guff-matrix

This crate uses SIMD code to achieve very fast Galois Field matrix multiplication.

The idea behind the matrix multiplication is to organise memory accesses so that the SIMD engine can achieve 100% utilisation while avoiding any non-aligned or partial vector reads. Output elements are produced sequentially along the output matrix diagonal.

In order to achieve this, the input and output matrices are constructed with a specific number of columns so that:

• the last vector read of the input matrix ends exactly at the matrix boundary

• when wrap-around at the output matrix occurs the algorithm starts filling a new diagonal

Essentially, the transform, input and output matrices are treated as infinite tapes. We select input/output matrix width so that it has a factor which is relatively prime to both of the transform matrix dimensions k and n (to satisfy the second condition). We also constrain the width of the input matrix to being a multiple of lcm(k,n,w,simd_width) to satisfy the first condition. (w is the word size, which for GF(2⁸) fields is 1)

The matrices are organised in memory as follows:

• transform is row-major
• input is column-major
• output is either

In order to facilitate wrap-around at the end of the transform matrix, if k * n is not a multiple of the SIMD width, we

The input matrix also has an extra simd_width bytes copied from the start of the matrix. This is to deal with wrap-around for all non-final reads. (?)

Previous Implementation

I have previously implemented a version of this algorithm on a PlayStation 3. It is available here

SIMD Support

I will implement three different SIMD engines for field multiplication across vectors:

• [x] x86 implementation of parallel long (bitwise) multiplication

• [x] Arm/Aarch64 NEON implementation using hardware polynomial multiply and table-based modular reduction (vmull/tvbl)

• [ ] Arm NEON implementation of parallel long (bitwise) multiplication

I also have a 4-way armv6 (Thumb) implementation of the long multiplication routine, which I may add for completeness. Its performance is roughly comparable to doing four single multiply using lookup tables, only slightly worse.

Support for Arm targets requires nightly Rust build.

Infinite Tape (Simulation)

Before I start writing arch-specific implementations, I'm focusing on clearly documenting how the algorithm works. I'm going to implement a non-SIMD version that uses the same basic ideas, but using a more rusty style (infinite iterators). That's in src/arch.rs and can be enabled as a feature:

cargo test --features simulation

I'll also use this to prove that the algorithm works as intended.

• [x] Write and test simulation of (non-vectorised) algorithm