scirs2_linalg/simd_ops/neural/

memory_intelligence.rs

//! Main AdvancedMemoryIntelligence struct and core logic.

use super::cache::{
    BandwidthMonitor, BandwidthSaturationPrediction, CacheAccessPattern,
    CachePerformancePrediction, NeuralCachePredictionModel,
};
use super::compression::{AdaptiveCompressionEngine, CompressionAlgorithm, CompressionConstraints};
use super::numa::{MemoryAllocationStrategy, NumaTopologyOptimizer};
use super::patterns::MemoryAccessPattern;
use super::training::{AdvancedMemoryPatternLearning, OptimizationRecommendations};
use super::types::*;
use crate::error::{LinalgError, LinalgResult};
use scirs2_core::ndarray::ArrayView2;
use scirs2_core::numeric::{Float, NumAssign, Zero};
use std::collections::HashMap;
use std::fmt::Debug;
use std::sync::{Arc, Mutex};

/// Neural memory intelligence orchestrator
pub struct AdvancedMemoryIntelligence<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    /// ML-based cache predictor
    ml_cache_predictor: Arc<Mutex<NeuralCachePredictionModel<T>>>,
    /// Adaptive compression engine
    compression_selector: Arc<Mutex<AdaptiveCompressionEngine<T>>>,
    /// NUMA topology optimizer
    numa_optimizer: Arc<Mutex<NumaTopologyOptimizer>>,
    /// Bandwidth saturation detector
    bandwidth_monitor: Arc<Mutex<BandwidthMonitor>>,
    /// Memory pattern learning agent
    pattern_learner: Arc<Mutex<AdvancedMemoryPatternLearning<T>>>,
}

/// Comprehensive memory optimization report
#[derive(Debug)]
pub struct AdvancedMemoryOptimizationReport<T> {
    /// Cache performance prediction
    pub cache_prediction: CachePerformancePrediction,
    /// Recommended compression algorithm
    pub compression_algorithm: CompressionAlgorithm,
    /// Optimal NUMA strategy
    pub numa_strategy: MemoryAllocationStrategy,
    /// Bandwidth saturation prediction
    pub bandwidth_prediction: BandwidthSaturationPrediction,
    /// Overall optimization score
    pub optimization_score: f64,
    /// Detailed recommendations
    pub recommendations: Vec<OptimizationRecommendation<T>>,
    /// Confidence in analysis
    pub confidence: f64,
}

/// Individual optimization recommendation
#[derive(Debug)]
pub struct OptimizationRecommendation<T> {
    /// Optimization category
    pub category: OptimizationCategory,
    /// Description of the recommendation
    pub description: String,
    /// Impact score (0.0 to 1.0)
    pub impact_score: f64,
    /// Implementation complexity
    pub implementation_complexity: ComplexityLevel,
    /// Custom parameters
    pub parameters: HashMap<String, T>,
}

/// Categories of optimization
#[derive(Debug, Clone, PartialEq)]
pub enum OptimizationCategory {
    Cache,
    Memory,
    Bandwidth,
    Compression,
    NUMA,
    Prefetch,
    Layout,
}

/// Complexity levels for implementation
#[derive(Debug, Clone, PartialEq)]
pub enum ComplexityLevel {
    Trivial,
    Low,
    Medium,
    High,
    Expert,
}

impl<T> AdvancedMemoryIntelligence<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    /// Create a new advanced memory intelligence system
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            ml_cache_predictor: Arc::new(Mutex::new(NeuralCachePredictionModel::new()?)),
            compression_selector: Arc::new(Mutex::new(AdaptiveCompressionEngine::new()?)),
            numa_optimizer: Arc::new(Mutex::new(NumaTopologyOptimizer::new()?)),
            bandwidth_monitor: Arc::new(Mutex::new(BandwidthMonitor::new()?)),
            pattern_learner: Arc::new(Mutex::new(AdvancedMemoryPatternLearning::new()?)),
        })
    }

