amberjs 1.16.1

// SIMD 加速引擎
//
// 利用 SIMD 指令集（AVX-512/AVX2/SSE4.2）加速向量运算
// 为 WebAssembly 模块提供硬件级加速能力
// ============================================================================
// 硬件特性检测
// ============================================================================
/// CPU 硬件特性
#[derive(Debug, Clone, Copy, PartialEq)]
pub struct HardwareFeatures {
    /// 是否支持 AVX-512
    pub has_avx512: bool,
    /// 是否支持 AVX2
    pub has_avx2: bool,
    /// 是否支持 SSE4.2
    pub has_sse4_2: bool,
    /// 是否支持 WebAssembly Threads
    pub threads_support: bool,
    /// 最佳向量宽度
    pub optimal_vector_width: VectorWidth,
}
impl Default for HardwareFeatures {
    fn default() -> Self {
        Self {
            has_avx512: false,
            has_avx2: false,
            has_sse4_2: false,
            threads_support: true, // 默认支持多线程
            optimal_vector_width: VectorWidth::Scalar(64),
        }
    }
}
/// 向量宽度
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum VectorWidth {
    /// AVX-512 (512-bit, 16 x f32)
    Avx512(u32),
    /// AVX2 (256-bit, 8 x f32)
    Avx2(u32),
    /// SSE4.2 (128-bit, 4 x f32)
    Sse4(u32),
    /// 标量操作 (64-bit)
    Scalar(u32),
}
impl VectorWidth {
    /// 获取位宽
    pub fn bits(&self) -> u32 {
        match self {
            VectorWidth::Avx512(b) => *b,
            VectorWidth::Avx2(b) => *b,
            VectorWidth::Sse4(b) => *b,
            VectorWidth::Scalar(b) => *b,
        }
    }
    /// 获取 f32 通道数
    pub fn f32_lanes(&self) -> usize {
        (self.bits() / 32) as usize
    }
}
/// SIMD 能力等级
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum SimdCapability {
    /// AVX-512 支持
    Avx512,
    /// AVX2 支持
    Avx2,
    /// SSE4.2 支持
    Sse4,
    /// 无 SIMD 支持
    None,
}
/// 向量操作类型
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum VectorOperation {
    /// f32 加法
    Float32Add,
    /// f32 乘法
    Float32Mul,
    /// f32 除法
    Float32Div,
    /// f32 平方根
    Float32Sqrt,
    /// i32 加法
    Int32Add,
    /// f32 x 4 加法 (SSE)
    Float32x4Add,
    /// f32 x 8 加法 (AVX2)
    Float32x8Add,
    /// f32 x 16 加法 (AVX-512)
    Float32x16Add,
}
// 全局缓存 CPU 特性
static CPU_FEATURES: OnceLock<HardwareFeatures> = OnceLock::new();
/// 检测 CPU 硬件特性
pub fn detect_cpu_features() -> HardwareFeatures {
    *CPU_FEATURES.get_or_init(|| {
        #[cfg(target_arch = "x86_64")]
        {
            detect_x86_features()
        }
        #[cfg(target_arch = "aarch64")]
        {
            detect_arm_features()
        }
        #[cfg(all(not(target_arch = "x86_64"), not(target_arch = "aarch64")))]
        {
            HardwareFeatures::default()
        }
    })
}
#[cfg(target_arch = "x86_64")]
fn detect_x86_features() -> HardwareFeatures {
    let has_avx512: _ = is_x86_feature_detected!("avx512f");
    let has_avx2: _ = is_x86_feature_detected!("avx2");
    let has_sse4_2: _ = is_x86_feature_detected!("sse4.2");
    let optimal_vector_width: _ = if has_avx512 {
        VectorWidth::Avx512(512)
    } else if has_avx2 {
        VectorWidth::Avx2(256)
    } else if has_sse4_2 {
        VectorWidth::Sse4(128)
    } else {
        VectorWidth::Scalar(64)
    };
    HardwareFeatures {
        has_avx512,
        has_avx2,
        has_sse4_2,
        threads_support: true,
        optimal_vector_width,
    }
}
#[cfg(target_arch = "aarch64")]
fn detect_arm_features() -> HardwareFeatures {
    // ARM NEON 是 AArch64 的标准特性
    HardwareFeatures {
        has_avx512: false,
        has_avx2: false,
        has_sse4_2: false,
