Lib.rs

In One Sentence

You want to use safe SIMD datatypes from wide but realized there is no simple, safe and fast way to align your f32x4 (and friends) in memory and treat them as regular f32 slices for easy loading and manipulation; simd_aligned to the rescue.

Highlights

• built on top of wide for easy data handling
• supports everything from u8x16 to f64x4
• think in flat slices (&[f32]), but get performance of properly aligned SIMD vectors (&[f32x4])
• provides N-dimensional VecSimd and NxM-dimensional MatSimd.

Examples

Produces a vector that can hold 10 elements of type f64. All elements are guaranteed to be properly aligned for fast access.

use simd_aligned::*;

// Create vectors of `10` f64 elements with value `0.0`.
let mut v1 = VecSimd::<f64x4>::with(0.0, 10);
let mut v2 = VecSimd::<f64x4>::with(0.0, 10);

// Get "flat", mutable view of the vector, and set individual elements:
let v1_m = v1.flat_mut();
let v2_m = v2.flat_mut();

// Set some elements on v1
v1_m[0] = 0.0;
v1_m[4] = 4.0;
v1_m[8] = 8.0;

// Set some others on v2
v2_m[1] = 0.0;
v2_m[5] = 5.0;
v2_m[9] = 9.0;

let mut sum = f64x4::splat(0.0);

// Eventually, do something with the actual SIMD types. Does
// `std::simd` vector math, e.g., f64x8 + f64x8 in one operation:
sum = v1[0] + v2[0];

Benchmarks

There is no performance penalty for using simd_aligned, while retaining all the simplicity of handling flat arrays.

test vectors::packed       ... bench:          77 ns/iter (+/- 4)
test vectors::scalar       ... bench:       1,177 ns/iter (+/- 464)
test vectors::simd_aligned ... bench:          71 ns/iter (+/- 5)

Status

• December 2024: Compiles on stable.
• March 2023: Compiles again on latest Rust nightly.
• August 2018: Initial version.

FAQ

How does it relate to faster and std::simd?

• simd_aligned builds on top of std::simd. At aims to provide common, SIMD-aligned data structure that support simple and safe scalar access patterns.

• faster (as of today) is good if you already have exiting flat slices in your code and want to operate them "full SIMD ahead". However, in particular when dealing with multiple slices at the same time (e.g., kernel computations) the performance impact of unaligned arrays can become a bit more noticeable (e.g., in the case of ffsvm up to 10% - 20%).