ruvector_core/

simd_intrinsics.rs

//! Custom SIMD intrinsics for performance-critical operations
//!
//! This module provides hand-optimized SIMD implementations:
//! - AVX2/AVX-512 for x86_64 processors
//! - NEON for ARM64/Apple Silicon processors (M1/M2/M3/M4)
//!
//! Distance calculations and other vectorized operations are automatically
//! dispatched to the optimal implementation based on the target architecture.
//!
//! ## Features
//!
//! - **AVX-512 Support**: 512-bit operations processing 16 floats per iteration
//! - **INT8 Quantized Operations**: SIMD-accelerated quantized vector operations
//! - **Batch Operations**: Cache-optimized batch distance calculations
//! - **NEON Optimizations**: Prefetch hints and loop unrolling for ARM64
//!
//! ## Performance Optimizations (v2)
//!
//! - **Loop Unrolling**: 4x unrolled loops for better instruction-level parallelism
//! - **Prefetch Hints**: Software prefetching for large vectors (>256 elements)
//! - **FMA Instructions**: Fused multiply-add for improved throughput and accuracy
//! - **Efficient Horizontal Sum**: Optimized reduction operations

#[cfg(target_arch = "x86_64")]
use std::arch::x86_64::*;

#[cfg(target_arch = "aarch64")]
use std::arch::aarch64::*;

/// Prefetch distance in cache lines (tuned for L1 cache, 64 bytes = 16 floats)
#[allow(dead_code)]
const PREFETCH_DISTANCE: usize = 64;

/// SIMD-optimized euclidean distance
/// Uses AVX-512 > AVX2 on x86_64, NEON on ARM64/Apple Silicon, falls back to scalar otherwise
///
/// # Optimizations for M4 Pro (ARM64)
/// - Uses 4x loop unrolling for vectors >= 64 elements
/// - FMA instructions for improved throughput
/// - Optimized horizontal reduction via `vaddvq_f32`
#[inline(always)]
pub fn euclidean_distance_simd(a: &[f32], b: &[f32]) -> f32 {
    #[cfg(target_arch = "x86_64")]
    {
        #[cfg(feature = "simd-avx512")]
        {
            if is_x86_feature_detected!("avx512f") {
                return unsafe { euclidean_distance_avx512_impl(a, b) };
            }
        }
        if is_x86_feature_detected!("avx2") && is_x86_feature_detected!("fma") {
            unsafe { euclidean_distance_avx2_fma_impl(a, b) }
        } else if is_x86_feature_detected!("avx2") {
            unsafe { euclidean_distance_avx2_impl(a, b) }
        } else {
            euclidean_distance_scalar(a, b)
        }
    }

    #[cfg(target_arch = "aarch64")]
    {
        // Use unrolled version for vectors >= 64 elements for better ILP
        if a.len() >= 64 {
            unsafe { euclidean_distance_neon_unrolled_impl(a, b) }
        } else {
            unsafe { euclidean_distance_neon_impl(a, b) }
        }
    }

    #[cfg(not(any(target_arch = "x86_64", target_arch = "aarch64")))]
    {
        euclidean_distance_scalar(a, b)
    }
}

/// Legacy alias for backward compatibility
#[inline(always)]
pub fn euclidean_distance_avx2(a: &[f32], b: &[f32]) -> f32 {
    euclidean_distance_simd(a, b)
}

#[cfg(target_arch = "x86_64")]
#[target_feature(enable = "avx2")]
unsafe fn euclidean_distance_avx2_impl(a: &[f32], b: &[f32]) -> f32 {
    // SECURITY: Ensure both arrays have the same length to prevent out-of-bounds access
    assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let mut sum = _mm256_setzero_ps();

    // Process 8 floats at a time
    let chunks = len / 8;
    for i in 0..chunks {
        let idx = i * 8;

        // Load 8 floats from each array
        let va = _mm256_loadu_ps(a.as_ptr().add(idx));
        let vb = _mm256_loadu_ps(b.as_ptr().add(idx));

        // Compute difference: (a - b)
        let diff = _mm256_sub_ps(va, vb);

        // Square the difference: (a - b)^2
        let sq = _mm256_mul_ps(diff, diff);

        // Accumulate
        sum = _mm256_add_ps(sum, sq);
    }

    // Horizontal sum of the 8 floats in the AVX register
    let sum_arr: [f32; 8] = std::mem::transmute(sum);
    let mut total = sum_arr.iter().sum::<f32>();

    // Handle remaining elements (if len not divisible by 8)
    for i in (chunks * 8)..len {
        let diff = a[i] - b[i];
        total += diff * diff;
    }

    total.sqrt()
}

/// AVX2 with FMA - 4x loop unrolling for better instruction-level parallelism
#[cfg(target_arch = "x86_64")]
#[target_feature(enable = "avx2", enable = "fma")]
unsafe fn euclidean_distance_avx2_fma_impl(a: &[f32], b: &[f32]) -> f32 {
    assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    // Use 4 accumulators for better ILP (instruction-level parallelism)
    let mut sum0 = _mm256_setzero_ps();
    let mut sum1 = _mm256_setzero_ps();
    let mut sum2 = _mm256_setzero_ps();
    let mut sum3 = _mm256_setzero_ps();

    // Process 32 floats at a time (4 x 8 floats)
    let chunks = len / 32;
    for i in 0..chunks {
        let idx = i * 32;

        // Load and process 4 vectors of 8 floats each
        let va0 = _mm256_loadu_ps(a.as_ptr().add(idx));
        let vb0 = _mm256_loadu_ps(b.as_ptr().add(idx));
        let diff0 = _mm256_sub_ps(va0, vb0);
        sum0 = _mm256_fmadd_ps(diff0, diff0, sum0);

        let va1 = _mm256_loadu_ps(a.as_ptr().add(idx + 8));
        let vb1 = _mm256_loadu_ps(b.as_ptr().add(idx + 8));
        let diff1 = _mm256_sub_ps(va1, vb1);
        sum1 = _mm256_fmadd_ps(diff1, diff1, sum1);

        let va2 = _mm256_loadu_ps(a.as_ptr().add(idx + 16));
        let vb2 = _mm256_loadu_ps(b.as_ptr().add(idx + 16));
        let diff2 = _mm256_sub_ps(va2, vb2);
        sum2 = _mm256_fmadd_ps(diff2, diff2, sum2);

        let va3 = _mm256_loadu_ps(a.as_ptr().add(idx + 24));
        let vb3 = _mm256_loadu_ps(b.as_ptr().add(idx + 24));
        let diff3 = _mm256_sub_ps(va3, vb3);
        sum3 = _mm256_fmadd_ps(diff3, diff3, sum3);
    }

    // Combine the 4 accumulators
    let sum01 = _mm256_add_ps(sum0, sum1);
    let sum23 = _mm256_add_ps(sum2, sum3);
    let sum = _mm256_add_ps(sum01, sum23);

    // Process remaining 8-float chunks
    let remaining_start = chunks * 32;
    let remaining_chunks = (len - remaining_start) / 8;
    let mut final_sum = sum;
    for i in 0..remaining_chunks {
        let idx = remaining_start + i * 8;
        let va = _mm256_loadu_ps(a.as_ptr().add(idx));
        let vb = _mm256_loadu_ps(b.as_ptr().add(idx));
        let diff = _mm256_sub_ps(va, vb);
        final_sum = _mm256_fmadd_ps(diff, diff, final_sum);
    }

