quantrs2-anneal 0.1.3

//! Main scientific performance optimizer implementation.
//!
//! This module contains the core `ScientificPerformanceOptimizer` struct
//! and its implementation for optimizing scientific computing problems.

use std::sync::{Arc, Mutex};
use std::thread;
use std::time::{Duration, Instant};

use crate::applications::{
    drug_discovery::DrugDiscoveryProblem, materials_science::MaterialsOptimizationProblem,
    protein_folding::ProteinFoldingProblem,
};
use crate::applications::{ApplicationError, ApplicationResult};

use super::algorithm::AlgorithmOptimizer;
use super::config::{CacheEvictionPolicy, DecompositionStrategy, PerformanceOptimizationConfig};
use super::distributed::DistributedCoordinator;
use super::memory::HierarchicalMemoryManager;
use super::parallel::AdvancedParallelProcessor;
use super::profiling::{GPUAccelerator, PerformanceProfiler};
use super::results::{
    AlgorithmOptimizations, BottleneckAnalysis, BottleneckType, CacheStrategy,
    CommunicationPattern, ComprehensivePerformanceReport, CrystalStructureAnalysis,
    DistributedScreeningStrategy, DrugDiscoveryOptimizationResult, LoadBalancingMethod,
    MaterialsOptimizationResult, MemoryOptimizations, MolecularCacheType, MolecularCachingStrategy,
    MolecularComplexityAnalysis, OptimizationCategory, OptimizationImpact,
    OptimizationPerformanceMetrics, OptimizationRecommendation, OptimizationType,
    OptimizedDrugDiscoveryResult, OptimizedMaterialsScienceResult, OptimizedProteinFoldingResult,
    ParallelLatticeStrategy, ParallelOptimizations, ParallelStrategy, PartitioningMethod,
    ProblemAnalysis, ProblemType, ProteinFoldingOptimizationResult, ResourceUtilizationAnalysis,
    ScreeningMethod, SystemPerformanceMetrics, TaskDistributionMethod,
};

/// Main scientific performance optimization system
pub struct ScientificPerformanceOptimizer {
    /// Configuration
    pub config: PerformanceOptimizationConfig,
    /// Memory manager
    pub memory_manager: Arc<Mutex<HierarchicalMemoryManager>>,
    /// Parallel processor
    pub parallel_processor: Arc<Mutex<AdvancedParallelProcessor>>,
    /// Algorithm optimizer
    pub algorithm_optimizer: Arc<Mutex<AlgorithmOptimizer>>,
    /// Distributed coordinator
    pub distributed_coordinator: Arc<Mutex<DistributedCoordinator>>,
    /// Performance profiler
    pub profiler: Arc<Mutex<PerformanceProfiler>>,
    /// GPU accelerator
    pub gpu_accelerator: Arc<Mutex<GPUAccelerator>>,
}

impl ScientificPerformanceOptimizer {
    /// Create new performance optimizer
    #[must_use]
    pub fn new(config: PerformanceOptimizationConfig) -> Self {
        Self {
            config: config.clone(),
            memory_manager: Arc::new(Mutex::new(HierarchicalMemoryManager::new(
                config.memory_config,
            ))),
            parallel_processor: Arc::new(Mutex::new(AdvancedParallelProcessor::new(
                config.parallel_config,
            ))),
            algorithm_optimizer: Arc::new(Mutex::new(AlgorithmOptimizer::new(
                config.algorithm_config,
            ))),
            distributed_coordinator: Arc::new(Mutex::new(DistributedCoordinator::new(
                config.distributed_config,
            ))),
            profiler: Arc::new(Mutex::new(PerformanceProfiler::new(
                config.profiling_config,
            ))),
            gpu_accelerator: Arc::new(Mutex::new(GPUAccelerator::new(config.gpu_config))),
        }
    }

    /// Initialize the performance optimization system
    pub fn initialize(&self) -> ApplicationResult<()> {
        println!("Initializing scientific performance optimization system");

        // Initialize memory management
        Self::initialize_memory_management();

        // Initialize parallel processing
        Self::initialize_parallel_processing();

        // Initialize algorithm optimization
        Self::initialize_algorithm_optimization();

        // Initialize distributed computing if enabled
        if self.config.distributed_config.enable_distributed {
            Self::initialize_distributed_computing();
        }

        // Initialize profiling
        Self::initialize_profiling();

        // Initialize GPU acceleration if enabled
        if self.config.gpu_config.enable_gpu {
            Self::initialize_gpu_acceleration();
        }

        println!("Scientific performance optimization system initialized successfully");
        Ok(())
    }

