#simd #aligned #encode #text #decode #binary #bin #encoded

bintext

Encode and decodes binary encoded text into aligned binary blobs using SIMD

3 releases

0.1.3 Aug 23, 2020
0.1.2 Aug 23, 2020
0.1.1 Aug 23, 2020
0.1.0 Aug 23, 2020

MIT license

33KB
596 lines

Bin Text

Encode and decodes binary encoded text into aligned binary blobs using SIMD

https://github.com/lassade/bintext/blob/main/.github/workflows/rust.yml

Binary text encoding and decoding with support for SIMD (AVX2 and SSSE3) with good fallback performance.

The main idea of this crate is to have a zero copy binary deserialization for text formats.

How it works

Alignment can't be guaranteed in a text format, so no matter what the data will need to be re-aligned while decoding, if the required alignment is N maximum amount of offset need to move the bytes is less than N thus by providing an start padding in the binary encoded text of N - 1 it's possible to align the data up to N.

Quick note this crate will only accept padding equal or grater than N, because it's a bit cheap to do this way.

// Padding of 8 (suppose it was read form a file)
let hex = "--------a1f7d5e8d14f0f76".to_string();

unsafe {
    // Decode with padding of 8 and alignment of 8
    let slice = bintext::hex::decode_aligned(&mut hex, 8, 8).unwrap();
    // Data is aligned so you can safely do this:
    let slice: &[u64] = std::slice::from_raw_parts(
        slice.as_ptr() as *const _,
        slice.len() / std::mem::size_of::<u64>()
    );
}

TODO

  • NEON instruction set
  • Base64

Other similar crates

There is a lot of other crates that already does the job, but every single one have some kind of performance downside:

  • hex bad performance, but smaller code size since doesn't rely on LUTs;
  • base16, no SIMD, but have faster decoding and overall good encoding;
  • faster-hex fastest encode using SSSE3 and AVX2, decode only has an AVX2. SSE has the best SIMD coverage of all instructions sets, so it should be a must.

This crate provides implementations that covers all their competitors performance weaknesses. Just run cargo bench.

All these crates doesn't seem to provide functions to decode aligned data, so after decoding you will need and extra alignment step.

No runtime deps