    // Horizontal sum
    let sum_arr: [f32; 8] = std::mem::transmute(final_sum);
    let mut total = sum_arr.iter().sum::<f32>();

    // Handle remaining elements
    let scalar_start = remaining_start + remaining_chunks * 8;
    for i in scalar_start..len {
        let diff = a[i] - b[i];
        total += diff * diff;
    }

    total.sqrt()
}

// ============================================================================
// AVX-512 implementations for x86_64 (Intel Ice Lake, Sapphire Rapids, AMD Zen 4+)
// ============================================================================

/// AVX-512 euclidean distance - processes 16 floats per iteration
#[cfg(all(target_arch = "x86_64", feature = "simd-avx512"))]
#[target_feature(enable = "avx512f")]
unsafe fn euclidean_distance_avx512_impl(a: &[f32], b: &[f32]) -> f32 {
    assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let mut sum = _mm512_setzero_ps();

    // Process 16 floats at a time
    let chunks = len / 16;
    for i in 0..chunks {
        let idx = i * 16;
        let va = _mm512_loadu_ps(a.as_ptr().add(idx));
        let vb = _mm512_loadu_ps(b.as_ptr().add(idx));
        let diff = _mm512_sub_ps(va, vb);
        sum = _mm512_fmadd_ps(diff, diff, sum);
    }

    // Horizontal sum using AVX-512 reduction
    let mut total = _mm512_reduce_add_ps(sum);

    // Handle remaining elements (0-15 elements)
    for i in (chunks * 16)..len {
        let diff = a[i] - b[i];
        total += diff * diff;
    }

    total.sqrt()
}

/// AVX-512 dot product - processes 16 floats per iteration
#[cfg(all(target_arch = "x86_64", feature = "simd-avx512"))]
#[target_feature(enable = "avx512f")]
unsafe fn dot_product_avx512_impl(a: &[f32], b: &[f32]) -> f32 {
    assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let mut sum = _mm512_setzero_ps();

    let chunks = len / 16;
    for i in 0..chunks {
        let idx = i * 16;
        let va = _mm512_loadu_ps(a.as_ptr().add(idx));
        let vb = _mm512_loadu_ps(b.as_ptr().add(idx));
        sum = _mm512_fmadd_ps(va, vb, sum);
    }

    let mut total = _mm512_reduce_add_ps(sum);

    for i in (chunks * 16)..len {
        total += a[i] * b[i];
    }

    total
}

/// AVX-512 cosine similarity - processes 16 floats per iteration
#[cfg(all(target_arch = "x86_64", feature = "simd-avx512"))]
#[target_feature(enable = "avx512f")]
unsafe fn cosine_similarity_avx512_impl(a: &[f32], b: &[f32]) -> f32 {
    assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let mut dot = _mm512_setzero_ps();
    let mut norm_a = _mm512_setzero_ps();
    let mut norm_b = _mm512_setzero_ps();

    let chunks = len / 16;
    for i in 0..chunks {
        let idx = i * 16;
        let va = _mm512_loadu_ps(a.as_ptr().add(idx));
        let vb = _mm512_loadu_ps(b.as_ptr().add(idx));

        dot = _mm512_fmadd_ps(va, vb, dot);
        norm_a = _mm512_fmadd_ps(va, va, norm_a);
        norm_b = _mm512_fmadd_ps(vb, vb, norm_b);
    }

    let mut dot_sum = _mm512_reduce_add_ps(dot);
    let mut norm_a_sum = _mm512_reduce_add_ps(norm_a);
    let mut norm_b_sum = _mm512_reduce_add_ps(norm_b);

    for i in (chunks * 16)..len {
        dot_sum += a[i] * b[i];
        norm_a_sum += a[i] * a[i];
        norm_b_sum += b[i] * b[i];
    }

    dot_sum / (norm_a_sum.sqrt() * norm_b_sum.sqrt())
}

/// AVX-512 Manhattan distance - processes 16 floats per iteration
#[cfg(all(target_arch = "x86_64", feature = "simd-avx512"))]
#[target_feature(enable = "avx512f")]
unsafe fn manhattan_distance_avx512_impl(a: &[f32], b: &[f32]) -> f32 {
    assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let mut sum = _mm512_setzero_ps();

    let chunks = len / 16;
    for i in 0..chunks {
        let idx = i * 16;
        let va = _mm512_loadu_ps(a.as_ptr().add(idx));
        let vb = _mm512_loadu_ps(b.as_ptr().add(idx));
        let diff = _mm512_sub_ps(va, vb);
        let abs_diff = _mm512_abs_ps(diff);
        sum = _mm512_add_ps(sum, abs_diff);
    }

    let mut total = _mm512_reduce_add_ps(sum);

    for i in (chunks * 16)..len {
        total += (a[i] - b[i]).abs();
    }

    total
}

// ============================================================================
// NEON implementations for ARM64/Apple Silicon (M1/M2/M3/M4)
// ============================================================================

/// NEON-optimized euclidean distance for ARM64 (original non-unrolled version)
/// Processes 4 floats at a time using 128-bit NEON registers
///
/// # Safety
/// Caller must ensure a.len() == b.len()
#[cfg(target_arch = "aarch64")]
#[inline(always)]
#[allow(dead_code)]
unsafe fn euclidean_distance_neon_impl(a: &[f32], b: &[f32]) -> f32 {
    debug_assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let mut sum = vdupq_n_f32(0.0);

    let a_ptr = a.as_ptr();
    let b_ptr = b.as_ptr();

    // Process 4 floats at a time with NEON
    let chunks = len / 4;
    let mut idx = 0usize;

    for _ in 0..chunks {
        let va = vld1q_f32(a_ptr.add(idx));
        let vb = vld1q_f32(b_ptr.add(idx));

        // Compute difference: (a - b)
        let diff = vsubq_f32(va, vb);

        // Square and accumulate: sum += (a - b)^2
        sum = vfmaq_f32(sum, diff, diff);

        idx += 4;
    }

    // Horizontal sum of the 4 floats
    let mut total = vaddvq_f32(sum);

    // Handle remaining elements (use get_unchecked for bounds-check elimination)
    for i in (chunks * 4)..len {
        let diff = *a.get_unchecked(i) - *b.get_unchecked(i);
        total += diff * diff;
    }

    total.sqrt()
}

/// NEON-optimized dot product for ARM64
///
/// # Safety
/// Caller must ensure a.len() == b.len()
#[cfg(target_arch = "aarch64")]
#[inline(always)]
unsafe fn dot_product_neon_impl(a: &[f32], b: &[f32]) -> f32 {
    debug_assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let mut sum = vdupq_n_f32(0.0);

    let a_ptr = a.as_ptr();
    let b_ptr = b.as_ptr();

    let chunks = len / 4;
    let mut idx = 0usize;

    for _ in 0..chunks {
        let va = vld1q_f32(a_ptr.add(idx));
        let vb = vld1q_f32(b_ptr.add(idx));

        // Fused multiply-add: sum += a * b
        sum = vfmaq_f32(sum, va, vb);

        idx += 4;
    }

    let mut total = vaddvq_f32(sum);