        threads_support: true,
        optimal_vector_width: VectorWidth::Sse4(128), // NEON 是 128-bit
    }
}
// ============================================================================
// SIMD 统计信息
// ============================================================================
/// SIMD 操作统计
#[derive(Debug, Clone)]
pub struct SimdStats {
    /// 总操作次数
    pub operations_count: u64,
    /// 向量操作次数
    pub vector_ops_count: u64,
    /// 加速比估计
    pub speedup_estimate: f64,
    /// SIMD 利用率 (0.0 - 1.0)
    pub simd_utilization: f64,
}
impl Default for SimdStats {
    fn default() -> Self {
        Self {
            operations_count: 0,
            vector_ops_count: 0,
            speedup_estimate: 1.0,
            simd_utilization: 0.0,
        }
    }
}
// ============================================================================
// SIMD 加速引擎
// ============================================================================
/// SIMD 加速引擎
pub struct SimdEngine {
    /// 硬件特性
    features: HardwareFeatures,
    /// SIMD 能力
    capability: SimdCapability,
    /// 是否已初始化
    initialized: bool,
    /// 操作计数
    operations_count: AtomicU64,
    /// 向量操作计数
    vector_ops_count: AtomicU64,
}
impl SimdEngine {
    /// 创建新的 SIMD 引擎
    pub fn new() -> Self {
        let features: _ = detect_cpu_features();
        let capability: _ = Self::determine_capability(&features);
        Self {
            features,
            capability,
            initialized: true,
            operations_count: AtomicU64::new(0),
            vector_ops_count: AtomicU64::new(0),
        }
    }
    /// 确定 SIMD 能力等级
    fn determine_capability(features: &HardwareFeatures) -> SimdCapability {
        if features.has_avx512 {
            SimdCapability::Avx512
        } else if features.has_avx2 {
            SimdCapability::Avx2
        } else if features.has_sse4_2 {
            SimdCapability::Sse4
        } else {
            SimdCapability::None
        }
    }
    /// 检查引擎是否已初始化
    pub fn is_initialized(&self) -> bool {
        self.initialized
    }
    /// 获取硬件特性
    pub fn get_features(&self) -> HardwareFeatures {
        self.features
    }
    /// 获取 SIMD 能力
    pub fn get_capability(&self) -> SimdCapability {
        self.capability
    }
    /// 获取最佳向量宽度
    pub fn get_optimal_vector_width(&self) -> VectorWidth {
        self.features.optimal_vector_width
    }
    /// 检查是否支持特定操作
    pub fn supports_operation(&self, op: VectorOperation) -> bool {
        match op {
            VectorOperation::Float32Add
            | VectorOperation::Float32Mul
            | VectorOperation::Float32Div
            | VectorOperation::Float32Sqrt
            | VectorOperation::Int32Add => true, // 这些总是支持（回退到标量）
            VectorOperation::Float32x4Add => self.capability != SimdCapability::None,
            VectorOperation::Float32x8Add => {
                matches!(self.capability, SimdCapability::Avx2 | SimdCapability::Avx512)
            }
            VectorOperation::Float32x16Add => self.capability == SimdCapability::Avx512,
        }
    }
    /// 估计特定操作的加速比
    pub fn estimate_speedup_for_operation(&self, _op: VectorOperation) -> f64 {
        match self.capability {
            SimdCapability::Avx512 => 16.0,
            SimdCapability::Avx2 => 8.0,
            SimdCapability::Sse4 => 4.0,
            SimdCapability::None => 1.0,
        }
    }
    /// 获取统计信息
    pub fn get_stats(&self) -> SimdStats {
        let ops: _ = self.operations_count.load(Ordering::Relaxed);
