amberjs 1.16.1

// 向量化优化器
// Stage 92 Phase 4: 实现 SIMD 向量化优化


use std::collections::{HashMap, HashSet};
use serde::{Deserialize, Serialize};
/// SIMD 指令类型
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum SimdInstructionType {
    /// SSE 128 位
    Sse128,
    /// AVX 256 位
    Avx256,
    /// AVX-512 512 位
    Avx512,
}
/// 向量化机会
#[derive(Debug, Clone)]
pub struct VectorizationOpportunity {
    pub loop_id: u64,
    pub instruction_type: SimdInstructionType,
    pub start_index: usize,
    pub end_index: usize,
    pub vector_width: usize,
    pub estimated_speedup: f64,
    pub dependencies: Vec<u64>,
    pub memory_alignment: usize,
}
/// 向量化优化结果
#[derive(Debug, Clone)]
pub struct VectorizationResult {
    pub loop_id: u64,
    pub original_instructions: Vec<String>,
    pub vectorized_instructions: Vec<String>,
    pub simd_type: SimdInstructionType,
    pub speedup_factor: f64,
    pub memory_operations: Vec<String>,
    pub safety_checks: Vec<String>,
}
/// 向量化优化配置
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VectorizationConfig {
    /// 启用向量化优化
    pub enabled: bool,
    /// 最小循环迭代次数才考虑向量化
    pub min_loop_iterations: usize,
    /// 最小速度提升阈值
    pub min_speedup_threshold: f64,
    /// 启用自动对齐
    pub enable_alignment_optimization: bool,
    /// 支持的 SIMD 指令集
    pub supported_simd_types: Vec<SimdInstructionType>,
    /// 向量化安全检查
    pub strict_safety_checks: bool,
}
impl Default for VectorizationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            min_loop_iterations: 100,
            min_speedup_threshold: 1.5,
            enable_alignment_optimization: true,
            supported_simd_types: vec![
                SimdInstructionType::Sse128,
                SimdInstructionType::Avx256,
            ],
            strict_safety_checks: true,
        }
    }
}
/// 向量化优化器
pub struct VectorizationOptimizer {
    config: VectorizationConfig,
    optimization_history: Vec<VectorizationResult>,
    alignment_cache: HashMap<u64, usize>,
}
impl VectorizationOptimizer {
    /// 创建新的向量化优化器
    pub fn new(config: VectorizationConfig) -> Self {
        Self {
            config,
            optimization_history: Vec::new(),
            alignment_cache: HashMap::new(),
        }
    }
    /// 分析代码中的向量化机会
    pub fn analyze_vectorization_opportunities(
        &self,
        ir_code: &[String],
    ) -> Vec<VectorizationOpportunity> {
        let mut opportunities = Vec::new();
        // 简化的循环检测和向量化机会识别
        for (i, instruction) in ir_code.iter().enumerate() {
            if self.is_loop_instruction(instruction) {
                if let Some(opportunity) = self.detect_vectorization_opportunity(i, instruction, ir_code) {
                    opportunities.push(opportunity);
                }
            }
        }
        opportunities
    }
    /// 检测向量化机会
    fn detect_vectorization_opportunity(
        &self,
        index: usize,
        instruction: &str,
        ir_code: &[String],
    ) -> Option<VectorizationOpportunity> {
        // 简化的向量化机会检测
        // 实际实现需要更复杂的分析
        // 检查是否是可向量化的循环
        if !self.is_vectorizable_loop(instruction) {
            return None;
        }
        // 估计循环迭代次数（简化）
        let estimated_iterations: _ = self.estimate_loop_iterations(instruction);
        if estimated_iterations < self.config.min_loop_iterations {
            return None;
        }
        // 选择合适的 SIMD 指令类型
        let simd_type: _ = self.select_optimal_simd_type();
        // 计算估计的性能提升
        let speedup: _ = self.estimate_speedup(simd_type, estimated_iterations);
        if speedup < self.config.min_speedup_threshold {
            return None;
        }
        Some(VectorizationOpportunity {
            loop_id: index as u64,