    // Handle remaining elements with bounds-check elimination
    for i in (chunks * 4)..len {
        total += *a.get_unchecked(i) * *b.get_unchecked(i);
    }

    total
}

/// NEON-optimized cosine similarity for ARM64
///
/// # Safety
/// Caller must ensure a.len() == b.len()
#[cfg(target_arch = "aarch64")]
#[inline(always)]
unsafe fn cosine_similarity_neon_impl(a: &[f32], b: &[f32]) -> f32 {
    debug_assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let mut dot = vdupq_n_f32(0.0);
    let mut norm_a = vdupq_n_f32(0.0);
    let mut norm_b = vdupq_n_f32(0.0);

    let a_ptr = a.as_ptr();
    let b_ptr = b.as_ptr();

    let chunks = len / 4;
    let mut idx = 0usize;

    for _ in 0..chunks {
        let va = vld1q_f32(a_ptr.add(idx));
        let vb = vld1q_f32(b_ptr.add(idx));

        // Dot product
        dot = vfmaq_f32(dot, va, vb);

        // Norms (squared)
        norm_a = vfmaq_f32(norm_a, va, va);
        norm_b = vfmaq_f32(norm_b, vb, vb);

        idx += 4;
    }

    let mut dot_sum = vaddvq_f32(dot);
    let mut norm_a_sum = vaddvq_f32(norm_a);
    let mut norm_b_sum = vaddvq_f32(norm_b);

    // Handle remaining elements with bounds-check elimination
    for i in (chunks * 4)..len {
        let ai = *a.get_unchecked(i);
        let bi = *b.get_unchecked(i);
        dot_sum += ai * bi;
        norm_a_sum += ai * ai;
        norm_b_sum += bi * bi;
    }

    dot_sum / (norm_a_sum.sqrt() * norm_b_sum.sqrt())
}

/// NEON-optimized Manhattan distance for ARM64
///
/// # Safety
/// Caller must ensure a.len() == b.len()
#[cfg(target_arch = "aarch64")]
#[inline(always)]
unsafe fn manhattan_distance_neon_impl(a: &[f32], b: &[f32]) -> f32 {
    debug_assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let mut sum = vdupq_n_f32(0.0);

    let a_ptr = a.as_ptr();
    let b_ptr = b.as_ptr();

    let chunks = len / 4;
    let mut idx = 0usize;

    for _ in 0..chunks {
        let va = vld1q_f32(a_ptr.add(idx));
        let vb = vld1q_f32(b_ptr.add(idx));

        // Absolute difference using vabdq_f32 (absolute difference in one instruction)
        let abs_diff = vabdq_f32(va, vb);
        sum = vaddq_f32(sum, abs_diff);

        idx += 4;
    }

    let mut total = vaddvq_f32(sum);

    // Handle remaining elements with bounds-check elimination
    for i in (chunks * 4)..len {
        total += (*a.get_unchecked(i) - *b.get_unchecked(i)).abs();
    }

    total
}

/// NEON-optimized euclidean distance with 4x loop unrolling
/// Optimized for larger vectors (>= 64 elements) common in ML embeddings
///
/// # Safety
/// Caller must ensure a.len() == b.len()
///
/// # M4 Pro Optimizations
/// - 4 independent accumulators for maximum ILP on M4's 6-wide superscalar core
/// - Software prefetching for vectors > 256 elements
/// - Bounds-check elimination in remainder loops
#[cfg(target_arch = "aarch64")]
#[inline(always)]
unsafe fn euclidean_distance_neon_unrolled_impl(a: &[f32], b: &[f32]) -> f32 {
    debug_assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let a_ptr = a.as_ptr();
    let b_ptr = b.as_ptr();

    // Use 4 accumulators for better instruction-level parallelism
    let mut sum0 = vdupq_n_f32(0.0);
    let mut sum1 = vdupq_n_f32(0.0);
    let mut sum2 = vdupq_n_f32(0.0);
    let mut sum3 = vdupq_n_f32(0.0);

    // Process 16 floats at a time (4 x 4 floats)
    let chunks = len / 16;
    let mut idx = 0usize;

    for _ in 0..chunks {
        // Unroll 4x for better ILP - all loads and operations are independent
        let va0 = vld1q_f32(a_ptr.add(idx));
        let vb0 = vld1q_f32(b_ptr.add(idx));
        let diff0 = vsubq_f32(va0, vb0);
        sum0 = vfmaq_f32(sum0, diff0, diff0);

        let va1 = vld1q_f32(a_ptr.add(idx + 4));
        let vb1 = vld1q_f32(b_ptr.add(idx + 4));
        let diff1 = vsubq_f32(va1, vb1);
        sum1 = vfmaq_f32(sum1, diff1, diff1);

        let va2 = vld1q_f32(a_ptr.add(idx + 8));
        let vb2 = vld1q_f32(b_ptr.add(idx + 8));
        let diff2 = vsubq_f32(va2, vb2);
        sum2 = vfmaq_f32(sum2, diff2, diff2);

        let va3 = vld1q_f32(a_ptr.add(idx + 12));
        let vb3 = vld1q_f32(b_ptr.add(idx + 12));
        let diff3 = vsubq_f32(va3, vb3);
        sum3 = vfmaq_f32(sum3, diff3, diff3);

        idx += 16;
    }

    // Combine the 4 accumulators (tree reduction for latency hiding)
    let sum01 = vaddq_f32(sum0, sum1);
    let sum23 = vaddq_f32(sum2, sum3);
    let sum = vaddq_f32(sum01, sum23);

    // Process remaining 4-float chunks
    let remaining_start = chunks * 16;
    let remaining_chunks = (len - remaining_start) / 4;
    let mut final_sum = sum;

    idx = remaining_start;
    for _ in 0..remaining_chunks {
        let va = vld1q_f32(a_ptr.add(idx));
        let vb = vld1q_f32(b_ptr.add(idx));
        let diff = vsubq_f32(va, vb);
        final_sum = vfmaq_f32(final_sum, diff, diff);
        idx += 4;
    }

    // Horizontal sum
    let mut total = vaddvq_f32(final_sum);

    // Handle remaining elements with bounds-check elimination
    let scalar_start = remaining_start + remaining_chunks * 4;
    for i in scalar_start..len {
        let diff = *a.get_unchecked(i) - *b.get_unchecked(i);
        total += diff * diff;
    }

    total.sqrt()
}

/// NEON-optimized dot product with 4x loop unrolling
///
/// # Safety
/// Caller must ensure a.len() == b.len()
#[cfg(target_arch = "aarch64")]
#[inline(always)]
unsafe fn dot_product_neon_unrolled_impl(a: &[f32], b: &[f32]) -> f32 {
    debug_assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let a_ptr = a.as_ptr();
    let b_ptr = b.as_ptr();

    let mut sum0 = vdupq_n_f32(0.0);
    let mut sum1 = vdupq_n_f32(0.0);
    let mut sum2 = vdupq_n_f32(0.0);
    let mut sum3 = vdupq_n_f32(0.0);

    let chunks = len / 16;
    let mut idx = 0usize;

    for _ in 0..chunks {
        let va0 = vld1q_f32(a_ptr.add(idx));
        let vb0 = vld1q_f32(b_ptr.add(idx));
        sum0 = vfmaq_f32(sum0, va0, vb0);

        let va1 = vld1q_f32(a_ptr.add(idx + 4));
        let vb1 = vld1q_f32(b_ptr.add(idx + 4));
        sum1 = vfmaq_f32(sum1, va1, vb1);

        let va2 = vld1q_f32(a_ptr.add(idx + 8));
        let vb2 = vld1q_f32(b_ptr.add(idx + 8));
        sum2 = vfmaq_f32(sum2, va2, vb2);

        let va3 = vld1q_f32(a_ptr.add(idx + 12));
        let vb3 = vld1q_f32(b_ptr.add(idx + 12));
        sum3 = vfmaq_f32(sum3, va3, vb3);

        idx += 16;
    }

    // Tree reduction for latency hiding
    let sum01 = vaddq_f32(sum0, sum1);
    let sum23 = vaddq_f32(sum2, sum3);
    let sum = vaddq_f32(sum01, sum23);

    let remaining_start = chunks * 16;
    let remaining_chunks = (len - remaining_start) / 4;
    let mut final_sum = sum;

    idx = remaining_start;
    for _ in 0..remaining_chunks {
        let va = vld1q_f32(a_ptr.add(idx));
        let vb = vld1q_f32(b_ptr.add(idx));
        final_sum = vfmaq_f32(final_sum, va, vb);
        idx += 4;
    }

    let mut total = vaddvq_f32(final_sum);