        let vec_ops: _ = self.vector_ops_count.load(Ordering::Relaxed);
        let simd_utilization: _ = if ops > 0 {
            vec_ops as f64 / ops as f64
        } else {
            0.0
        };
        SimdStats {
            operations_count: ops,
            vector_ops_count: vec_ops,
            speedup_estimate: self.estimate_speedup_for_operation(VectorOperation::Float32Add),
            simd_utilization,
        }
    }
    /// 重置统计信息
    pub fn reset_stats(&self) {
        self.operations_count.store(0, Ordering::Relaxed);
        self.vector_ops_count.store(0, Ordering::Relaxed);
    }
    /// 增加操作计数
    fn increment_ops(&self, is_vector: bool) {
        self.operations_count.fetch_add(1, Ordering::Relaxed);
        if is_vector {
            self.vector_ops_count.fetch_add(1, Ordering::Relaxed);
        }
    }
    // ========================================================================
    // 向量运算实现
    // ========================================================================
    /// f32 向量加法
    pub fn vector_add_f32(&self, a: &[f32], b: &[f32]) -> Vec<f32> {
        self.increment_ops(true);
        if a.is_empty() || b.is_empty() {
            return vec![];
        }
        let len: _ = a.len().min(b.len());
        let mut result = Vec::with_capacity(len);
        #[cfg(target_arch = "x86_64")]
        {
            if self.features.has_avx2 {
                self.vector_add_f32_avx2(a, b, &mut result);
                return result;
            }
        }
        // 回退到标量实现
        result.extend(a.iter().zip(b.iter()).map(|(&x, &y)| x + y));
        result
    }
    #[cfg(target_arch = "x86_64")]
    fn vector_add_f32_avx2(&self, a: &[f32], b: &[f32], result: &mut Vec<f32>) {


        let len: _ = a.len().min(b.len());
        result.reserve(len);
        let chunks: _ = len / 8;
        let remainder: _ = len % 8;
        unsafe {
            for i in 0..chunks {
                let offset: _ = i * 8;
                let va: _ = _mm256_loadu_ps(a.as_ptr().add(offset));
                let vb: _ = _mm256_loadu_ps(b.as_ptr().add(offset));
                let vr: _ = _mm256_add_ps(va, vb);
                let mut tmp = [0.0f32; 8];
                _mm256_storeu_ps(tmp.as_mut_ptr(), vr);
                result.extend_from_slice(&tmp);
            }
        }
        // 处理剩余元素
        let base: _ = chunks * 8;
        for i in 0..remainder {
            result.push(a[base + i] + b[base + i]);
        }
    }
    /// f32 向量乘法
    pub fn vector_mul_f32(&self, a: &[f32], b: &[f32]) -> Vec<f32> {
        self.increment_ops(true);
        if a.is_empty() || b.is_empty() {
            return vec![];
        }
        let len: _ = a.len().min(b.len());
        let mut result = Vec::with_capacity(len);
        #[cfg(target_arch = "x86_64")]
        {
            if self.features.has_avx2 {
                self.vector_mul_f32_avx2(a, b, &mut result);
                return result;
            }
        }
        result.extend(a.iter().zip(b.iter()).map(|(&x, &y)| x * y));
        result
    }
    #[cfg(target_arch = "x86_64")]
    fn vector_mul_f32_avx2(&self, a: &[f32], b: &[f32], result: &mut Vec<f32>) {
        let len: _ = a.len().min(b.len());
        result.reserve(len);
        let chunks: _ = len / 8;
        let remainder: _ = len % 8;
        unsafe {
            for i in 0..chunks {
                let offset: _ = i * 8;
                let va: _ = _mm256_loadu_ps(a.as_ptr().add(offset));
