brrrrrm

18 ms·

211.
by brrrrrm 3y ago
Some cool examples, like an animated attention head layer: https://bhosmer.github.io/mm/index.html?0=out+%3D+%28attn+%3...
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Mm – 3D Matmul Visualizer(bhosmer.github.io)
2 points by brrrrrm 3y ago|1 comments
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by brrrrrm 3y ago
AMD supports the 256 wide subset of the AVX-512 ISA?
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by brrrrrm 3y ago
I doubt it was obvious scaling up would magically work. I suspect the experiments were limited for analytic simplicity rather than computational.
215.
AWS Announces AWS Payment Cryptography(aws.amazon.com)
2 points by brrrrrm 3y ago|0 comments
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by brrrrrm 3y ago
I think you're more likely to train a model capable of writing Doom using this machine.
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by brrrrrm 3y ago
layer of indirection introduces scatter/gather and other dynamic loads, which is tricky to optimize
218.
by brrrrrm 3y ago
I wonder if the parent comment was made tongue-in-cheek :)
219.
by brrrrrm 3y ago
Once fused into super-instructions/adaptive instructions, does `disas` still allow users to manipulate the bytecode as if it were not optimized? That'd be a very nice feature and I'm not sure it exists in other languages.
220.
by brrrrrm 3y ago
That’s very unlikely, tokenization is really simple and usually quite fast (scales with input size). Unexpected slowness with a larger context window might point to a non-existent or unoptimized KV cache.
221.
by brrrrrm 3y ago
nothing really, that's effectively what's achieved by comptime. it's funny to see it written as "basic" dependent typing, considering it's an extremely complex type system to implement.
222.
by brrrrrm 3y ago
I've been working on mix of ML performance and abstraction for a while and this is a breath of fresh air. Zig is truly a fascinating language, and the semantics revealed in this post are surprisingly powerful. "By hard coding the
223.
by brrrrrm 3y ago
In terms of production pipeline, sure. But since there’s an established model architecture, the hardware doesn’t need to be super generic. Google had to build TPUs in a world where everything was still rapidly changing - deciding on things
224.
by brrrrrm 4y ago
> WebGPU ML runtime oh cool! will this be numpy-like or will it have autograd as well? We're looking around for a web backend for shumai[1] and the former is really all we need :) [1]: https://github.com/facebookres
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by brrrrrm 4y ago
ML is still in a compute hungry growth phase. Every exciting new achievement in accuracy requires more compute than before. It makes sense that tinyML, which has less compute, would always have lagging modeling support and be a bit less in
226.
by brrrrrm 4y ago
The WebGPU demo mentioned in this post is insane. Blows any WASM approach out of the water. Unfortunately that performance is not supported anywhere but chrome canary (behind a flag)
227.
by brrrrrm 4y ago
> JavaScript isn't used for any machine learning tasks as far as I'm aware https://github.com/facebookresearch/shumai
228.
by brrrrrm 4y ago
My claim is pretty subjective, but the idea is that there aren't many distinct kernels used in machine learning. It's all tensor contractions and element-wise operations. I'd argue that this can be maintained by hand with
229.
by brrrrrm 4y ago
> Hand-written assembly kernels don’t scale! I used to think this. And I think, in theory, it is true. But the fact of the matter is, modern ML just doesn't use that many kernels. Every framework uses the same libraries (BLAS) an
230.
by brrrrrm 4y ago
> be tied to aspects of the company's ... Coinbase Earn "Commodity linked securities are investment instruments or securities that are linked to one or more commodity prices. Unlike commodities, which provide no income to the o
231.
by brrrrrm 4y ago
I don't think a high level representation is necessary for relatively straightforward FMA extensions (either outer products in the case of Apple AMX or matrix products in the case of CUDA/Intel AMX). WebGPU + tensor core support
232.
by brrrrrm 4y ago
It’s pretty obvious that cloning ML APIs (which are a collection of well defined independent math operations) is a better long term solution than shoehorning an entire language into the browser.
233.
by brrrrrm 4y ago
Hmm, slower as the number scales up. Wonder why my M1 didn’t do that
234.
by brrrrrm 4y ago
JavaScript is 10-37x faster out of the box without any imports async function time_tasks(count=100) { async function nop_task() { return performance.now(); } const start = performance.now() let tasks =
235.
Twitter In the Browser Is Down(jott.live)
9 points by brrrrrm 4y ago|3 comments
236.
by brrrrrm 4y ago
Puzzles + learning openings. I highly recommend lichess for this
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by brrrrrm 4y ago
what’s nice is that pyodide folks have gotten sympy compiled to web assembly, so it’s a viable browser library too!
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by brrrrrm 4y ago
Huge! Congrats team and Colin in particular :)
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Ask HN: How important is a zero-install experience to you?
1 points by brrrrrm 4y ago|2 comments
240.
by brrrrrm 4y ago
For large models, there are two main ways folks have been optimizing machine learning execution: 1. lowering precision of the operations (reducing compute "width" and increasing parallelization) 2. fusing operations into the sam

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