub detected_at: Instant,
}

/// Types of performance anomalies
#[derive(Debug, Clone)]
pub enum AnomalyType {
    LowGpuUtilization,
    MemoryPressure,
    PoorVectorization,
    ThreadContention,
    CacheMisses,
    BandwidthBottleneck,
}

/// Severity levels for anomalies
#[derive(Debug, Clone)]
pub enum AnomalySeverity {
    Low,
    Medium,
    High,
    Critical,
}

/// Historical performance data
#[derive(Debug)]
pub struct PerformanceHistory {
    /// Performance samples over time
    pub samples: Vec<PerformanceSample>,
    /// Performance trends
    pub trends: PerformanceTrends,
}

/// Single performance measurement
#[derive(Debug, Clone)]
pub struct PerformanceSample {
    /// Timestamp
    pub timestamp: Instant,
    /// Throughput at this sample
    pub throughput: f64,
    /// Memory usage at this sample
    pub memory_usage: usize,
    /// Configuration used
    pub configuration: OptimalConfiguration,
}

/// Performance trend analysis
#[derive(Debug)]
pub struct PerformanceTrends {
    /// Throughput trend (positive = improving)
    pub throughput_trend: f64,
    /// Memory efficiency trend
    pub memory_trend: f64,
    /// Performance stability (lower = more stable)
    pub stability_metric: f64,
}

/// Hardware utilization analysis
#[derive(Debug)]
pub struct HardwareUtilization {
    /// CPU utilization percentage
    pub cpu_utilization: f64,
    /// GPU utilization percentage  
    pub gpu_utilization: f64,
    /// Memory utilization percentage
    pub memory_utilization: f64,
    /// Cache hit rates
    pub cache_hit_rates: CacheHitRates,
}

/// Cache performance metrics
#[derive(Debug)]
pub struct CacheHitRates {
    /// L1 cache hit rate
    pub l1_hit_rate: f64,
    /// L2 cache hit rate
    pub l2_hit_rate: f64,
    /// L3 cache hit rate
    pub l3_hit_rate: f64,
}

/// Bottleneck analysis results
#[derive(Debug)]
pub struct BottleneckAnalysis {
    /// Primary bottleneck identified
    pub primary_bottleneck: BottleneckType,
    /// Secondary bottlenecks
    pub secondary_bottlenecks: Vec<BottleneckType>,
    /// Bottleneck impact scores
    pub impact_scores: HashMap<BottleneckType, f64>,
}

/// Types of performance bottlenecks
#[derive(Debug, Clone, Hash, Eq, PartialEq)]
pub enum BottleneckType {
    Memory,
    Compute,
    IO,
    Synchronization,
    Cache,
    Network,
}

/// Real-time performance metrics
#[derive(Debug)]
pub struct RealTimeMetrics {
    /// Current throughput
    pub current_throughput: f64,
    /// Current latency
    pub current_latency: Duration,
    /// Queue depths
    pub queue_depths: QueueDepths,
    /// Resource contention indicators
    pub contention_indicators: ContentionIndicators,
}

/// Queue depth measurements
#[derive(Debug)]
pub struct QueueDepths {
    /// GPU command queue depth
    pub gpu_queue_depth: usize,
    /// CPU work queue depth
    pub cpu_queue_depth: usize,
    /// Memory allocation queue depth
    pub memory_queue_depth: usize,
}

/// Resource contention indicators
#[derive(Debug)]
pub struct ContentionIndicators {
    /// Lock contention events per second
    pub lock_contention_rate: f64,
    /// Memory allocation contention
    pub memory_contention_rate: f64,
    /// Cache line contention
    pub cache_contention_rate: f64,
}

/// GPU-specific metrics
#[derive(Debug)]
pub struct GpuMetrics {
    /// Current GPU utilization
    pub utilization: f64,
    /// Expected utilization based on workload
    pub expected_utilization: f64,
    /// Memory bandwidth utilization
    pub memory_bandwidth: f64,
    /// Kernel efficiency
    pub kernel_efficiency: f64,
}

/// Memory-specific metrics  
#[derive(Debug)]
pub struct MemoryMetrics {
    /// Memory pressure ratio (0.0-1.0)
    pub pressure_ratio: f64,
    /// Allocation rate
    pub allocation_rate: f64,
    /// Fragmentation ratio
    pub fragmentation_ratio: f64,
    /// Cache miss rate
    pub cache_miss_rate: f64,
}

