A friend shared Sylvain Kerkour's post SIMD programming in pure Rust which covers AVX-512 on AMD Zen 5. That got me curious about ARM's side of the story—specifically how std::simd compares to hand-written NEON intrinsics. My main development machine is a MacBook Pro with Apple M4, so I ran the benchmarks there.
I ran 9 benchmarks comparing three approaches: scalar, std::simd, and NEON. All numbers below are averaged from 3 runs.
Key finding: std::simd ranged from 9x faster to 7.7x slower than scalar code. NEON always delivered speedups (1.2x–4.3x). The difference comes down to data layout—interleaved data like RGB images and stereo audio exposed limitations in the portable SIMD abstraction.
Test Environment
CPU: Apple M4 (MacBook Pro 2024)
Rust: rustc 1.94.0-nightly (2026-01-14)
OS: macOS
Command:cargo run --release
Three implementations per scenario:
| Approach | Stability | Portability |
|---|---|---|
| Scalar | stable | everywhere |
| std::simd | nightly | cross-platform |
| std::arch (NEON) | stable | ARM64 only |
Results Summary
| Scenario | Scalar | std::simd | NEON | std::simd | NEON |
|---|---|---|---|---|---|
| RGB→Grayscale | 6.16ms | 18.51ms | 1.45ms | 0.33x | 4.26x |
| Volume Adjust | 1.39ms | 3.42ms | 0.82ms | 0.40x | 1.70x |
| Audio Mixing | 0.63ms | 4.84ms | 0.53ms | 0.13x | 1.20x |
| Count Newlines | 14.10ms | 3.22ms | 3.87ms | 4.38x | 3.65x |
| Find Byte | 24.07ms | 2.61ms | 2.68ms | 9.23x | 9.00x |
| Dot Product | 51.20ms | 10.99ms | 20.14ms | 4.66x | 2.54x |
| Matrix-Vec Mul | 4.48ms | 0.69ms | 1.35ms | 6.53x | 3.32x |
| Range Check | 24.93ms | 8.24ms | 9.33ms | 3.02x | 2.67x |
| Sorted Check | 25.14ms | 37.18ms | 6.87ms | 0.68x | 3.66x |
Three scenarios showed std::simd slower than scalar: RGB conversion (0.33x), audio mixing (0.13x), and sorted check (0.68x).
Scenario 1: RGB to Grayscale
Task: Convert 1920×1080 RGB image to grayscale using ITU-R BT.601 formula.
I used fixed-point arithmetic to avoid floating-point operations—much faster on integer pipelines: Gray = (77*R + 150*G + 29*B) >> 8
Scalar
The scalar version is straightforward with chunks_exact(3):
pub fn rgb_to_grayscale(rgb: &[u8], gray: &mut [u8]) {
for (i, chunk) in rgb.chunks_exact(3).enumerate() {
let r = chunk[0] as u32;
let g = chunk[1] as u32;
let b = chunk[2] as u32;
gray[i] = ((77 * r + 150 * g + 29 * b) >> 8) as u8;
}
}
std::simd
pub fn rgb_to_grayscale(rgb: &[u8], gray: &mut [u8]) {
let chunks = rgb.chunks_exact(48);
let weight_r = u16x16::splat(77);
let weight_g = u16x16::splat(150);
let weight_b = u16x16::splat(29);
let mut out_idx = 0;
for chunk in chunks {
// No deinterleave instruction available
// Must use scalar loop: 48 memory accesses per iteration
let mut r_vals = [0u8; 16];
let mut g_vals = [0u8; 16];
let mut b_vals = [0u8; 16];
for i in 0..16 {
r_vals[i] = chunk[i * 3];
g_vals[i] = chunk[i * 3 + 1];
b_vals[i] = chunk[i * 3 + 2];
}
let r = u16x16::from_array(r_vals.map(|x| x as u16));
let g = u16x16::from_array(g_vals.map(|x| x as u16));
let b = u16x16::from_array(b_vals.map(|x| x as u16));
let gray_u16 = (r * weight_r + g * weight_g + b * weight_b) >> Simd::splat(8);
let gray_u8: [u8; 16] = gray_u16.to_array().map(|x| x as u8);
gray[out_idx..out_idx + 16].copy_from_slice(&gray_u8);
out_idx += 16;
}
}
NEON
pub fn rgb_to_grayscale(rgb: &[u8], gray: &mut [u8]) {
unsafe {
let weight_r = vdupq_n_u8(77);
let weight_g = vdupq_n_u8(150);
let weight_b = vdupq_n_u8(29);