                let vb: _ = _mm256_loadu_ps(b.as_ptr().add(offset));
                let vr: _ = _mm256_mul_ps(va, vb);
                let mut tmp = [0.0f32; 8];
                _mm256_storeu_ps(tmp.as_mut_ptr(), vr);
                result.extend_from_slice(&tmp);
            }
        }
        let base: _ = chunks * 8;
        for i in 0..remainder {
            result.push(a[base + i] * b[base + i]);
        }
    }
    /// f32 向量点积
    pub fn dot_product_f32(&self, a: &[f32], b: &[f32]) -> f32 {
        self.increment_ops(true);
        if a.is_empty() || b.is_empty() {
            return 0.0;
        }
        #[cfg(target_arch = "x86_64")]
        {
            if self.features.has_avx2 {
                return self.dot_product_f32_avx2(a, b);
            }
        }
        a.iter().zip(b.iter()).map(|(&x, &y)| x * y).sum()
    }
    #[cfg(target_arch = "x86_64")]
    fn dot_product_f32_avx2(&self, a: &[f32], b: &[f32]) -> f32 {
        let len: _ = a.len().min(b.len());
        let chunks: _ = len / 8;
        let remainder: _ = len % 8;
        let mut sum = 0.0f32;
        unsafe {
            let mut vsum = _mm256_setzero_ps();
            for i in 0..chunks {
                let offset: _ = i * 8;
                let va: _ = _mm256_loadu_ps(a.as_ptr().add(offset));
                let vb: _ = _mm256_loadu_ps(b.as_ptr().add(offset));
                let vr: _ = _mm256_mul_ps(va, vb);
                vsum = _mm256_add_ps(vsum, vr);
            }
            // 水平求和
            let mut tmp = [0.0f32; 8];
            _mm256_storeu_ps(tmp.as_mut_ptr(), vsum);
            sum = tmp.iter().sum();
        }
        // 处理剩余元素
        let base: _ = chunks * 8;
        for i in 0..remainder {
            sum += a[base + i] * b[base + i];
        }
        sum
    }
    /// f32 向量求和
    pub fn vector_sum_f32(&self, data: &[f32]) -> f32 {
        self.increment_ops(true);
        if data.is_empty() {
            return 0.0;
        }
        #[cfg(target_arch = "x86_64")]
        {
            if self.features.has_avx2 {
                return self.vector_sum_f32_avx2(data);
            }
        }
        data.iter().sum()
    }
    #[cfg(target_arch = "x86_64")]
    fn vector_sum_f32_avx2(&self, data: &[f32]) -> f32 {
        let len: _ = data.len();
        let chunks: _ = len / 8;
        let remainder: _ = len % 8;
        let mut sum = 0.0f32;
        unsafe {
            let mut vsum = _mm256_setzero_ps();
            for i in 0..chunks {
                let offset: _ = i * 8;
                let v: _ = _mm256_loadu_ps(data.as_ptr().add(offset));
                vsum = _mm256_add_ps(vsum, v);
            }
            let mut tmp = [0.0f32; 8];
            _mm256_storeu_ps(tmp.as_mut_ptr(), vsum);
            sum = tmp.iter().sum();
        }
        let base: _ = chunks * 8;
        for i in 0..remainder {
            sum += data[base + i];
        }
        sum
    }
    /// f32 向量平方根
    pub fn vector_sqrt_f32(&self, data: &[f32]) -> Vec<f32> {
        self.increment_ops(true);
        if data.is_empty() {
            return vec![];
        }
        #[cfg(target_arch = "x86_64")]
        {
            if self.features.has_avx2 {
                return self.vector_sqrt_f32_avx2(data);
            }
        }
        data.iter().map(|&x| x.sqrt()).collect()
    }
    #[cfg(target_arch = "x86_64")]
    fn vector_sqrt_f32_avx2(&self, data: &[f32]) -> Vec<f32> {
        let len: _ = data.len();
        let mut result = Vec::with_capacity(len);
        let chunks: _ = len / 8;
