Cool stuff! I'll give it a lookover later.

A few years ago, I wrote AESRAND (https://github.com/dragontamer/AESRand). I managed to get some well-known programmers to look into it, and their advice helped me write some pretty neat SIMD-tricks. EX: I SIMD-implemented a 32-bit integer -> floating point [0.0, 1.0] operator, to convert the bitstream into floats. As well as integer-based nearly bias-free division / modulus free conversion into [0, WhateverInt] (such as D20 rolls). For 16-bit, 32-bit, and 64-bit integers (with less bias the more bits you supplied).

Unfortunately, I ran out of time and some work-related stuff came up. So I never really finished the experiments.

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My current home project is bump-allocator + semi-space garbage collection in SIMD for GPUs. As far as I can tell, both bump-allocation and semi-space garbage collection are easily SIMDified in an obvious manner. And since cudamalloc is fully synchronous, I wanted a more scalable, parallel solution to the GPU memory allocation problem.

SIMD-compute is a totally different model of compute than what most programmers are familiar with.

That's the biggest problem. If you write optimal SIMD-code, no one else in your team will understand it. Since we have so much compute these days (to the point where O(n^2) scanf parsers are all over the place), its become increasingly obvious that few modern programmers care about performance at all.

Nonetheless, the more and more I study SIMD-compute, the more I realize that these expert programmers have figured out a ton of good and fast solutions to a wide-variety of problems... decades ago and then somehow forgotten until recently.

Seriously: that Data Parallel Algorithms paper is just WTF to me. Linked list traversal (scan-reduced sum from a linked list in SIMD-parallel), Regular Expressions and more.

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Then I look at the GPU-graphics guys, and they're doing like BVH tree traversal in parallel so that their raytracers work.

Its like "Yeah, Raytracing is clearly a parallel operation cause GPUs can do it". So I look it up and wtf? Its not easy. Someone really thought things through. Its non-obvious how they managed to get a recursive / sequential operation to operate in highly parallel SIMD operations while avoiding branch divergence issues.

Really: think about it: Raytracing is effectively:

    If(ray hit object) recursively bounce ray.

How the hell did they make that parallel? A combination of stream-compaction and very intelligent data-structures, as well as a set of new SIMD-assembly instructions to cover some obscure cases.

There's some really intelligent stuff going on in the SIMD-compute world, that clearly applies beyond just the machine-learning crowd.