for i in (0..simd_len).step_by(16) {
// vld3q_u8: load + deinterleave in one instruction
// [R0,G0,B0,R1,G1,B1,...] → R[], G[], B[]
let rgb_data = vld3q_u8(rgb.as_ptr().add(i * 3));
let r = rgb_data.0;
let g = rgb_data.1;
let b = rgb_data.2;
// Widening multiply: u8 × u8 → u16
let r_lo = vmull_u8(vget_low_u8(r), vget_low_u8(weight_r));
let r_hi = vmull_high_u8(r, weight_r);
let g_lo = vmull_u8(vget_low_u8(g), vget_low_u8(weight_g));
let g_hi = vmull_high_u8(g, weight_g);
let b_lo = vmull_u8(vget_low_u8(b), vget_low_u8(weight_b));
let b_hi = vmull_high_u8(b, weight_b);
let sum_lo = vaddq_u16(vaddq_u16(r_lo, g_lo), b_lo);
let sum_hi = vaddq_u16(vaddq_u16(r_hi, g_hi), b_hi);
// Shift right + narrow: u16 → u8
let gray_lo = vshrn_n_u16(sum_lo, 8);
let gray_hi = vshrn_n_u16(sum_hi, 8);
let gray_vec = vcombine_u8(gray_lo, gray_hi);
vst1q_u8(gray.as_mut_ptr().add(i), gray_vec);
}
}
}
Results
| Implementation | Time | vs Scalar |
|---|---|---|
| Scalar | 6.16ms | 1.0x |
| std::simd | 18.51ms | 0.33x |
| NEON | 1.45ms | 4.26x |
Analysis: vld3q_u8 performs load and 3-way deinterleave in one instruction. std::simd lacks this capability, requiring a 16-iteration scalar loop that dominates execution time.
Scenario 2: Audio Volume Adjustment
Task: Multiply 880K audio samples (i16) by gain factor 0.8.
I used fixed-point here too—multiplying by 256 and shifting right 8 bits keeps everything in integer domain.
Scalar
pub fn adjust_volume(samples: &mut [i16], volume: f32) {
for sample in samples.iter_mut() {
let adjusted = (*sample as f32 * volume) as i32;
*sample = adjusted.clamp(-32768, 32767) as i16;
}
}
std::simd
pub fn adjust_volume(samples: &mut [i16], volume: f32) {
let vol_fixed = (volume * 256.0) as i32;
let vol_vec = i32x8::splat(vol_fixed);
for i in (0..simd_len).step_by(8) {
// No i16 → i32 widening instruction
let mut vals = [0i32; 8];
for j in 0..8 {
vals[j] = samples[i + j] as i32;
}
let v = i32x8::from_array(vals);
let adjusted = (v * vol_vec) >> Simd::splat(8);
let clamped = adjusted.simd_clamp(i32x8::splat(-32768), i32x8::splat(32767));
// No i32 → i16 narrowing instruction
for (j, &val) in clamped.to_array().iter().enumerate() {
samples[i + j] = val as i16;
}
}
}
NEON
pub fn adjust_volume(samples: &mut [i16], volume: f32) {
let vol_fixed = (volume * 256.0) as i32;
unsafe {
let vol_vec = vdupq_n_s32(vol_fixed);
for i in (0..simd_len).step_by(8) {
let v = vld1q_s16(samples.as_ptr().add(i));
// vmovl: widen i16 → i32
let v_lo = vmovl_s16(vget_low_s16(v));
let v_hi = vmovl_high_s16(v);
let mul_lo = vmulq_s32(v_lo, vol_vec);
let mul_hi = vmulq_s32(v_hi, vol_vec);
let shifted_lo = vshrq_n_s32(mul_lo, 8);
let shifted_hi = vshrq_n_s32(mul_hi, 8);
// vqmovn: saturating narrow i32 → i16
let result_lo = vqmovn_s32(shifted_lo);
let result_hi = vqmovn_s32(shifted_hi);
let result = vcombine_s16(result_lo, result_hi);
vst1q_s16(samples.as_mut_ptr().add(i), result);
}
}
}
Results
| Implementation | Time | vs Scalar |
|---|---|---|
| Scalar | 1.39ms | 1.0x |
| std::simd | 3.42ms | 0.40x |
| NEON | 0.82ms | 1.70x |
Analysis: std::simd lacks type conversion instructions. vmovl (widen) and vqmovn (saturating narrow) are single instructions in NEON but require scalar loops in std::simd.
Scenario 3: Audio Mixing
Task: Mix two audio tracks using (a + b) / 2 to prevent overflow.
Division by 2 is the classic way to mix audio without clipping. Simple but requires widening to i32 first.
