Vector API benefits for small matrices/vectors?

[Johannes Lichtenberger]

The question seems to be also interesting in regards to in-memory database
systems for query engines and storage engines (for instance the index
structure Adaptive Radix Trie or its sibling the Height Optimized Trie
HOT). Of course it's only part of the story as the data should be aligned
in memory next to each other...).

Kind regards
Johannes

Mark Raynsford <org.openjdk at io7m.com> schrieb am Fr., 17. Dez. 2021, 12:58:

> Hello!
>
> I'm curious if there are likely to be performance benefits for small
> matrices and vectors when using the vector API. I'm intending to try
> out the second incubator soon, but I'm unclear on whether the
> underlying hardware APIs are really targeted at this kind of work.
>
> I maintain a small vector algebra package intended for realtime computer
> graphics:
>
>   https://github.com/io7m/jtensors
>
> It's focused on immutable 2-4 element vectors and 3x3 and 4x4 element
> matrices, in single and double precision floating point. The code is
> mostly generated from a template so that I don't have to write the same
> code twice for float/double. A 4x4 matrix multiplication looks like
> this:
>
>   https://gist.github.com/io7m/f5453332c6ef268c78db1f81c63f0066
>
> On an AMD Ryzen 5 3600, this code is in the ballpark of ~34000000 ops/s,
> but I believe with the various hardware extensions, I could probably go
> faster than this. However, most of the examples I see for working with
> this hardware are focused on multiplying very large matrices with
> hundreds of elements. I feel like maybe in the case of 4x4 matrices,
> any hardware speedup would be negated by the amount of packing and
> unpacking required to get from what amounts to a record class
> containing 16 double fields, to the various vector API types, and back
> again. Am I wrong about this?
>
> I will likely be trying this regardless, but I'm wondering if anyone
> has an input/experience before I do.
>
> --
> Mark Raynsford | https://www.io7m.com
>