            instruction_type: simd_type,
            start_index: index,
            end_index: index + 10, // 简化：假设循环体长度为 10
            vector_width: self.get_vector_width(simd_type),
            estimated_speedup: speedup,
            dependencies: self.analyze_dependencies(index, ir_code),
            memory_alignment: self.analyze_memory_alignment(index, ir_code),
        })
    }
    /// 执行向量化优化
    pub fn vectorize(
        &mut self,
        opportunity: &VectorizationOpportunity,
        ir_code: &[String],
    ) -> Result<VectorizationResult, String> {
        if !self.config.enabled {
            return Err("向量化优化已禁用".to_string());
        }
        // 验证向量化安全性
        if self.config.strict_safety_checks {
            if let Err(msg) = self.verify_safety(opportunity, ir_code) {
                return Err(format!("向量化安全检查失败: {}", msg));
            }
        }
        // 生成向量化代码
        let vectorized_instructions: _ = self.generate_vectorized_code(opportunity, ir_code)?;
        // 执行内存对齐优化
        let memory_operations: _ = if self.config.enable_alignment_optimization {
            self.optimize_memory_alignment(opportunity, ir_code)
        } else {
            vec![]
        };
        // 执行安全检查
        let safety_checks: _ = self.perform_safety_checks(opportunity);
        let result: _ = VectorizationResult {
            loop_id: opportunity.loop_id,
            original_instructions: ir_code[opportunity.start_index..opportunity.end_index].to_vec(),
            vectorized_instructions,
            simd_type: opportunity.instruction_type,
            speedup_factor: opportunity.estimated_speedup,
            memory_operations,
            safety_checks,
        };
        // 记录到历史
        self.optimization_history.push(result.clone());
        Ok(result)
    }
    /// 检查是否是循环指令
    fn is_loop_instruction(&self, instruction: &str) -> bool {
        instruction.contains("loop") || instruction.contains("for") || instruction.contains("while")
    }
    /// 检查循环是否可向量化
    fn is_vectorizable_loop(&self, instruction: &str) -> bool {
        // 简化检查：检查是否存在数据依赖
        // 实际实现需要更复杂的依赖分析
        // 检查是否有向量化友好的操作
        let vectorizable_ops: _ = ["add", "mul", "load", "store", "fadd", "fmul"];
        vectorizable_ops.iter().any(|op| instruction.contains(op))
    }
    /// 估计循环迭代次数
    fn estimate_loop_iterations(&self, instruction: &str) -> usize {
        // 简化实现：基于指令模式猜测
        // 实际实现需要从循环边界分析
        if instruction.contains("1000") {
            1000
        } else if instruction.contains("100") {
            100
        } else {
            1000 // 默认值
        }
    }
    /// 选择最优的 SIMD 指令类型
    fn select_optimal_simd_type(&self) -> SimdInstructionType {
        // 简化策略：优先选择 AVX256，然后是 SSE128
        if self.config.supported_simd_types.contains(&SimdInstructionType::Avx256) {
            SimdInstructionType::Avx256
        } else {
            SimdInstructionType::Sse128
        }
    }
    /// 计算估计的性能提升
    fn estimate_speedup(&self, simd_type: SimdInstructionType, iterations: usize) -> f64 {
        let vector_width: _ = self.get_vector_width(simd_type);
        // 简化计算：理论最大速度提升 = 向量宽度
        // 实际会有开销，所以取 80%
        (vector_width as f64) * 0.8
    }
    /// 获取向量宽度
    fn get_vector_width(&self, simd_type: SimdInstructionType) -> usize {
        match simd_type {
            SimdInstructionType::Sse128 => 4,   // 128位 / 32位 = 4个float
            SimdInstructionType::Avx256 => 8,   // 256位 / 32位 = 8个float
            SimdInstructionType::Avx512 => 16,  // 512位 / 32位 = 16个float
        }
    }
    /// 分析依赖关系
    fn analyze_dependencies(&self, _index: usize, _ir_code: &[String]) -> Vec<u64> {
        // 简化实现：返回空依赖列表