Scalar
pub fn mix_tracks(track_a: &[i16], track_b: &[i16], output: &mut [i16]) {
for ((a, b), out) in track_a.iter().zip(track_b.iter()).zip(output.iter_mut()) {
*out = ((*a as i32 + *b as i32) / 2) as i16;
}
}
std::simd
pub fn mix_tracks(track_a: &[i16], track_b: &[i16], output: &mut [i16]) {
for i in (0..simd_len).step_by(8) {
let mut a_vals = [0i32; 8];
let mut b_vals = [0i32; 8];
for j in 0..8 {
a_vals[j] = track_a[i + j] as i32;
b_vals[j] = track_b[i + j] as i32;
}
let a = i32x8::from_array(a_vals);
let b = i32x8::from_array(b_vals);
let mixed = (a + b) >> Simd::splat(1);
for (j, &val) in mixed.to_array().iter().enumerate() {
output[i + j] = val as i16;
}
}
}
NEON
pub fn mix_tracks(track_a: &[i16], track_b: &[i16], output: &mut [i16]) {
unsafe {
for i in (0..simd_len).step_by(8) {
let a = vld1q_s16(track_a.as_ptr().add(i));
let b = vld1q_s16(track_b.as_ptr().add(i));
// vhaddq: halving add, computes (a + b) / 2 without overflow
let mixed = vhaddq_s16(a, b);
vst1q_s16(output.as_mut_ptr().add(i), mixed);
}
}
}
Results
| Implementation | Time | vs Scalar |
|---|---|---|
| Scalar | 0.63ms | 1.0x |
| std::simd | 4.84ms | 0.13x |
| NEON | 0.53ms | 1.20x |
Analysis: vhaddq_s16 performs halving add in one instruction—designed specifically for audio mixing. std::simd requires widening to i32, adding, shifting, and narrowing back, plus three scalar loops for type conversions.
Scenario 4: Counting Newlines
Task: Count occurrences of \n in 10MB text.
This is where SIMD really shines—contiguous data with simple comparison. I used filter().count() for scalar, which Rust's iterator makes clean.
Scalar
pub fn count_byte(data: &[u8], target: u8) -> usize {
data.iter().filter(|&&b| b == target).count()
}
std::simd
pub fn count_byte(data: &[u8], target: u8) -> usize {
let target_vec = u8x32::splat(target);
let chunks = data.chunks_exact(32);
let mut count = 0usize;
for chunk in chunks {
let v = u8x32::from_slice(chunk);
let mask = v.simd_eq(target_vec);
count += mask.to_bitmask().count_ones() as usize;
}
count += chunks.remainder().iter().filter(|&&b| b == target).count();
count
}
NEON
pub fn count_byte(data: &[u8], target: u8) -> usize {
let mut count = 0usize;
unsafe {
let target_vec = vdupq_n_u8(target);
for i in (0..simd_len).step_by(16) {
let v = vld1q_u8(data.as_ptr().add(i));
let eq = vceqq_u8(v, target_vec);
let ones = vshrq_n_u8(eq, 7);
count += vaddvq_u8(ones) as usize;
}
}
count
}
Results
| Implementation | Time | vs Scalar |
|---|---|---|
| Scalar | 14.10ms | 1.0x |
| std::simd | 3.22ms | 4.38x |
| NEON | 3.87ms | 3.65x |
Analysis: Contiguous data, simple comparison, no type conversions. Both SIMD approaches perform well. std::simd uses 256-bit vectors, NEON uses 128-bit, explaining the slight std::simd advantage.
Scenario 5: Finding a Byte
Task: Find position of @ in 10MB data (target near end).
I placed the target byte near the end to simulate worst-case search. The position() iterator is nice for scalar.
Scalar
pub fn find_byte(data: &[u8], target: u8) -> Option<usize> {
data.iter().position(|&b| b == target)
}
std::simd
pub fn find_byte(data: &[u8], target: u8) -> Option<usize> {
let target_vec = u8x32::splat(target);
let chunks = data.chunks_exact(32);
for (chunk_idx, chunk) in chunks.enumerate() {
let v = u8x32::from_slice(chunk);
let mask = v.simd_eq(target_vec);
let bitmask = mask.to_bitmask();
if bitmask != 0 {
return Some(chunk_idx * 32 + bitmask.trailing_zeros() as usize);
}
}
None
}
NEON
pub fn find_byte(data: &[u8], target: u8) -> Option<usize> {
let len = data.len();
let simd_len = len - (len % 16);
unsafe {
let target_vec = vdupq_n_u8(target);
for i in (0..simd_len).step_by(16) {
let v = vld1q_u8(data.as_ptr().add(i));
let eq = vceqq_u8(v, target_vec);
// vmaxvq_u8: if any 0xFF exists, returns 0xFF
if vmaxvq_u8(eq) != 0 {
// Found match, linear search for exact position
for j in 0..16 {
if data[i + j] == target {
return Some(i + j);
}
}
}
}
}
None
}
Results
| Implementation | Time | vs Scalar |
|---|---|---|
| Scalar | 24.07ms | 1.0x |
| std::simd | 2.61ms | 9.23x |
| NEON | 2.68ms | 9.00x |
Analysis: Best-case SIMD scenario. Compare 32 bytes per iteration, early exit on match.