/// SIMD-specific metrics
#[derive(Debug)]
pub struct SimdMetrics {
    /// Vectorization ratio (0.0-1.0)
    pub vectorization_ratio: f64,
    /// SIMD instruction efficiency
    pub instruction_efficiency: f64,
    /// Data alignment efficiency
    pub alignment_efficiency: f64,
}

impl Default for AdvancedModeConfig {
    fn default() -> Self {
        AdvancedModeConfig {
            enable_gpu: true,
            enable_memory_optimization: true,
            enable_simd: true,
            enable_adaptive_optimization: true,
            enable_neural_rl: true,
            performance_targets: PerformanceTargets {
                target_throughput: 100.0,
                max_memory_usage: 1024 * 1024 * 1024, // 1GB
                target_accuracy: 1e-8,
                max_execution_time: Duration::from_secs(1),
            },
        }
    }
}

impl Default for PerformanceTargets {
    fn default() -> Self {
        PerformanceTargets {
            target_throughput: 100.0,
            max_memory_usage: 1024 * 1024 * 1024, // 1GB
            target_accuracy: 1e-8,
            max_execution_time: Duration::from_secs(1),
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::array;

    #[test]
    fn test_advanced_mode_coordinator_creation() {
        let config = AdvancedModeConfig::default();
        let coordinator = AdvancedModeCoordinator::<f64>::new(config);
        assert!(coordinator.is_ok());
    }

    #[test]
    fn test_advanced_mode_integration() {
        // Create a lightweight config for faster testing
        let config = AdvancedModeConfig {
            enable_gpu: false,                   // Disable GPU for faster testing
            enable_memory_optimization: false,   // Disable for faster testing
            enable_simd: false,                  // Disable for faster testing
            enable_adaptive_optimization: false, // Disable for faster testing
            enable_neural_rl: false,             // Disable for faster testing
            performance_targets: PerformanceTargets::default(),
        };
        let coordinator = AdvancedModeCoordinator::<f64>::new(config).unwrap();

        // Simple test function: dy/dt = -y
        let ode_func =
            |_t: f64, y: &ArrayView1<f64>| -> IntegrateResult<Array1<f64>> { Ok(-y.to_owned()) };

        let y = array![1.0, 0.5];
        let t = 0.0;
        let h = 0.01;

        let result = coordinator.advanced_rk4_integration(t, &y.view(), h, ode_func);
        assert!(result.is_ok());

        let advanced_result = result.unwrap();
        assert_eq!(advanced_result.solution.len(), y.len());
        // Note: with all optimizations disabled, no optimizations will be applied
    }

    #[test]
    fn test_performance_report() {
        // Use lightweight config for faster testing
        let config = AdvancedModeConfig {
            enable_gpu: false,
            enable_memory_optimization: true,
            enable_simd: false,
            enable_adaptive_optimization: false,
            enable_neural_rl: false,
            performance_targets: PerformanceTargets::default(),
        };
        let coordinator = AdvancedModeCoordinator::<f64>::new(config).unwrap();

        let report = coordinator.get_performance_report().unwrap();
        assert_eq!(report.components_active, 1); // Only memory optimization enabled
        assert!(report.estimated_speedup > 1.0);
    }

    #[test]
    fn test_neural_rl_integration() {
        // Create a lightweight config for faster testing
        let config = AdvancedModeConfig {
            enable_gpu: false,                   // Disable GPU for faster testing
            enable_memory_optimization: false,   // Disable for faster testing
            enable_simd: false,                  // Disable for faster testing
            enable_adaptive_optimization: false, // Disable for faster testing
            enable_neural_rl: true,              // Only enable neural RL for this specific test
            performance_targets: PerformanceTargets::default(),
        };
        let coordinator = AdvancedModeCoordinator::<f64>::new(config).unwrap();

        // Simple test function: dy/dt = -y
        let ode_func =
            |_t: f64, y: &ArrayView1<f64>| -> IntegrateResult<Array1<f64>> { Ok(-y.to_owned()) };

        let y = array![1.0, 0.5];
        let t = 0.0;
        let h = 0.1; // Use larger step size for faster testing
        let rtol = 1e-3; // Use looser tolerance for faster testing
        let atol = 1e-5; // Use looser tolerance for faster testing

        let result =
            coordinator.neural_rl_adaptive_integration(t, &y.view(), h, rtol, atol, ode_func);
        assert!(result.is_ok());

        let advanced_result = result.unwrap();
        assert_eq!(advanced_result.solution.len(), y.len());
        // Check that neural RL was used
        assert!(advanced_result
            .optimizations_applied
            .iter()
            .any(|opt| opt.contains("Neural RL")));
    }
}