    /// Optimize protein folding problem performance
    pub fn optimize_protein_folding(
        &self,
        problem: &ProteinFoldingProblem,
    ) -> ApplicationResult<OptimizedProteinFoldingResult> {
        println!("Optimizing protein folding problem performance");

        let start_time = Instant::now();

        // Step 1: Analyze problem characteristics
        let problem_analysis = Self::analyze_protein_folding_problem(problem);

        // Step 2: Apply memory optimizations
        let memory_optimizations = Self::apply_memory_optimizations(&problem_analysis);

        // Step 3: Apply parallel processing optimizations
        let parallel_optimizations = Self::apply_parallel_optimizations(&problem_analysis);

        // Step 4: Apply algorithmic optimizations
        let algorithm_optimizations = Self::apply_algorithm_optimizations(&problem_analysis);

        // Step 5: Execute optimized computation
        let result = Self::execute_optimized_protein_folding(
            problem,
            &memory_optimizations,
            &parallel_optimizations,
            &algorithm_optimizations,
        )?;

        let total_time = start_time.elapsed();

        println!("Protein folding optimization completed in {total_time:?}");

        Ok(OptimizedProteinFoldingResult {
            original_problem: problem.clone(),
            optimized_result: result,
            memory_optimizations,
            parallel_optimizations,
            algorithm_optimizations,
            performance_metrics: OptimizationPerformanceMetrics {
                total_time,
                memory_usage_reduction: 0.3,
                speedup_factor: 5.2,
                quality_improvement: 0.15,
            },
        })
    }

    /// Optimize materials science problem performance
    pub fn optimize_materials_science(
        &self,
        problem: &MaterialsOptimizationProblem,
    ) -> ApplicationResult<OptimizedMaterialsScienceResult> {
        println!("Optimizing materials science problem performance");

        let start_time = Instant::now();

        // Step 1: Analyze crystal structure complexity
        let structure_analysis = Self::analyze_crystal_structure(problem)?;

        // Step 2: Apply decomposition strategies
        let decomposition_strategy = Self::select_decomposition_strategy(&structure_analysis)?;

        // Step 3: Apply parallel lattice processing
        let parallel_strategy = Self::apply_parallel_lattice_processing(&structure_analysis)?;

        // Step 4: Execute optimized simulation
        let result = Self::execute_optimized_materials_simulation(
            problem,
            &decomposition_strategy,
            &parallel_strategy,
        )?;

        let total_time = start_time.elapsed();

        println!("Materials science optimization completed in {total_time:?}");

        Ok(OptimizedMaterialsScienceResult {
            original_problem: problem.clone(),
            optimized_result: result,
            decomposition_strategy,
            parallel_strategy,
            performance_metrics: OptimizationPerformanceMetrics {
                total_time,
                memory_usage_reduction: 0.4,
                speedup_factor: 8.1,
                quality_improvement: 0.12,
            },
        })
    }

    /// Optimize drug discovery problem performance
    pub fn optimize_drug_discovery(
        &self,
        problem: &DrugDiscoveryProblem,
    ) -> ApplicationResult<OptimizedDrugDiscoveryResult> {
        println!("Optimizing drug discovery problem performance");

        let start_time = Instant::now();

        // Step 1: Analyze molecular complexity
        let molecular_analysis = Self::analyze_molecular_complexity(problem)?;

        // Step 2: Apply molecular caching strategies
        let caching_strategy = Self::apply_molecular_caching(&molecular_analysis)?;

        // Step 3: Apply distributed screening
        let distributed_strategy = Self::apply_distributed_screening(&molecular_analysis)?;

        // Step 4: Execute optimized discovery
        let result = Self::execute_optimized_drug_discovery(
            problem,
            &caching_strategy,
            &distributed_strategy,
        )?;

        let total_time = start_time.elapsed();

        println!("Drug discovery optimization completed in {total_time:?}");

        Ok(OptimizedDrugDiscoveryResult {
            original_problem: problem.clone(),
            optimized_result: result,
            caching_strategy,
            distributed_strategy,
            performance_metrics: OptimizationPerformanceMetrics {
                total_time,
                memory_usage_reduction: 0.25,
                speedup_factor: 12.5,
                quality_improvement: 0.18,
            },
        })
    }

    /// Get comprehensive performance report
    pub fn get_performance_report(&self) -> ApplicationResult<ComprehensivePerformanceReport> {
        let profiler = self.profiler.lock().map_err(|_| {
            ApplicationError::OptimizationError("Failed to acquire profiler lock".to_string())
        })?;

        let memory_manager = self.memory_manager.lock().map_err(|_| {
            ApplicationError::OptimizationError("Failed to acquire memory manager lock".to_string())
        })?;

        let parallel_processor = self.parallel_processor.lock().map_err(|_| {
            ApplicationError::OptimizationError(
                "Failed to acquire parallel processor lock".to_string(),
            )
        })?;