Scenario 6: Dot Product
Task: Dot product of two 10M-element f32 vectors.
Classic numerical computing workload. Rust's iterator chain makes the scalar version readable.
Scalar
pub fn dot_product(a: &[f32], b: &[f32]) -> f32 {
a.iter().zip(b.iter()).map(|(x, y)| x * y).sum()
}
std::simd
pub fn dot_product(a: &[f32], b: &[f32]) -> f32 {
let chunks_a = a.chunks_exact(8);
let chunks_b = b.chunks_exact(8);
let mut acc = f32x8::splat(0.0);
for (a_chunk, b_chunk) in chunks_a.zip(chunks_b) {
let va = f32x8::from_slice(a_chunk);
let vb = f32x8::from_slice(b_chunk);
acc += va * vb;
}
acc.reduce_sum()
}
NEON
pub fn dot_product(a: &[f32], b: &[f32]) -> f32 {
unsafe {
let mut acc = vdupq_n_f32(0.0);
for i in (0..simd_len).step_by(4) {
let va = vld1q_f32(a.as_ptr().add(i));
let vb = vld1q_f32(b.as_ptr().add(i));
acc = vfmaq_f32(acc, va, vb);
}
vaddvq_f32(acc)
}
}
Results
| Implementation | Time | vs Scalar |
|---|---|---|
| Scalar | 51.20ms | 1.0x |
| std::simd | 10.99ms | 4.66x |
| NEON | 20.14ms | 2.54x |
Analysis: std::simd outperforms hand-written NEON. std::simd uses f32x8 (256-bit), while the NEON implementation uses f32x4 (128-bit). The compiler generates efficient code from the portable abstraction.
Scenario 7: Matrix-Vector Multiplication
Task: 1024×1024 matrix times 1024-element vector.
I reused the dot product implementation row by row—keeps the code simple.
Scalar
pub fn matrix_vector_mul(matrix: &[f32], vector: &[f32], result: &mut [f32], rows: usize, cols: usize) {
for i in 0..rows {
let row_start = i * cols;
result[i] = matrix[row_start..row_start + cols]
.iter()
.zip(vector.iter())
.map(|(m, v)| m * v)
.sum();
}
}
std::simd
pub fn matrix_vector_mul(matrix: &[f32], vector: &[f32], result: &mut [f32], rows: usize, cols: usize) {
for i in 0..rows {
let row = &matrix[i * cols..(i + 1) * cols];
result[i] = dot_product(row, vector); // reuse SIMD dot product
}
}
NEON
pub fn matrix_vector_mul(matrix: &[f32], vector: &[f32], result: &mut [f32], rows: usize, cols: usize) {
for i in 0..rows {
let row = &matrix[i * cols..(i + 1) * cols];
result[i] = dot_product(row, vector); // reuse NEON dot product
}
}
Results
| Implementation | Time | vs Scalar |
|---|---|---|
| Scalar | 4.48ms | 1.0x |
| std::simd | 0.69ms | 6.53x |
| NEON | 1.35ms | 3.32x |
Analysis: Regular memory access pattern, same characteristics as dot product.
Scenario 8: Range Check
Task: Verify all 10M i32 values are in [0, 100).
Early exit on failure makes this fast when data is invalid. I kept all values in range to measure worst-case (full scan).