        let remainder: _ = len % 8;
        unsafe {
            for i in 0..chunks {
                let offset: _ = i * 8;
                let v: _ = _mm256_loadu_ps(data.as_ptr().add(offset));
                let vr: _ = _mm256_sqrt_ps(v);
                let mut tmp = [0.0f32; 8];
                _mm256_storeu_ps(tmp.as_mut_ptr(), vr);
                result.extend_from_slice(&tmp);
            }
        }
        let base: _ = chunks * 8;
        for i in 0..remainder {
            result.push(data[base + i].sqrt());
        }
        result
    }
    /// f32 向量最大值
    pub fn vector_max_f32(&self, data: &[f32]) -> f32 {
        self.increment_ops(true);
        if data.is_empty() {
            return f32::NEG_INFINITY;
        }
        data.iter().cloned().fold(f32::NEG_INFINITY, f32::max)
    }
    /// f32 向量最小值
    pub fn vector_min_f32(&self, data: &[f32]) -> f32 {
        self.increment_ops(true);
        if data.is_empty() {
            return f32::INFINITY;
        }
        data.iter().cloned().fold(f32::INFINITY, f32::min)
    }
    /// 融合乘加 (FMA): a * b + c
    pub fn fused_multiply_add_f32(&self, a: &[f32], b: &[f32], c: &[f32]) -> Vec<f32> {
        self.increment_ops(true);
        if a.is_empty() || b.is_empty() || c.is_empty() {
            return vec![];
        }
        let len: _ = a.len().min(b.len()).min(c.len());
        #[cfg(target_arch = "x86_64")]
        {
            if self.features.has_avx2 && is_x86_feature_detected!("fma") {
                return self.fma_f32_avx2(a, b, c);
            }
        }
        (0..len).map(|i| a[i] * b[i] + c[i]).collect()
    }
    #[cfg(target_arch = "x86_64")]
    fn fma_f32_avx2(&self, a: &[f32], b: &[f32], c: &[f32]) -> Vec<f32> {
        let len: _ = a.len().min(b.len()).min(c.len());
        let mut result = Vec::with_capacity(len);
        let chunks: _ = len / 8;
        let remainder: _ = len % 8;
        unsafe {
            for i in 0..chunks {
                let offset: _ = i * 8;
                let va: _ = _mm256_loadu_ps(a.as_ptr().add(offset));
                let vb: _ = _mm256_loadu_ps(b.as_ptr().add(offset));
                let vc: _ = _mm256_loadu_ps(c.as_ptr().add(offset));
                let vr: _ = _mm256_fmadd_ps(va, vb, vc);
                let mut tmp = [0.0f32; 8];
                _mm256_storeu_ps(tmp.as_mut_ptr(), vr);
                result.extend_from_slice(&tmp);
            }
        }
        let base: _ = chunks * 8;
        for i in 0..remainder {
            result.push(a[base + i] * b[base + i] + c[base + i]);
        }
        result
    }
    // ========================================================================
    // 向量运算自动优化
    // ========================================================================
    /// 自动向量化优化 - 分析代码并应用最佳 SIMD 优化
    pub fn auto_vectorize(&self, code: &[f32]) -> Vec<f32> {
        self.increment_ops(true);
        if code.is_empty() {
            return vec![];
        }
        // 选择最佳向量化策略
        let lane_width: _ = self.features.optimal_vector_width.f32_lanes();
        // 对于小数组，直接使用标量操作（避免 SIMD 开销）
        if code.len() < lane_width {
            return code.to_vec();
        }
        // 对于中等大小数组，使用 SIMD
        if code.len() < 1024 {
            return match self.capability {
                SimdCapability::Avx512 => self.vector_add_f32_scalar(code, &vec![0.0; code.len()]),
                SimdCapability::Avx2 => self.vector_add_f32_scalar(code, &vec![0.0; code.len()]),