        // 实际实现需要数据流分析
        vec![]
    }
    /// 分析内存对齐
    fn analyze_memory_alignment(&self, index: usize, _ir_code: &[String]) -> usize {
        // 检查缓存中是否有记录
        *self.alignment_cache.get(&(index as u64)).unwrap_or(&16)
    }
    /// 验证向量化安全性
    fn verify_safety(&self, opportunity: &VectorizationOpportunity, ir_code: &[String]) -> Result<(), String> {
        // 检查数据依赖
        if !opportunity.dependencies.is_empty() {
            return Err("存在数据依赖，不能向量化".to_string());
        }
        // 检查内存对齐
        if opportunity.memory_alignment < 16 {
            return Err("内存对齐不足".to_string());
        }
        // 检查循环不变式
        if !self.check_loop_invariants(opportunity, ir_code) {
            return Err("循环中存在不变式代码".to_string());
        }
        Ok(())
    }
    /// 检查循环不变式
    fn check_loop_invariants(&self, opportunity: &VectorizationOpportunity, ir_code: &[String]) -> bool {
        // 简化检查：假设所有循环都是安全的
        true
    }
    /// 生成向量化代码
    fn generate_vectorized_code(
        &self,
        opportunity: &VectorizationOpportunity,
        ir_code: &[String],
    ) -> Result<Vec<String>, String> {
        let mut vectorized = Vec::new();
        let original_instructions: _ = &ir_code[opportunity.start_index..opportunity.end_index];
        for instr in original_instructions {
            let vectorized_instr: _ = self.vectorize_instruction(instr, opportunity)?;
            vectorized.push(vectorized_instr);
        }
        Ok(vectorized)
    }
    /// 向量化单条指令
    fn vectorize_instruction(
        &self,
        instruction: &str,
        opportunity: &VectorizationOpportunity,
    ) -> Result<String, String> {
        match opportunity.instruction_type {
            SimdInstructionType::Sse128 => self.vectorize_with_sse(instruction),
            SimdInstructionType::Avx256 => self.vectorize_with_avx(instruction),
            SimdInstructionType::Avx512 => self.vectorize_with_avx512(instruction),
        }
    }
    /// 使用 SSE 向量化
    fn vectorize_with_sse(&self, instruction: &str) -> Result<String, String> {
        if instruction.contains("add") {
            Ok(instruction.replace("add", "paddd").replace("fadd", "addps"))
        } else if instruction.contains("mul") {
            Ok(instruction.replace("mul", "pmulld").replace("fmul", "mulps"))
        } else if instruction.contains("load") {
            Ok(instruction.replace("load", "loadu").replace("store", "storeu"))
        } else {
            Ok(instruction.to_string())
        }
    }
    /// 使用 AVX 向量化
    fn vectorize_with_avx(&self, instruction: &str) -> Result<String, String> {
        if instruction.contains("add") {
            Ok(instruction.replace("add", "vpaddd").replace("fadd", "vaddps"))
        } else if instruction.contains("mul") {
            Ok(instruction.replace("mul", "vpmulld").replace("fmul", "vmulps"))
        } else if instruction.contains("load") {
            Ok(instruction.replace("load", "vloadu").replace("store", "vstoreu"))
        } else {
            Ok(instruction.to_string())
        }
    }
    /// 使用 AVX-512 向量化
    fn vectorize_with_avx512(&self, instruction: &str) -> Result<String, String> {
        if instruction.contains("add") {
            Ok(instruction.replace("add", "vpaddd").replace("fadd", "vaddps"))
        } else if instruction.contains("mul") {
            Ok(instruction.replace("mul", "vpmulld").replace("fmul", "vmulps"))
        } else if instruction.contains("load") {
            Ok(instruction.replace("load", "zloadu").replace("store", "zstoreu"))
        } else {
            Ok(instruction.to_string())
        }
    }
    /// 优化内存对齐
    fn optimize_memory_alignment(
        &self,
        opportunity: &VectorizationOpportunity,
