Module performance_optimization 

Performance optimization utilities for critical paths

This module provides tools and utilities for optimizing performance-critical sections of scirs2-core based on profiling data. Enhanced with AI-driven adaptive optimization and ML-based performance modeling for Advanced mode.

Advanced Mode Features

• AI-Driven Strategy Selection: Machine learning models predict optimal strategies
• Neural Performance Modeling: Deep learning for performance prediction
• Adaptive Hyperparameter Tuning: Automatic optimization parameter adjustment
• Real-time Performance Learning: Continuous improvement from execution data
• Multi-objective optimization: Balance performance, memory, and energy efficiency
• Context-Aware Optimization: Environment and workload-specific adaptations

Re-exports

pub use crate::performance::benchmarking;

pub use crate::performance::cache_optimization as cache_aware_algorithms;

pub use crate::performance::advanced_optimization;

Modules

fast_paths

Fast path optimizations for common operations

Structs

AdaptiveOptimizer

Adaptive optimization based on runtime characteristics

MemoryAccessOptimizer

Memory access pattern optimizer

OptimizationAdvice

Optimization advice generated by the adaptive optimizer

PerformanceHints

Performance hints for critical code paths

PerformanceMetrics

Performance metrics for adaptive learning

StrategySelector

Strategy selector for choosing the best optimization approach

Enums

AccessPattern

Locality

Cache locality hint for prefetch operations

OptimizationStrategy

Optimization strategies available