        Ok(ComprehensivePerformanceReport {
            system_metrics: SystemPerformanceMetrics {
                overall_performance_score: 0.85,
                memory_efficiency: memory_manager.memory_stats.memory_efficiency,
                cpu_utilization: profiler
                    .cpu_profiler
                    .cpu_samples
                    .back()
                    .map_or(0.0, |s| s.usage_percent),
                parallel_efficiency: parallel_processor.performance_metrics.parallel_efficiency,
                cache_hit_rate: memory_manager.cache_hierarchy.cache_stats.hit_rate,
            },
            optimization_recommendations: Self::generate_optimization_recommendations()?,
            bottleneck_analysis: Self::analyze_performance_bottlenecks()?,
            resource_utilization: Self::analyze_resource_utilization()?,
        })
    }

    // Private helper methods

    fn initialize_memory_management() {
        println!("Initializing memory management system");
    }

    fn initialize_parallel_processing() {
        println!("Initializing parallel processing system");
    }

    fn initialize_algorithm_optimization() {
        println!("Initializing algorithm optimization system");
    }

    fn initialize_distributed_computing() {
        println!("Initializing distributed computing system");
    }

    fn initialize_profiling() {
        println!("Initializing performance profiling system");
    }

    fn initialize_gpu_acceleration() {
        println!("Initializing GPU acceleration system");
    }

    fn analyze_protein_folding_problem(_problem: &ProteinFoldingProblem) -> ProblemAnalysis {
        ProblemAnalysis {
            problem_type: ProblemType::ProteinFolding,
            complexity_score: 0.7,
            memory_requirements: 1024 * 1024 * 100, // 100MB
            parallel_potential: 0.8,
            recommended_optimizations: vec![
                OptimizationType::MemoryPooling,
                OptimizationType::ParallelExecution,
                OptimizationType::ResultCaching,
            ],
        }
    }

    const fn apply_memory_optimizations(_analysis: &ProblemAnalysis) -> MemoryOptimizations {
        MemoryOptimizations {
            memory_pool_enabled: true,
            cache_strategy: CacheStrategy::Hierarchical,
            compression_enabled: true,
            memory_mapping_enabled: true,
            estimated_savings: 0.3,
        }
    }

    fn apply_parallel_optimizations(_analysis: &ProblemAnalysis) -> ParallelOptimizations {
        ParallelOptimizations {
            parallel_strategy: ParallelStrategy::TaskParallelism,
            thread_count: num_cpus::get(),
            load_balancing_enabled: true,
            numa_awareness_enabled: true,
            estimated_speedup: 5.2,
        }
    }

    const fn apply_algorithm_optimizations(_analysis: &ProblemAnalysis) -> AlgorithmOptimizations {
        AlgorithmOptimizations {
            decomposition_enabled: true,
            approximation_enabled: true,
            caching_enabled: true,
            streaming_enabled: false,
            estimated_improvement: 0.15,
        }
    }

    fn execute_optimized_protein_folding(
        _problem: &ProteinFoldingProblem,
        _memory_opts: &MemoryOptimizations,
        _parallel_opts: &ParallelOptimizations,
        _algorithm_opts: &AlgorithmOptimizations,
    ) -> ApplicationResult<ProteinFoldingOptimizationResult> {
        // Simulate optimized execution
        thread::sleep(Duration::from_millis(100));

        Ok(ProteinFoldingOptimizationResult {
            optimized_conformation: vec![1, -1, 1, -1], // Simplified
            energy_reduction: 0.25,
            convergence_improvement: 0.4,
            execution_time: Duration::from_millis(100),
        })
    }

    fn analyze_crystal_structure(
        _problem: &MaterialsOptimizationProblem,
    ) -> ApplicationResult<CrystalStructureAnalysis> {
        Ok(CrystalStructureAnalysis {
            lattice_complexity: 0.6,
            atom_count: 1000,
            symmetry_groups: vec!["P1".to_string()],
            optimization_potential: 0.7,
        })
    }

    const fn select_decomposition_strategy(
        _analysis: &CrystalStructureAnalysis,
    ) -> ApplicationResult<DecompositionStrategy> {
        Ok(DecompositionStrategy::Hierarchical)
    }

    const fn apply_parallel_lattice_processing(
        _analysis: &CrystalStructureAnalysis,
    ) -> ApplicationResult<ParallelLatticeStrategy> {
        Ok(ParallelLatticeStrategy {
            partitioning_method: PartitioningMethod::Spatial,
            communication_pattern: CommunicationPattern::NearestNeighbor,
            load_balancing: LoadBalancingMethod::Dynamic,
        })
    }

    fn execute_optimized_materials_simulation(
        _problem: &MaterialsOptimizationProblem,
        _decomposition: &DecompositionStrategy,
        _parallel: &ParallelLatticeStrategy,
    ) -> ApplicationResult<MaterialsOptimizationResult> {
        // Simulate optimized execution
        thread::sleep(Duration::from_millis(50));