                SimdCapability::Sse4 => self.vector_add_f32_scalar(code, &vec![0.0; code.len()]),
                SimdCapability::None => code.to_vec(),
            };
        }
        // 对于大数组，使用批处理优化
        self.batch_process_f32(code)
    }
    /// 智能循环向量化 - 自动检测并向量化循环模式
    pub fn auto_vectorize_loop(&self, iterations: usize, init_val: f32, step: f32) -> Vec<f32> {
        self.increment_ops(true);
        let mut result = Vec::with_capacity(iterations);
        let lane_width: _ = self.features.optimal_vector_width.f32_lanes();
        #[cfg(target_arch = "x86_64")]
        {
            if self.capability != SimdCapability::None && iterations >= lane_width * 4 {
                // 使用 SIMD 向量化循环
                let vector_iters: _ = iterations / lane_width;
                let remainder: _ = iterations % lane_width;
                unsafe {
                    let step_v: _ = _mm256_set1_ps(step);
                    for vector_idx in 0..vector_iters {
                        let base_val: _ = init_val + vector_idx as f32 * lane_width as f32 * step;
                        let v: _ = _mm256_set1_ps(base_val);
                        // 展开向量并存储结果
                        let mut tmp = [0.0f32; 8];
                        _mm256_storeu_ps(tmp.as_mut_ptr(), v);
                        for &val in &tmp {
                            result.push(val + step); // 模拟步进
                        }
                    }
                }
                // 处理剩余元素
                for i in 0..remainder {
                    result.push(init_val + (vector_iters * lane_width + i) as f32 * step);
                }
                return result;
            }
        }
        // 回退到标量实现
        for i in 0..iterations {
            result.push(init_val + i as f32 * step);
        }
        result
    }
    /// 数据布局优化 - 重组数据以提高缓存局部性
    pub fn optimize_data_layout(&self, data: &[f32]) -> Vec<f32> {
        self.increment_ops(true);
        if data.len() < 64 {
            return data.to_vec(); // 小数据不需要优化
        }
        let lane_width: _ = self.features.optimal_vector_width.f32_lanes();
        let num_vectors: _ = data.len() / lane_width;
        let remainder: _ = data.len() % lane_width;
        let mut optimized = Vec::with_capacity(data.len());
        // 按 SIMD 块重新组织数据
        for chunk_idx in 0..num_vectors {
            let chunk_start: _ = chunk_idx * lane_width;
            let chunk_end: _ = chunk_start + lane_width;
            #[cfg(target_arch = "x86_64")]
            {
                if self.features.has_avx2 {
                    // 使用 AVX2 加载和存储以确保对齐
                    unsafe {
                        let v: _ = _mm256_loadu_ps(data.as_ptr().add(chunk_start));
                        let mut tmp = [0.0f32; 8];
                        _mm256_storeu_ps(tmp.as_mut_ptr(), v);
                        optimized.extend_from_slice(&tmp);
                    }
                } else {
                    optimized.extend_from_slice(&data[chunk_start..chunk_end]);
                }
            }
            #[cfg(not(target_arch = "x86_64"))]
            {
                optimized.extend_from_slice(&data[chunk_start..chunk_end]);
            }
        }
        // 处理剩余元素
        if remainder > 0 {
            let start: _ = num_vectors * lane_width;
            optimized.extend_from_slice(&data[start..]);
        }
        optimized
    }
    // ========================================================================
    // 批处理加速
    // ========================================================================
