amberjs 1.16.1

JavaScript/TypeScript runtime built with Rust and V8
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145

// 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()
    }
}