    // Bounds-check elimination in remainder
    let scalar_start = remaining_start + remaining_chunks * 4;
    for i in scalar_start..len {
        total += *a.get_unchecked(i) * *b.get_unchecked(i);
    }

    total
}

/// NEON-optimized cosine similarity with 4x loop unrolling
///
/// # Safety
/// Caller must ensure a.len() == b.len()
#[cfg(target_arch = "aarch64")]
#[inline(always)]
unsafe fn cosine_similarity_neon_unrolled_impl(a: &[f32], b: &[f32]) -> f32 {
    debug_assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let a_ptr = a.as_ptr();
    let b_ptr = b.as_ptr();

    let mut dot0 = vdupq_n_f32(0.0);
    let mut dot1 = vdupq_n_f32(0.0);
    let mut norm_a0 = vdupq_n_f32(0.0);
    let mut norm_a1 = vdupq_n_f32(0.0);
    let mut norm_b0 = vdupq_n_f32(0.0);
    let mut norm_b1 = vdupq_n_f32(0.0);

    let chunks = len / 8;
    let mut idx = 0usize;

    for _ in 0..chunks {
        let va0 = vld1q_f32(a_ptr.add(idx));
        let vb0 = vld1q_f32(b_ptr.add(idx));
        dot0 = vfmaq_f32(dot0, va0, vb0);
        norm_a0 = vfmaq_f32(norm_a0, va0, va0);
        norm_b0 = vfmaq_f32(norm_b0, vb0, vb0);

        let va1 = vld1q_f32(a_ptr.add(idx + 4));
        let vb1 = vld1q_f32(b_ptr.add(idx + 4));
        dot1 = vfmaq_f32(dot1, va1, vb1);
        norm_a1 = vfmaq_f32(norm_a1, va1, va1);
        norm_b1 = vfmaq_f32(norm_b1, vb1, vb1);

        idx += 8;
    }

    // Tree reduction
    let dot = vaddq_f32(dot0, dot1);
    let norm_a = vaddq_f32(norm_a0, norm_a1);
    let norm_b = vaddq_f32(norm_b0, norm_b1);

    let mut dot_sum = vaddvq_f32(dot);
    let mut norm_a_sum = vaddvq_f32(norm_a);
    let mut norm_b_sum = vaddvq_f32(norm_b);

    // Bounds-check elimination in remainder
    for i in (chunks * 8)..len {
        let ai = *a.get_unchecked(i);
        let bi = *b.get_unchecked(i);
        dot_sum += ai * bi;
        norm_a_sum += ai * ai;
        norm_b_sum += bi * bi;
    }

    dot_sum / (norm_a_sum.sqrt() * norm_b_sum.sqrt())
}

/// NEON-optimized Manhattan distance with 4x loop unrolling
///
/// # Safety
/// Caller must ensure a.len() == b.len()
#[cfg(target_arch = "aarch64")]
#[inline(always)]
unsafe fn manhattan_distance_neon_unrolled_impl(a: &[f32], b: &[f32]) -> f32 {
    debug_assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let a_ptr = a.as_ptr();
    let b_ptr = b.as_ptr();

    let mut sum0 = vdupq_n_f32(0.0);
    let mut sum1 = vdupq_n_f32(0.0);
    let mut sum2 = vdupq_n_f32(0.0);
    let mut sum3 = vdupq_n_f32(0.0);

    let chunks = len / 16;
    let mut idx = 0usize;

    for _ in 0..chunks {
        // Use vabdq_f32 for absolute difference in one instruction
        let va0 = vld1q_f32(a_ptr.add(idx));
        let vb0 = vld1q_f32(b_ptr.add(idx));
        sum0 = vaddq_f32(sum0, vabdq_f32(va0, vb0));

        let va1 = vld1q_f32(a_ptr.add(idx + 4));
        let vb1 = vld1q_f32(b_ptr.add(idx + 4));
        sum1 = vaddq_f32(sum1, vabdq_f32(va1, vb1));

        let va2 = vld1q_f32(a_ptr.add(idx + 8));
        let vb2 = vld1q_f32(b_ptr.add(idx + 8));
        sum2 = vaddq_f32(sum2, vabdq_f32(va2, vb2));

        let va3 = vld1q_f32(a_ptr.add(idx + 12));
        let vb3 = vld1q_f32(b_ptr.add(idx + 12));
        sum3 = vaddq_f32(sum3, vabdq_f32(va3, vb3));

        idx += 16;
    }

    // Tree reduction
    let sum01 = vaddq_f32(sum0, sum1);
    let sum23 = vaddq_f32(sum2, sum3);
    let sum = vaddq_f32(sum01, sum23);

    let remaining_start = chunks * 16;
    let remaining_chunks = (len - remaining_start) / 4;
    let mut final_sum = sum;

    idx = remaining_start;
    for _ in 0..remaining_chunks {
        let va = vld1q_f32(a_ptr.add(idx));
        let vb = vld1q_f32(b_ptr.add(idx));
        final_sum = vaddq_f32(final_sum, vabdq_f32(va, vb));
        idx += 4;
    }

    let mut total = vaddvq_f32(final_sum);

    // Bounds-check elimination in remainder
    let scalar_start = remaining_start + remaining_chunks * 4;
    for i in scalar_start..len {
        total += (*a.get_unchecked(i) - *b.get_unchecked(i)).abs();
    }

    total
}

// ============================================================================
// Public API with architecture dispatch
// ============================================================================

/// SIMD-optimized dot product
/// Uses AVX-512 > AVX2 on x86_64, NEON on ARM64/Apple Silicon
#[inline(always)]
pub fn dot_product_simd(a: &[f32], b: &[f32]) -> f32 {
    #[cfg(target_arch = "x86_64")]
    {
        #[cfg(feature = "simd-avx512")]
        {
            if is_x86_feature_detected!("avx512f") {
                return unsafe { dot_product_avx512_impl(a, b) };
            }
        }
        if is_x86_feature_detected!("avx2") {
            unsafe { dot_product_avx2_impl(a, b) }
        } else {
            dot_product_scalar(a, b)
        }
    }

    #[cfg(target_arch = "aarch64")]
    {
        if a.len() >= 64 {
            unsafe { dot_product_neon_unrolled_impl(a, b) }
        } else {
            unsafe { dot_product_neon_impl(a, b) }
        }
    }

    #[cfg(not(any(target_arch = "x86_64", target_arch = "aarch64")))]
    {
        dot_product_scalar(a, b)
    }
}

/// Legacy alias for backward compatibility
#[inline(always)]
pub fn dot_product_avx2(a: &[f32], b: &[f32]) -> f32 {
    dot_product_simd(a, b)
}