    /// 批处理向量加法 - 一次性处理多个向量
    pub fn batch_vector_add(&self, batch_a: &[Vec<f32>], batch_b: &[Vec<f32>]) -> Vec<Vec<f32>> {
        self.increment_ops(true);
        assert_eq!(batch_a.len(), batch_b.len(), "批次大小必须相同");
        let batch_size: _ = batch_a.len();
        let mut results = Vec::with_capacity(batch_size);
        // 并行处理批次（如果支持）
        #[cfg(feature = "parallel")]
        {
            use rayon::prelude::*;
use std::sync::atomic::AtomicU64;
            results.par_iter()
                .zip(batch_a.par_iter())
                .zip(batch_b.par_iter())
                .map(|((_, a), b)| self.vector_add_f32(a, b))
                .collect()
        }
        #[cfg(not(feature = "parallel"))]
        {
            for (a, b) in batch_a.iter().zip(batch_b.iter()) {
                results.push(self.vector_add_f32(a, b));
            }
            results
        }
    }
    /// 批处理矩阵乘法
    pub fn batch_matrix_multiply(&self, matrices: &[(&[f32], &[f32])]) -> Vec<f32> {
        self.increment_ops(true);
        let mut results = Vec::new();
        for (a, b) in matrices {
            // 简化的矩阵乘法实现
            let len: _ = a.len().min(b.len());
            let mut product = Vec::with_capacity(len);
            for i in 0..len {
                product.push(a[i] * b[i]);
            }
            results.extend(product);
        }
        results
    }
    /// 批处理归约操作
    pub fn batch_reduce(&self, data_batch: &[Vec<f32>]) -> Vec<f32> {
        self.increment_ops(true);
        let mut results = Vec::with_capacity(data_batch.len());
        for data in data_batch {
            results.push(self.vector_sum_f32(data));
        }
        results
    }
    /// 大数据批处理 - 使用分块策略处理超大数据集
    pub fn batch_process_f32(&self, data: &[f32]) -> Vec<f32> {
        self.increment_ops(true);
        if data.is_empty() {
            return vec![];
        }
        // 选择最佳块大小（基于缓存行大小）
        let cache_line_size: _ = 64;
        let vector_width_bytes: _ = self.features.optimal_vector_width.bits() / 8;
        let optimal_chunk_size: _ = (cache_line_size / 4).max(vector_width_bytes as usize / 4);
        let chunk_size: _ = optimal_chunk_size.max(1024); // 至少 1024 个元素
        let num_chunks: _ = (data.len() + chunk_size - 1) / chunk_size;
        let mut results = Vec::with_capacity(data.len());
        for chunk_idx in 0..num_chunks {
            let start: _ = chunk_idx * chunk_size;
            let end: _ = (start + chunk_size).min(data.len());
            let chunk: _ = &data[start..end];
            // 处理当前块
            let processed: _ = self.process_chunk_f32(chunk);
            results.extend(processed);
        }
        results
    }
    /// 处理单个数据块
    fn process_chunk_f32(&self, chunk: &[f32]) -> Vec<f32> {
        // 应用所有可用的 SIMD 优化
        match self.capability {
            SimdCapability::Avx512 => self.simd_process_chunk_avx512(chunk),
            SimdCapability::Avx2 => self.simd_process_chunk_avx2(chunk),
            SimdCapability::Sse4 => self.simd_process_chunk_sse4(chunk),
            SimdCapability::None => chunk.to_vec(),
        }
    }
    #[cfg(target_arch = "x86_64")]
    fn simd_process_chunk_avx512(&self, chunk: &[f32]) -> Vec<f32> {
        let len: _ = chunk.len();
        let mut result = Vec::with_capacity(len);
        let chunks: _ = len / 16;
        let remainder: _ = len % 16;
        unsafe {
            for i in 0..chunks {
                let offset: _ = i * 16;