Scalar
pub fn all_in_range(data: &[i32], min: i32, max: i32) -> bool {
data.iter().all(|&x| x >= min && x <= max)
}
std::simd
pub fn all_in_range(data: &[i32], min: i32, max: i32) -> bool {
let min_vec = i32x8::splat(min);
let max_vec = i32x8::splat(max);
for chunk in data.chunks_exact(8) {
let v = i32x8::from_slice(chunk);
let ge_min = v.simd_ge(min_vec);
let le_max = v.simd_le(max_vec);
if !(ge_min & le_max).all() {
return false;
}
}
true
}
NEON
pub fn all_in_range(data: &[i32], min: i32, max: i32) -> bool {
let len = data.len();
let simd_len = len - (len % 4);
unsafe {
let min_vec = vdupq_n_s32(min);
let max_vec = vdupq_n_s32(max);
for i in (0..simd_len).step_by(4) {
let v = vld1q_s32(data.as_ptr().add(i));
let ge_min = vcgeq_s32(v, min_vec);
let le_max = vcleq_s32(v, max_vec);
let both = vandq_u32(ge_min, le_max);
// vminvq: if any 0 exists, returns 0
if vminvq_u32(both) == 0 {
return false;
}
}
}
true
}
Results
| Implementation | Time | vs Scalar |
|---|---|---|
| Scalar | 24.93ms | 1.0x |
| std::simd | 8.24ms | 3.02x |
| NEON | 9.33ms | 2.67x |
Analysis: Simple comparisons on contiguous i32 data. No type conversions needed.
Scenario 9: Sorted Check
Task: Verify 10M i32 values are sorted ascending.
I used pre-sorted data to measure full-scan performance. The windows() iterator is convenient but has overhead.
Scalar
pub fn is_sorted(data: &[i32]) -> bool {
data.windows(2).all(|w| w[0] <= w[1])
}
std::simd
pub fn is_sorted(data: &[i32]) -> bool {
for window in data.windows(9) {
let current = i32x8::from_slice(&window[0..8]);
let next = i32x8::from_slice(&window[1..9]);
if !current.simd_le(next).all() {
return false;
}
}
true
}
NEON
pub fn is_sorted(data: &[i32]) -> bool {
unsafe {
let mut i = 0;
while i + 4 < data.len() {
let current = vld1q_s32(data.as_ptr().add(i));
let next = vld1q_s32(data.as_ptr().add(i + 1));
let le = vcleq_s32(current, next);
if vminvq_u32(le) == 0 {
return false;
}
i += 4;
}
}
true
}
Results
| Implementation | Time | vs Scalar |
|---|---|---|
| Scalar | 25.14ms | 1.0x |
| std::simd | 37.18ms | 0.68x |
| NEON | 6.87ms | 3.66x |
Analysis: windows(9) iterator creates overlapping slices with overhead. NEON uses direct pointer arithmetic.
Root Cause Analysis
After digging into the assembly, the pattern became clear. std::simd exposes only operations available across all target platforms. ARM-specific instructions cannot be represented:
| Operation | std::simd | NEON |
|---|---|---|
| Deinterleave load | scalar loop | vld3q_u8 |
| Widen i16→i32 | scalar loop | vmovl_s16 |
| Saturating narrow i32→i16 | manual clamp | vqmovn_s32 |
| Halving add | add + shift + narrow | vhaddq_s16 |
When these operations are needed, std::simd falls back to scalar loops, negating SIMD benefits and adding overhead.
NEON Instruction Reference
Naming pattern:
vld3q_u8
│││││└─ u8: data type
││││└── q: 128-bit register
│││└─── 3: 3-way deinterleave
││└──── ld: load
│└───── v: vector
| Instruction | Operation |
|---|---|
vld1q_u8 | Load 16 bytes |
vld3q_u8 | Load + deinterleave RGB |
vmull_u8 | Widening multiply u8→u16 |
vmovl_s16 | Widen i16→i32 |
vqmovn_s32 | Saturating narrow i32→i16 |
vfmaq_f32 | Fused multiply-add |
vhaddq_s16 | Halving add |
vaddvq_f32 | Horizontal sum |
Recommendations
| Data Pattern | Approach |
|---|---|
| Contiguous f32/i32 arrays | std::simd |
| Interleaved data (RGB, stereo audio) | Platform intrinsics |
| Operations requiring type conversion | Platform intrinsics |
| Cross-platform library | std::simd + scalar fallback |
| Maximum ARM performance | NEON intrinsics |
Conclusion
My takeaway: std::simd is great for numerical workloads on contiguous data—often better than hand-written intrinsics because the compiler knows optimization tricks I don't.
But for image and audio processing, std::simd falls short. The portable abstraction cannot express instructions like vld3q_u8 or vhaddq_s16 that ARM provides specifically for these workloads.
If we're targeting ARM and working with interleaved data, NEON intrinsics remain the way to go.
References
Rust std::simd documentation - Portable SIMD module (nightly)
Rust std::arch::aarch64 - ARM64 intrinsics in Rust
ARM NEON Intrinsics Reference - Official ARM intrinsics search
ARM NEON Programmer's Guide - NEON architecture overview
Source
Full source code: github.com/Erio-Harrison/simd_benchmark
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