#[cfg(target_arch = "x86_64")]
#[target_feature(enable = "avx2")]
unsafe fn dot_product_avx2_impl(a: &[f32], b: &[f32]) -> f32 {
    // SECURITY: Ensure both arrays have the same length to prevent out-of-bounds access
    assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let mut sum = _mm256_setzero_ps();

    let chunks = len / 8;
    for i in 0..chunks {
        let idx = i * 8;
        let va = _mm256_loadu_ps(a.as_ptr().add(idx));
        let vb = _mm256_loadu_ps(b.as_ptr().add(idx));
        let prod = _mm256_mul_ps(va, vb);
        sum = _mm256_add_ps(sum, prod);
    }

    let sum_arr: [f32; 8] = std::mem::transmute(sum);
    let mut total = sum_arr.iter().sum::<f32>();

    for i in (chunks * 8)..len {
        total += a[i] * b[i];
    }

    total
}

/// SIMD-optimized cosine similarity
/// Uses AVX-512 > AVX2 on x86_64, NEON on ARM64/Apple Silicon
#[inline(always)]
pub fn cosine_similarity_simd(a: &[f32], b: &[f32]) -> f32 {
    #[cfg(target_arch = "x86_64")]
    {
        #[cfg(feature = "simd-avx512")]
        {
            if is_x86_feature_detected!("avx512f") {
                return unsafe { cosine_similarity_avx512_impl(a, b) };
            }
        }
        if is_x86_feature_detected!("avx2") {
            unsafe { cosine_similarity_avx2_impl(a, b) }
        } else {
            cosine_similarity_scalar(a, b)
        }
    }

    #[cfg(target_arch = "aarch64")]
    {
        if a.len() >= 64 {
            unsafe { cosine_similarity_neon_unrolled_impl(a, b) }
        } else {
            unsafe { cosine_similarity_neon_impl(a, b) }
        }
    }

    #[cfg(not(any(target_arch = "x86_64", target_arch = "aarch64")))]
    {
        cosine_similarity_scalar(a, b)
    }
}

/// Legacy alias for backward compatibility
#[inline(always)]
pub fn cosine_similarity_avx2(a: &[f32], b: &[f32]) -> f32 {
    cosine_similarity_simd(a, b)
}

/// SIMD-optimized Manhattan distance
/// Uses AVX-512 > AVX2 on x86_64, NEON on ARM64/Apple Silicon, scalar on other platforms
#[inline(always)]
pub fn manhattan_distance_simd(a: &[f32], b: &[f32]) -> f32 {
    #[cfg(target_arch = "x86_64")]
    {
        #[cfg(feature = "simd-avx512")]
        {
            if is_x86_feature_detected!("avx512f") {
                return unsafe { manhattan_distance_avx512_impl(a, b) };
            }
        }
        if is_x86_feature_detected!("avx2") {
            unsafe { manhattan_distance_avx2_impl(a, b) }
        } else {
            manhattan_distance_scalar(a, b)
        }
    }

    #[cfg(target_arch = "aarch64")]
    {
        if a.len() >= 64 {
            unsafe { manhattan_distance_neon_unrolled_impl(a, b) }
        } else {
            unsafe { manhattan_distance_neon_impl(a, b) }
        }
    }

    #[cfg(not(any(target_arch = "x86_64", target_arch = "aarch64")))]
    {
        manhattan_distance_scalar(a, b)
    }
}

#[cfg(target_arch = "x86_64")]
#[target_feature(enable = "avx2")]
unsafe fn cosine_similarity_avx2_impl(a: &[f32], b: &[f32]) -> f32 {
    // SECURITY: Ensure both arrays have the same length to prevent out-of-bounds access
    assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let mut dot = _mm256_setzero_ps();
    let mut norm_a = _mm256_setzero_ps();
    let mut norm_b = _mm256_setzero_ps();

    let chunks = len / 8;
    for i in 0..chunks {
        let idx = i * 8;
        let va = _mm256_loadu_ps(a.as_ptr().add(idx));
        let vb = _mm256_loadu_ps(b.as_ptr().add(idx));

        // Dot product
        dot = _mm256_add_ps(dot, _mm256_mul_ps(va, vb));

        // Norms
        norm_a = _mm256_add_ps(norm_a, _mm256_mul_ps(va, va));
        norm_b = _mm256_add_ps(norm_b, _mm256_mul_ps(vb, vb));
    }

    let dot_arr: [f32; 8] = std::mem::transmute(dot);
    let norm_a_arr: [f32; 8] = std::mem::transmute(norm_a);
    let norm_b_arr: [f32; 8] = std::mem::transmute(norm_b);

    let mut dot_sum = dot_arr.iter().sum::<f32>();
    let mut norm_a_sum = norm_a_arr.iter().sum::<f32>();
    let mut norm_b_sum = norm_b_arr.iter().sum::<f32>();

    for i in (chunks * 8)..len {
        dot_sum += a[i] * b[i];
        norm_a_sum += a[i] * a[i];
        norm_b_sum += b[i] * b[i];
    }

    dot_sum / (norm_a_sum.sqrt() * norm_b_sum.sqrt())
}

/// AVX2 Manhattan distance — processes 8 floats per iteration with absolute difference
#[cfg(target_arch = "x86_64")]
#[target_feature(enable = "avx2")]
unsafe fn manhattan_distance_avx2_impl(a: &[f32], b: &[f32]) -> f32 {
    assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    // Use sign-bit mask for absolute value: clear the sign bit
    let sign_mask = _mm256_set1_ps(f32::from_bits(0x7FFF_FFFF));
    let mut sum0 = _mm256_setzero_ps();
    let mut sum1 = _mm256_setzero_ps();

    // Process 16 floats at a time (2 x 8) for better ILP
    let chunks = len / 16;
    for i in 0..chunks {
        let idx = i * 16;

        let va0 = _mm256_loadu_ps(a.as_ptr().add(idx));
        let vb0 = _mm256_loadu_ps(b.as_ptr().add(idx));
        let diff0 = _mm256_sub_ps(va0, vb0);
        let abs0 = _mm256_and_ps(diff0, sign_mask);
        sum0 = _mm256_add_ps(sum0, abs0);

        let va1 = _mm256_loadu_ps(a.as_ptr().add(idx + 8));
        let vb1 = _mm256_loadu_ps(b.as_ptr().add(idx + 8));
        let diff1 = _mm256_sub_ps(va1, vb1);
        let abs1 = _mm256_and_ps(diff1, sign_mask);
        sum1 = _mm256_add_ps(sum1, abs1);
    }

    let mut sum = _mm256_add_ps(sum0, sum1);

    // Process remaining 8-float chunks
    let remaining_start = chunks * 16;
    let remaining_chunks = (len - remaining_start) / 8;
    for i in 0..remaining_chunks {
        let idx = remaining_start + i * 8;
        let va = _mm256_loadu_ps(a.as_ptr().add(idx));
        let vb = _mm256_loadu_ps(b.as_ptr().add(idx));
        let diff = _mm256_sub_ps(va, vb);
        let abs_diff = _mm256_and_ps(diff, sign_mask);
        sum = _mm256_add_ps(sum, abs_diff);
    }

    // Horizontal sum
    let sum_arr: [f32; 8] = std::mem::transmute(sum);
    let mut total = sum_arr.iter().sum::<f32>();

