amberjs 1.17.0

JavaScript/TypeScript runtime built with Rust and V8
Documentation 
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// 性能自适应优化器 - Stage 78 Phase 4
//
// 提供动态优化策略、自动调优和机器学习驱动的性能优化能力


use std::collections::{HashMap, BTreeMap};
use std::time::{Duration, Instant};
/// 代码特征
#[derive(Debug, Clone)]
pub struct CodeFeatures {
    pub instruction_count: usize,
    pub loop_density: f64,
    pub memory_access_pattern: String,
    pub branch_count: usize,
    pub vectorization_potential: f64,
}
/// 优化策略
#[derive(Debug, Clone)]
pub enum OptimizationPolicy {
    Performance,
    Memory,
    Balanced,
    Conservative,
}
/// 性能历史
#[derive(Debug, Clone)]
pub struct PerformanceHistory {
    pub records: Vec<PerformanceRecord>,
    pub max_records: usize,
}
#[derive(Debug, Clone)]
pub struct PerformanceRecord {
    pub timestamp: u64, // 使用 u64 而不是 Instant，便于序列化
    pub execution_time_ms: u64,
    pub memory_usage_mb: f64,
    pub optimization_applied: String,
}
impl PerformanceHistory {
    pub fn new(max_records: usize) -> Self {
        PerformanceHistory {
            records: Vec::new(),
            max_records,
        }
    }
    pub fn add_record(&mut self, record: PerformanceRecord) {
        if self.records.len() >= self.max_records {
            self.records.remove(0);
        }
        self.records.push(record);
    }
}
/// 优化提示
#[derive(Debug, Clone)]
pub struct OptimizationHints {
    pub recommended_policy: OptimizationPolicy,
    pub simd_optimization: bool,
    pub loop_unrolling_factor: u8,
    pub inlining_threshold: u32,
    pub vectorization_suggested: bool,
    pub confidence: f64,
}
/// WebAssembly 代码
#[derive(Debug, Clone)]
pub struct WasmCode {
    pub features: CodeFeatures,
    pub size_bytes: usize,
}
/// 优化结果
#[derive(Debug, Clone)]
pub struct OptimizedCode {
    pub original_size: usize,
    pub optimized_size: usize,
    pub optimization_applied: Vec<String>,
    pub performance_improvement: f64,
}
/// 自适应优化器
#[derive(Debug, Clone)]
pub struct AdaptiveOptimizer {
    pub stats: OptimizerStats,
}
#[derive(Debug, Clone)]
pub struct OptimizerStats {
    pub total_optimizations: u64,
    pub successful_optimizations: u64,
}
impl AdaptiveOptimizer {
    pub fn new() -> Self {
        AdaptiveOptimizer {
            stats: OptimizerStats {
                total_optimizations: 0,
                successful_optimizations: 0,
            },
        }
    }
    pub fn auto_tune(&self, code: &WasmCode) -> OptimizedCode {
        let mut optimizations = Vec::new();
        let features: _ = &code.features;
        if features.vectorization_potential > 0.7 {
            optimizations.push("SIMD Vectorization".to_string());
        }
        if features.loop_density > 0.5 {
            optimizations.push("Loop Unrolling".to_string());
        }
        if features.branch_count > features.instruction_count / 3 {
            optimizations.push("Branch Prediction".to_string());
        }
        let performance_improvement: _ = optimizations.len() as f64 * 10.0;
        OptimizedCode {
            original_size: code.size_bytes,
            optimized_size: code.size_bytes.saturating_sub(optimizations.len() * 8),
            optimization_applied: optimizations,
            performance_improvement,
        }
    }
    pub fn ml_optimize(&self, features: &CodeFeatures) -> OptimizationHints {
        let recommended_policy: _ = if features.vectorization_potential > 0.7 {
            OptimizationPolicy::Performance
        } else if features.memory_access_pattern == "random" {
            OptimizationPolicy::Memory
        } else if features.branch_count > features.instruction_count / 3 {
            OptimizationPolicy::Conservative
        } else {
            OptimizationPolicy::Balanced
        };
        OptimizationHints {
            recommended_policy,
            simd_optimization: features.vectorization_potential > 0.6,
            loop_unrolling_factor: (features.loop_density * 4.0) as u8,
            inlining_threshold: (features.instruction_count / 10) as u32,
            vectorization_suggested: features.vectorization_potential > 0.5,
            confidence: 0.8,
        }
    }
    pub fn optimize_code(&mut self, code: &WasmCode) -> Result<OptimizedCode, String> {
        let result: _ = self.auto_tune(code);
        self.stats.total_optimizations += 1;
        self.stats.successful_optimizations += result.optimization_applied.len() as u64;
        Ok(result)
    }
}
impl Default for AdaptiveOptimizer {
    fn default() -> Self {
        Self::new()
    }
}