        ir_code: &[String],
    ) -> Vec<String> {
        let mut alignment_ops = Vec::new();
        // 生成对齐提示
        match opportunity.instruction_type {
            SimdInstructionType::Sse128 => {
                alignment_ops.push(format!("# 对齐到 16 字节边界"));
            }
            SimdInstructionType::Avx256 => {
                alignment_ops.push(format!("# 对齐到 32 字节边界"));
            }
            SimdInstructionType::Avx512 => {
                alignment_ops.push(format!("# 对齐到 64 字节边界"));
            }
        }
        alignment_ops
    }
    /// 执行安全检查
    fn perform_safety_checks(&self, opportunity: &VectorizationOpportunity) -> Vec<String> {
        let mut checks = vec!["检查数据依赖".to_string()];
        if opportunity.memory_alignment >= 16 {
            checks.push("内存对齐检查通过".to_string());
        }
        if opportunity.estimated_speedup >= self.config.min_speedup_threshold {
            checks.push("性能提升阈值满足".to_string());
        }
        checks
    }
    /// 获取优化历史
    pub fn get_optimization_history(&self) -> &[VectorizationResult] {
        &self.optimization_history
    }
    /// 清除优化历史
    pub fn clear_history(&mut self) {
        self.optimization_history.clear();
    }
}
#[cfg(test)]
mod tests {
    use super::*;
use std::collections::{BTreeMap};
    #[test]
    fn test_vectorization_optimizer_creation() {
        let config: _ = VectorizationConfig::default();
        let optimizer: _ = VectorizationOptimizer::new(config);
        assert!(optimizer.get_optimization_history().is_empty());
    }
    #[test]
    fn test_vectorization_opportunity_detection() {
        let config: _ = VectorizationConfig::default();
        let optimizer: _ = VectorizationOptimizer::new(config);
        let ir_code: _ = vec![
            "loop_start".to_string(),
            "load %0".to_string(),
            "load %1".to_string(),
            "fadd".to_string(),
            "store %2".to_string(),
            "loop_end".to_string(),
        ];
        let opportunities: _ = optimizer.analyze_vectorization_opportunities(&ir_code);
        // 根据我们的简化检测，应该能找到一些机会
        assert!(!opportunities.is_empty());
    }
    #[test]
    fn test_vectorization_with_avx() {
        let config: _ = VectorizationConfig::default();
        let mut optimizer = VectorizationOptimizer::new(config);
        let opportunity: _ = VectorizationOpportunity {
            loop_id: 1,
            instruction_type: SimdInstructionType::Avx256,
            start_index: 0,
            end_index: 3,
            vector_width: 8,
            estimated_speedup: 6.4,
            dependencies: vec![],
            memory_alignment: 32,
        };
        let ir_code: _ = vec![
            "fadd %0, %1, %2".to_string(),
            "fmul %2, %3, %4".to_string(),
        ];
        let result: _ = optimizer.vectorize(&opportunity, &ir_code).unwrap();
        assert!(result.simd_type == SimdInstructionType::Avx256);
        assert!(result.speedup_factor > 1.0);
    }
    #[test]
    fn test_vectorization_disabled() {
        let mut config = VectorizationConfig::default();
        config.enabled = false;
        let mut optimizer = VectorizationOptimizer::new(config);
        let opportunity: _ = VectorizationOpportunity {
            loop_id: 1,
            instruction_type: SimdInstructionType::Sse128,
            start_index: 0,
            end_index: 3,
            vector_width: 4,
            estimated_speedup: 3.2,
            dependencies: vec![],
            memory_alignment: 16,
        };
        let ir_code: _ = vec!["fadd %0, %1, %2".to_string()];
        let result: _ = optimizer.vectorize(&opportunity, &ir_code);
        assert!(result.is_err());
    }
}