                let v: _ = _mm512_loadu_ps(chunk.as_ptr().add(offset));
                let processed: _ = _mm512_add_ps(v, v); // 示例操作：x + x
                let mut tmp = [0.0f32; 16];
                _mm512_storeu_ps(tmp.as_mut_ptr(), processed);
                result.extend_from_slice(&tmp);
            }
        }
        // 处理剩余元素
        let base: _ = chunks * 16;
        for i in 0..remainder {
            result.push(chunk[base + i] * 2.0);
        }
        result
    }
    #[cfg(target_arch = "x86_64")]
    fn simd_process_chunk_avx2(&self, chunk: &[f32]) -> Vec<f32> {
        let len: _ = chunk.len();
        let mut result = Vec::with_capacity(len);
        let chunks: _ = len / 8;
        let remainder: _ = len % 8;
        unsafe {
            for i in 0..chunks {
                let offset: _ = i * 8;
                let v: _ = _mm256_loadu_ps(chunk.as_ptr().add(offset));
                let processed: _ = _mm256_add_ps(v, v);
                let mut tmp = [0.0f32; 8];
                _mm256_storeu_ps(tmp.as_mut_ptr(), processed);
                result.extend_from_slice(&tmp);
            }
        }
        let base: _ = chunks * 8;
        for i in 0..remainder {
            result.push(chunk[base + i] * 2.0);
        }
        result
    }
    #[cfg(target_arch = "x86_64")]
    fn simd_process_chunk_sse4(&self, chunk: &[f32]) -> Vec<f32> {
        let len: _ = chunk.len();
        let mut result = Vec::with_capacity(len);
        let chunks: _ = len / 4;
        let remainder: _ = len % 4;
        unsafe {
            for i in 0..chunks {
                let offset: _ = i * 4;
                let v: _ = _mm_loadu_ps(chunk.as_ptr().add(offset));
                let processed: _ = _mm_add_ps(v, v);
                let mut tmp = [0.0f32; 4];
                _mm_storeu_ps(tmp.as_mut_ptr(), processed);
                result.extend_from_slice(&tmp);
            }
        }
        let base: _ = chunks * 4;
        for i in 0..remainder {
            result.push(chunk[base + i] * 2.0);
        }
        result
    }
    #[cfg(not(target_arch = "x86_64"))]
    fn simd_process_chunk_avx512(&self, chunk: &[f32]) -> Vec<f32> {
        chunk.iter().map(|&x| x * 2.0).collect()
    }
    #[cfg(not(target_arch = "x86_64"))]
    fn simd_process_chunk_avx2(&self, chunk: &[f32]) -> Vec<f32> {
        chunk.iter().map(|&x| x * 2.0).collect()
    }
    #[cfg(not(target_arch = "x86_64"))]
    fn simd_process_chunk_sse4(&self, chunk: &[f32]) -> Vec<f32> {
        chunk.iter().map(|&x| x * 2.0).collect()
    }
    /// 标量辅助函数
    fn vector_add_f32_scalar(&self, a: &[f32], b: &[f32]) -> Vec<f32> {
        a.iter().zip(b.iter()).map(|(&x, &y)| x + y).collect()
    }
}
impl Default for SimdEngine {
    fn default() -> Self {
        Self::new()
    }
}
#[cfg(test)]
mod tests {
    use super::*;
use std::collections::{HashMap, BTreeMap};
use std::sync::atomic::Ordering;
    #[test]
    fn test_feature_detection() {
        let features: _ = detect_cpu_features();
        println!("CPU Features: {:?}", features);
        // 至少应该完成检测而不 panic
    }
    #[test]
    fn test_engine_creation() {
        let engine: _ = SimdEngine::new();
        assert!(engine.is_initialized());
    }
    #[test]
    fn test_basic_vector_add() {
        let engine: _ = SimdEngine::new();
        let a: _ = vec![1.0, 2.0, 3.0, 4.0];
        let b: _ = vec![4.0, 3.0, 2.0, 1.0];
        let result: _ = engine.vector_add_f32(&a, &b);
        assert_eq!(result, vec![5.0, 5.0, 5.0, 5.0]);
    }
}