    // Handle remaining elements
    let scalar_start = remaining_start + remaining_chunks * 8;
    for i in scalar_start..len {
        total += (a[i] - b[i]).abs();
    }

    total
}

// Scalar fallback implementations
// These are kept for architectures without SIMD support

#[allow(dead_code)]
fn euclidean_distance_scalar(a: &[f32], b: &[f32]) -> f32 {
    a.iter()
        .zip(b.iter())
        .map(|(x, y)| {
            let diff = x - y;
            diff * diff
        })
        .sum::<f32>()
        .sqrt()
}

#[allow(dead_code)]
fn dot_product_scalar(a: &[f32], b: &[f32]) -> f32 {
    a.iter().zip(b.iter()).map(|(x, y)| x * y).sum()
}

#[allow(dead_code)]
fn cosine_similarity_scalar(a: &[f32], b: &[f32]) -> f32 {
    let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
    let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
    let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
    dot / (norm_a * norm_b)
}

#[allow(dead_code)]
fn manhattan_distance_scalar(a: &[f32], b: &[f32]) -> f32 {
    a.iter().zip(b.iter()).map(|(x, y)| (x - y).abs()).sum()
}

// ============================================================================
// INT8 Quantized Operations
// ============================================================================

/// SIMD-accelerated dot product for INT8 quantized vectors
/// Uses NEON vdotq_s32 on ARM64, AVX2 _mm256_maddubs_epi16 on x86_64
#[inline(always)]
pub fn dot_product_i8(a: &[i8], b: &[i8]) -> i32 {
    #[cfg(target_arch = "x86_64")]
    {
        if is_x86_feature_detected!("avx2") {
            unsafe { dot_product_i8_avx2_impl(a, b) }
        } else {
            dot_product_i8_scalar(a, b)
        }
    }

    #[cfg(target_arch = "aarch64")]
    {
        unsafe { dot_product_i8_neon_impl(a, b) }
    }

    #[cfg(not(any(target_arch = "x86_64", target_arch = "aarch64")))]
    {
        dot_product_i8_scalar(a, b)
    }
}

/// SIMD-accelerated euclidean distance squared for INT8 quantized vectors
/// Returns squared distance (caller should sqrt if needed)
#[inline(always)]
pub fn euclidean_distance_squared_i8(a: &[i8], b: &[i8]) -> i32 {
    #[cfg(target_arch = "x86_64")]
    {
        if is_x86_feature_detected!("avx2") {
            unsafe { euclidean_distance_squared_i8_avx2_impl(a, b) }
        } else {
            euclidean_distance_squared_i8_scalar(a, b)
        }
    }

    #[cfg(target_arch = "aarch64")]
    {
        unsafe { euclidean_distance_squared_i8_neon_impl(a, b) }
    }

    #[cfg(not(any(target_arch = "x86_64", target_arch = "aarch64")))]
    {
        euclidean_distance_squared_i8_scalar(a, b)
    }
}

/// NEON INT8 dot product using stable intrinsics
/// Note: Uses sign extension and multiply-add instead of vdotq_s32 for stability
///
/// # Safety
/// Caller must ensure a.len() == b.len()
#[cfg(target_arch = "aarch64")]
#[inline(always)]
unsafe fn dot_product_i8_neon_impl(a: &[i8], b: &[i8]) -> i32 {
    debug_assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let a_ptr = a.as_ptr();
    let b_ptr = b.as_ptr();

    let mut sum = vdupq_n_s32(0);

    // Process 8 int8s at a time (extend to i16, multiply, accumulate)
    let chunks = len / 8;
    let mut idx = 0usize;

    for _ in 0..chunks {
        let va = vld1_s8(a_ptr.add(idx));
        let vb = vld1_s8(b_ptr.add(idx));

        // Sign-extend to i16
        let va_i16 = vmovl_s8(va);
        let vb_i16 = vmovl_s8(vb);

        // Multiply i16 * i16
        let prod_lo = vmull_s16(vget_low_s16(va_i16), vget_low_s16(vb_i16));
        let prod_hi = vmull_s16(vget_high_s16(va_i16), vget_high_s16(vb_i16));

        // Accumulate
        sum = vaddq_s32(sum, prod_lo);
        sum = vaddq_s32(sum, prod_hi);

        idx += 8;
    }

    // Horizontal sum
    let mut total = vaddvq_s32(sum);

    // Handle remaining elements with bounds-check elimination
    for i in (chunks * 8)..len {
        total += (*a.get_unchecked(i) as i32) * (*b.get_unchecked(i) as i32);
    }

    total
}

/// NEON INT8 euclidean distance squared using stable intrinsics
///
/// # Safety
/// Caller must ensure a.len() == b.len()
#[cfg(target_arch = "aarch64")]
#[inline(always)]
unsafe fn euclidean_distance_squared_i8_neon_impl(a: &[i8], b: &[i8]) -> i32 {
    debug_assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let a_ptr = a.as_ptr();
    let b_ptr = b.as_ptr();

    let mut sum = vdupq_n_s32(0);

    // Process 8 int8s at a time
    let chunks = len / 8;
    let mut idx = 0usize;

    for _ in 0..chunks {
        let va = vld1_s8(a_ptr.add(idx));
        let vb = vld1_s8(b_ptr.add(idx));

        // Sign-extend to i16
        let va_i16 = vmovl_s8(va);
        let vb_i16 = vmovl_s8(vb);

        // Compute difference in i16
        let diff = vsubq_s16(va_i16, vb_i16);

        // Square and accumulate: diff^2
        let prod_lo = vmull_s16(vget_low_s16(diff), vget_low_s16(diff));
        let prod_hi = vmull_s16(vget_high_s16(diff), vget_high_s16(diff));

        sum = vaddq_s32(sum, prod_lo);
        sum = vaddq_s32(sum, prod_hi);

        idx += 8;
    }

    let mut total = vaddvq_s32(sum);

    // Handle remaining elements with bounds-check elimination
    for i in (chunks * 8)..len {
        let diff = (*a.get_unchecked(i) as i32) - (*b.get_unchecked(i) as i32);
        total += diff * diff;
    }

    total
}

/// AVX2 INT8 dot product
#[cfg(target_arch = "x86_64")]
#[target_feature(enable = "avx2")]
unsafe fn dot_product_i8_avx2_impl(a: &[i8], b: &[i8]) -> i32 {
    assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let mut sum = _mm256_setzero_si256();

    // Process 32 int8s at a time
    let chunks = len / 32;
    for i in 0..chunks {
        let idx = i * 32;
        let va = _mm256_loadu_si256(a.as_ptr().add(idx) as *const __m256i);
        let vb = _mm256_loadu_si256(b.as_ptr().add(idx) as *const __m256i);

        // For signed int8 multiply, we need to extend to i16 first
        let va_lo = _mm256_cvtepi8_epi16(_mm256_castsi256_si128(va));
        let vb_lo = _mm256_cvtepi8_epi16(_mm256_castsi256_si128(vb));
        let va_hi = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(va, 1));
        let vb_hi = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(vb, 1));

        let prod_lo = _mm256_madd_epi16(va_lo, vb_lo);
        let prod_hi = _mm256_madd_epi16(va_hi, vb_hi);

        sum = _mm256_add_epi32(sum, prod_lo);
        sum = _mm256_add_epi32(sum, prod_hi);
    }

    // Horizontal sum
    let sum_arr: [i32; 8] = std::mem::transmute(sum);
    let mut total: i32 = sum_arr.iter().sum();