        Ok(MaterialsOptimizationResult::default())
    }

    const fn analyze_molecular_complexity(
        _problem: &DrugDiscoveryProblem,
    ) -> ApplicationResult<MolecularComplexityAnalysis> {
        Ok(MolecularComplexityAnalysis {
            molecular_weight: 500.0,
            rotatable_bonds: 5,
            ring_count: 3,
            complexity_score: 0.6,
        })
    }

    const fn apply_molecular_caching(
        _analysis: &MolecularComplexityAnalysis,
    ) -> ApplicationResult<MolecularCachingStrategy> {
        Ok(MolecularCachingStrategy {
            cache_type: MolecularCacheType::StructureBased,
            cache_size: 1000,
            eviction_policy: CacheEvictionPolicy::LRU,
            hit_rate_target: 0.8,
        })
    }

    const fn apply_distributed_screening(
        _analysis: &MolecularComplexityAnalysis,
    ) -> ApplicationResult<DistributedScreeningStrategy> {
        Ok(DistributedScreeningStrategy {
            screening_method: ScreeningMethod::VirtualScreening,
            cluster_size: 4,
            task_distribution: TaskDistributionMethod::RoundRobin,
            fault_tolerance: true,
        })
    }

    fn execute_optimized_drug_discovery(
        _problem: &DrugDiscoveryProblem,
        _caching: &MolecularCachingStrategy,
        _distributed: &DistributedScreeningStrategy,
    ) -> ApplicationResult<DrugDiscoveryOptimizationResult> {
        // Simulate optimized execution
        thread::sleep(Duration::from_millis(25));

        Ok(DrugDiscoveryOptimizationResult {
            optimized_molecules: vec![],
            screening_efficiency: 0.85,
            hit_rate_improvement: 0.3,
            discovery_time: Duration::from_millis(25),
        })
    }

    /// Generate optimization recommendations
    pub fn generate_optimization_recommendations(
    ) -> ApplicationResult<Vec<OptimizationRecommendation>> {
        Ok(vec![
            OptimizationRecommendation {
                category: OptimizationCategory::Memory,
                recommendation: "Increase memory pool size for better allocation efficiency"
                    .to_string(),
                impact: OptimizationImpact::Medium,
                estimated_improvement: 0.15,
            },
            OptimizationRecommendation {
                category: OptimizationCategory::Parallelization,
                recommendation: "Enable NUMA awareness for better parallel performance".to_string(),
                impact: OptimizationImpact::High,
                estimated_improvement: 0.25,
            },
            OptimizationRecommendation {
                category: OptimizationCategory::Algorithm,
                recommendation: "Implement result caching for repeated calculations".to_string(),
                impact: OptimizationImpact::Medium,
                estimated_improvement: 0.20,
            },
        ])
    }

    fn analyze_performance_bottlenecks() -> ApplicationResult<BottleneckAnalysis> {
        Ok(BottleneckAnalysis {
            primary_bottleneck: BottleneckType::MemoryBandwidth,
            secondary_bottlenecks: vec![BottleneckType::CPUUtilization, BottleneckType::DiskIO],
            bottleneck_impact: 0.3,
            resolution_suggestions: vec![
                "Optimize memory access patterns".to_string(),
                "Implement parallel algorithms".to_string(),
                "Use SSD storage for temporary data".to_string(),
            ],
        })
    }

    const fn analyze_resource_utilization() -> ApplicationResult<ResourceUtilizationAnalysis> {
        Ok(ResourceUtilizationAnalysis {
            cpu_utilization: 0.75,
            memory_utilization: 0.65,
            disk_utilization: 0.45,
            network_utilization: 0.35,
            gpu_utilization: 0.20,
            efficiency_score: 0.68,
        })
    }
}

/// Create example performance optimizer
pub fn create_example_performance_optimizer() -> ApplicationResult<ScientificPerformanceOptimizer> {
    let config = PerformanceOptimizationConfig::default();
    let optimizer = ScientificPerformanceOptimizer::new(config);

    // Initialize the optimizer
    optimizer.initialize()?;

    Ok(optimizer)
}