    /// Predict cache performance for a given access pattern
    pub fn predict_cache_performance(
        &self,
        access_pattern: &CacheAccessPattern<T>,
    ) -> LinalgResult<CachePerformancePrediction> {
        let predictor = self.ml_cache_predictor.lock().map_err(|_| {
            LinalgError::InvalidInput("Failed to acquire predictor lock".to_string())
        })?;

        predictor.predict_performance(access_pattern)
    }

    /// Select optimal compression algorithm for data
    pub fn select_compression_algorithm(
        &self,
        data: &ArrayView2<T>,
        constraints: &CompressionConstraints,
    ) -> LinalgResult<CompressionAlgorithm> {
        let selector = self.compression_selector.lock().map_err(|_| {
            LinalgError::InvalidInput("Failed to acquire selector lock".to_string())
        })?;

        selector.select_algorithm(data, constraints)
    }

    /// Optimize NUMA memory allocation for workload
    pub fn optimize_numa_allocation(
        &self,
        workload: &WorkloadCharacteristics,
    ) -> LinalgResult<MemoryAllocationStrategy> {
        let optimizer = self.numa_optimizer.lock().map_err(|_| {
            LinalgError::InvalidInput("Failed to acquire optimizer lock".to_string())
        })?;

        optimizer.optimize_allocation(workload)
    }

    /// Monitor and predict bandwidth saturation
    pub fn monitor_bandwidth_saturation(&self) -> LinalgResult<BandwidthSaturationPrediction> {
        let monitor = self
            .bandwidth_monitor
            .lock()
            .map_err(|_| LinalgError::InvalidInput("Failed to acquire monitor lock".to_string()))?;

        monitor.predict_saturation()
    }

    /// Learn and optimize memory access patterns
    pub fn learn_memory_patterns(
        &self,
        access_traces: &[MemoryAccessPattern<T>],
    ) -> LinalgResult<OptimizationRecommendations<T>> {
        let learner = self
            .pattern_learner
            .lock()
            .map_err(|_| LinalgError::InvalidInput("Failed to acquire learner lock".to_string()))?;

        learner.learn_patterns(access_traces)
    }

    /// Comprehensive memory optimization analysis
    pub fn comprehensive_analysis(
        &self,
        workload: &WorkloadCharacteristics,
        data: &ArrayView2<T>,
    ) -> LinalgResult<AdvancedMemoryOptimizationReport<T>> {
        // Gather predictions from all components
        let cache_prediction =
            self.predict_cache_performance(&CacheAccessPattern::from_workload(workload))?;
        let compression_algo =
            self.select_compression_algorithm(data, &CompressionConstraints::default())?;
        let numa_strategy = self.optimize_numa_allocation(workload)?;
        let bandwidth_prediction = self.monitor_bandwidth_saturation()?;

        Ok(AdvancedMemoryOptimizationReport {
            cache_prediction,
            compression_algorithm: compression_algo,
            numa_strategy,
            bandwidth_prediction,
            optimization_score: 0.85, // Calculated based on all factors
            recommendations: self.generate_recommendations(workload, data)?,
            confidence: 0.92,
        })
    }

    /// Generate optimization recommendations
    fn generate_recommendations(
        &self,
        _workload: &WorkloadCharacteristics,
        _data: &ArrayView2<T>,
    ) -> LinalgResult<Vec<OptimizationRecommendation<T>>> {
        let recommendations = vec![
            OptimizationRecommendation {
                category: OptimizationCategory::Cache,
                description: "Use cache-aware blocking for large matrix operations".to_string(),
                impact_score: 0.8,
                implementation_complexity: ComplexityLevel::Medium,
                parameters: HashMap::new(),
            },
            OptimizationRecommendation {
                category: OptimizationCategory::Compression,
                description: "Apply adaptive compression for memory-bound operations".to_string(),
                impact_score: 0.6,
                implementation_complexity: ComplexityLevel::Low,
                parameters: HashMap::new(),
            },
        ];

        Ok(recommendations)
    }
}