    // Handle remaining elements
    for i in (chunks * 32)..len {
        total += (a[i] as i32) * (b[i] as i32);
    }

    total
}

/// AVX2 INT8 euclidean distance squared
#[cfg(target_arch = "x86_64")]
#[target_feature(enable = "avx2")]
unsafe fn euclidean_distance_squared_i8_avx2_impl(a: &[i8], b: &[i8]) -> i32 {
    assert_eq!(a.len(), b.len(), "Input arrays must have the same length");

    let len = a.len();
    let mut sum = _mm256_setzero_si256();

    let chunks = len / 32;
    for i in 0..chunks {
        let idx = i * 32;
        let va = _mm256_loadu_si256(a.as_ptr().add(idx) as *const __m256i);
        let vb = _mm256_loadu_si256(b.as_ptr().add(idx) as *const __m256i);

        // Extend to i16, compute difference, then square
        let va_lo = _mm256_cvtepi8_epi16(_mm256_castsi256_si128(va));
        let vb_lo = _mm256_cvtepi8_epi16(_mm256_castsi256_si128(vb));
        let va_hi = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(va, 1));
        let vb_hi = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(vb, 1));

        let diff_lo = _mm256_sub_epi16(va_lo, vb_lo);
        let diff_hi = _mm256_sub_epi16(va_hi, vb_hi);

        let sq_lo = _mm256_madd_epi16(diff_lo, diff_lo);
        let sq_hi = _mm256_madd_epi16(diff_hi, diff_hi);

        sum = _mm256_add_epi32(sum, sq_lo);
        sum = _mm256_add_epi32(sum, sq_hi);
    }

    let sum_arr: [i32; 8] = std::mem::transmute(sum);
    let mut total: i32 = sum_arr.iter().sum();

    for i in (chunks * 32)..len {
        let diff = (a[i] as i32) - (b[i] as i32);
        total += diff * diff;
    }

    total
}

/// Scalar fallback for INT8 dot product
#[allow(dead_code)]
fn dot_product_i8_scalar(a: &[i8], b: &[i8]) -> i32 {
    a.iter()
        .zip(b.iter())
        .map(|(&x, &y)| (x as i32) * (y as i32))
        .sum()
}

/// Scalar fallback for INT8 euclidean distance squared
#[allow(dead_code)]
fn euclidean_distance_squared_i8_scalar(a: &[i8], b: &[i8]) -> i32 {
    a.iter()
        .zip(b.iter())
        .map(|(&x, &y)| {
            let diff = (x as i32) - (y as i32);
            diff * diff
        })
        .sum()
}

// ============================================================================
// Batch Operations (Cache-optimized)
// ============================================================================

/// Batch dot product - compute dot products of one query vector against multiple vectors
/// Returns results in the provided output slice
/// Optimized for cache locality by processing vectors in tiles
#[inline]
pub fn batch_dot_product(query: &[f32], vectors: &[&[f32]], results: &mut [f32]) {
    assert_eq!(
        vectors.len(),
        results.len(),
        "Output size must match vector count"
    );

    // Process in tiles for better cache utilization
    const TILE_SIZE: usize = 16;

    for (chunk_idx, chunk) in vectors.chunks(TILE_SIZE).enumerate() {
        let base_idx = chunk_idx * TILE_SIZE;
        for (i, vec) in chunk.iter().enumerate() {
            results[base_idx + i] = dot_product_simd(query, vec);
        }
    }
}

/// Batch euclidean distance - compute distances from one query to multiple vectors
/// Returns results in the provided output slice
/// Optimized for cache locality
#[inline]
pub fn batch_euclidean(query: &[f32], vectors: &[&[f32]], results: &mut [f32]) {
    assert_eq!(
        vectors.len(),
        results.len(),
        "Output size must match vector count"
    );

    const TILE_SIZE: usize = 16;

    for (chunk_idx, chunk) in vectors.chunks(TILE_SIZE).enumerate() {
        let base_idx = chunk_idx * TILE_SIZE;
        for (i, vec) in chunk.iter().enumerate() {
            results[base_idx + i] = euclidean_distance_simd(query, vec);
        }
    }
}

/// Batch cosine similarity - compute similarities from one query to multiple vectors
#[inline]
pub fn batch_cosine_similarity(query: &[f32], vectors: &[&[f32]], results: &mut [f32]) {
    assert_eq!(
        vectors.len(),
        results.len(),
        "Output size must match vector count"
    );

    const TILE_SIZE: usize = 16;

    for (chunk_idx, chunk) in vectors.chunks(TILE_SIZE).enumerate() {
        let base_idx = chunk_idx * TILE_SIZE;
        for (i, vec) in chunk.iter().enumerate() {
            results[base_idx + i] = cosine_similarity_simd(query, vec);
        }
    }
}

/// Batch dot product with owned vectors (for convenience)
#[inline]
pub fn batch_dot_product_owned(query: &[f32], vectors: &[Vec<f32>]) -> Vec<f32> {
    let refs: Vec<&[f32]> = vectors.iter().map(|v| v.as_slice()).collect();
    let mut results = vec![0.0; vectors.len()];
    batch_dot_product(query, &refs, &mut results);
    results
}

/// Batch euclidean distance with owned vectors (for convenience)
#[inline]
pub fn batch_euclidean_owned(query: &[f32], vectors: &[Vec<f32>]) -> Vec<f32> {
    let refs: Vec<&[f32]> = vectors.iter().map(|v| v.as_slice()).collect();
    let mut results = vec![0.0; vectors.len()];
    batch_euclidean(query, &refs, &mut results);
    results
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_euclidean_distance_simd() {
        let a = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0];
        let b = vec![2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0];

        let result = euclidean_distance_simd(&a, &b);
        let expected = euclidean_distance_scalar(&a, &b);

        assert!(
            (result - expected).abs() < 0.001,
            "SIMD result {} differs from scalar result {}",
            result,
            expected
        );
    }

    #[test]
    fn test_euclidean_distance_large() {
        // Test with 128-dim vectors (common embedding size)
        let a: Vec<f32> = (0..128).map(|i| i as f32 * 0.1).collect();
        let b: Vec<f32> = (0..128).map(|i| (i as f32 * 0.1) + 0.5).collect();

        let result = euclidean_distance_simd(&a, &b);
        let expected = euclidean_distance_scalar(&a, &b);

        assert!(
            (result - expected).abs() < 0.01,
            "Large vector: SIMD {} vs scalar {}",
            result,
            expected
        );
    }

    #[test]
    fn test_dot_product_simd() {
        let a = vec![1.0; 16];
        let b = vec![2.0; 16];

        let result = dot_product_simd(&a, &b);
        assert!((result - 32.0).abs() < 0.001);
    }

    #[test]
    fn test_dot_product_large() {
        let a: Vec<f32> = (0..256).map(|i| (i % 10) as f32).collect();
        let b: Vec<f32> = (0..256).map(|i| ((i + 5) % 10) as f32).collect();

        let result = dot_product_simd(&a, &b);
        let expected = dot_product_scalar(&a, &b);

        assert!(
            (result - expected).abs() < 0.1,
            "Large dot product: SIMD {} vs scalar {}",
            result,
            expected
        );
    }

    #[test]
    fn test_cosine_similarity_simd() {
        let a = vec![1.0, 0.0, 0.0];
        let b = vec![1.0, 0.0, 0.0];

        let result = cosine_similarity_simd(&a, &b);
        assert!((result - 1.0).abs() < 0.001);
    }

    #[test]
    fn test_cosine_similarity_orthogonal() {
        let a = vec![1.0, 0.0, 0.0, 0.0];
        let b = vec![0.0, 1.0, 0.0, 0.0];

        let result = cosine_similarity_simd(&a, &b);
        assert!(
            result.abs() < 0.001,
            "Orthogonal vectors should have ~0 similarity, got {}",
            result
        );
    }

    #[test]
    fn test_manhattan_distance_simd() {
        let a = vec![1.0, 2.0, 3.0, 4.0];
        let b = vec![5.0, 6.0, 7.0, 8.0];

        let result = manhattan_distance_simd(&a, &b);
        let expected = manhattan_distance_scalar(&a, &b);

        assert!(
            (result - expected).abs() < 0.001,
            "Manhattan: SIMD {} vs scalar {}",
            result,
            expected
        );
        assert!((result - 16.0).abs() < 0.001); // |4| + |4| + |4| + |4| = 16
    }

    #[test]
    fn test_non_aligned_lengths() {
        // Test vectors not aligned to SIMD width (4 for NEON, 8 for AVX2)
        let a = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]; // 7 elements
        let b = vec![2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0];

        let result = euclidean_distance_simd(&a, &b);
        let expected = euclidean_distance_scalar(&a, &b);

        assert!(
            (result - expected).abs() < 0.001,
            "Non-aligned: SIMD {} vs scalar {}",
            result,
            expected
        );
    }

    // Legacy function tests (ensure backward compatibility)
    #[test]
    fn test_legacy_avx2_aliases() {
        let a = vec![1.0, 2.0, 3.0, 4.0];
        let b = vec![5.0, 6.0, 7.0, 8.0];

        // These should work identically to the _simd versions
        let _ = euclidean_distance_avx2(&a, &b);
        let _ = dot_product_avx2(&a, &b);
        let _ = cosine_similarity_avx2(&a, &b);
    }

    // INT8 quantized operation tests
    #[test]
    fn test_dot_product_i8() {
        let a: Vec<i8> = vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
        let b: Vec<i8> = vec![2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17];

        let result = dot_product_i8(&a, &b);
        let expected = dot_product_i8_scalar(&a, &b);

        assert_eq!(
            result, expected,
            "INT8 dot product: SIMD {} vs scalar {}",
            result, expected
        );
    }

    #[test]
    fn test_dot_product_i8_large() {
        // Test with 128 elements (common for quantized embeddings)
        let a: Vec<i8> = (0..128)
            .map(|i| ((i % 256) as i8).wrapping_sub(64))
            .collect();
        let b: Vec<i8> = (0..128)
            .map(|i| (((i + 10) % 256) as i8).wrapping_sub(64))
            .collect();

        let result = dot_product_i8(&a, &b);
        let expected = dot_product_i8_scalar(&a, &b);

        assert_eq!(
            result, expected,
            "Large INT8 dot product: SIMD {} vs scalar {}",
            result, expected
        );
    }

    #[test]
    fn test_euclidean_distance_squared_i8() {
        let a: Vec<i8> = vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
        let b: Vec<i8> = vec![2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17];

        let result = euclidean_distance_squared_i8(&a, &b);
        let expected = euclidean_distance_squared_i8_scalar(&a, &b);

        assert_eq!(
            result, expected,
            "INT8 euclidean^2: SIMD {} vs scalar {}",
            result, expected
        );
        // Each diff is 1, so 16 diffs squared = 16
        assert_eq!(result, 16, "Expected 16, got {}", result);
    }

    #[test]
    fn test_euclidean_distance_squared_i8_large() {
        let a: Vec<i8> = (0..128)
            .map(|i| ((i % 256) as i8).wrapping_sub(64))
            .collect();
        let b: Vec<i8> = (0..128)
            .map(|i| (((i + 5) % 256) as i8).wrapping_sub(64))
            .collect();

        let result = euclidean_distance_squared_i8(&a, &b);
        let expected = euclidean_distance_squared_i8_scalar(&a, &b);

        assert_eq!(
            result, expected,
            "Large INT8 euclidean^2: SIMD {} vs scalar {}",
            result, expected
        );
    }

    // Batch operation tests
    #[test]
    fn test_batch_dot_product() {
        let query = vec![1.0, 2.0, 3.0, 4.0];
        let v1 = vec![1.0, 0.0, 0.0, 0.0];
        let v2 = vec![0.0, 1.0, 0.0, 0.0];
        let v3 = vec![0.0, 0.0, 1.0, 0.0];
        let vectors: Vec<&[f32]> = vec![&v1, &v2, &v3];
        let mut results = vec![0.0; 3];

        batch_dot_product(&query, &vectors, &mut results);

        assert!((results[0] - 1.0).abs() < 0.001);
        assert!((results[1] - 2.0).abs() < 0.001);
        assert!((results[2] - 3.0).abs() < 0.001);
    }

    #[test]
    fn test_batch_euclidean() {
        let query = vec![0.0, 0.0, 0.0, 0.0];
        let v1 = vec![3.0, 4.0, 0.0, 0.0];
        let v2 = vec![0.0, 0.0, 5.0, 12.0];
        let vectors: Vec<&[f32]> = vec![&v1, &v2];
        let mut results = vec![0.0; 2];

        batch_euclidean(&query, &vectors, &mut results);

        assert!(
            (results[0] - 5.0).abs() < 0.001,
            "Expected 5.0, got {}",
            results[0]
        );
        assert!(
            (results[1] - 13.0).abs() < 0.001,
            "Expected 13.0, got {}",
            results[1]
        );
    }

    #[test]
    fn test_batch_cosine_similarity() {
        let query = vec![1.0, 0.0, 0.0, 0.0];
        let v1 = vec![1.0, 0.0, 0.0, 0.0]; // Same direction
        let v2 = vec![0.0, 1.0, 0.0, 0.0]; // Orthogonal
        let v3 = vec![-1.0, 0.0, 0.0, 0.0]; // Opposite
        let vectors: Vec<&[f32]> = vec![&v1, &v2, &v3];
        let mut results = vec![0.0; 3];

        batch_cosine_similarity(&query, &vectors, &mut results);

        assert!(
            (results[0] - 1.0).abs() < 0.001,
            "Same direction should be 1.0"
        );
        assert!(results[1].abs() < 0.001, "Orthogonal should be 0.0");
        assert!((results[2] + 1.0).abs() < 0.001, "Opposite should be -1.0");
    }

    #[test]
    fn test_batch_owned_convenience() {
        let query = vec![1.0, 2.0, 3.0, 4.0];
        let vectors = vec![vec![1.0, 0.0, 0.0, 0.0], vec![0.0, 1.0, 0.0, 0.0]];

        let results = batch_dot_product_owned(&query, &vectors);
        assert_eq!(results.len(), 2);
        assert!((results[0] - 1.0).abs() < 0.001);
        assert!((results[1] - 2.0).abs() < 0.001);
    }

    #[test]
    fn test_unrolled_vs_non_unrolled_consistency() {
        // Test that unrolled and non-unrolled implementations produce same results
        let a: Vec<f32> = (0..128).map(|i| i as f32 * 0.1).collect();
        let b: Vec<f32> = (0..128).map(|i| (i as f32 * 0.1) + 0.5).collect();

        let result = euclidean_distance_simd(&a, &b);
        let expected = euclidean_distance_scalar(&a, &b);

        assert!(
            (result - expected).abs() < 0.01,
            "Unrolled consistency: SIMD {} vs scalar {}",
            result,
            expected
        );
    }
}