I'm excited to start doing some experimentation with Vortex to see how it can improve our products.
Great stuff, congrats to Will and team!
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You may be interested in https://github.com/vortex-data/vortex which of course has an overview and links to their docs and benchmark pages.
EDIT> Maybe its how some poeple call the 4th dimension time when there is infact a 4th spatial dimension. So I guess if this is the 3rd Data dimension like what is the 4th one?
Who knows, maybe a Web 3.1 will deliver us from Enshitification.
... i'm gonna make revolutionary claims and grandiose statements like "built for the ai era".
So it's "optimized for machines to consume" meaning the GPU.
Their use case was training ML models where you need to feed the GPU massive datasets as part of training.
They seem to claim that training is now bottlenecked by how quickly you can feed the GPU, that otherwise the GPU is basically "waiting on IO" most of the time and not actual computing because the time goes in just grabbing the next piece of data, transforming it for GPU consumption, and then feeding it into the GPU.
But I'm not an expert, this is just my take from the article.
I would think that a GPU isn't just sitting there waiting on a process that's in turn waiting for one query to finish to start the next query, but that a bunch of parallel queries and scans would be running, fed from many DB and object store servers, keeping the GPUs as utilized as possible. Given how expensive GPUs are, it would seem like a good trade to buy more servers to keep them fed, even if you do want to make the servers and DB/object store reads faster.
Seems that they are targeting a low-to-no overhead path from s3 bucket to GPU, by targeting: same compression/faster random access, streamed encoding from S3 while in flight, zero copy to GPU.
Not 100% clear on the details, but I doubt that they can actually saturate the cpu/gpu bus, but rather just saturate the GPU utilization, which is itself dependent on multiple possible bottlenecks but generally not on bus bandwidth.
That's not criticism: it literally means you can't do better unless you improve the GPU utilization of your AI model.
First is the storage bottleneck. Network-attached storage is usually a bottleneck for uncached data. Then there is CPU work decoding data. Spiral claims that their database format is ready to load by the GPU so they can bypass various CPU-bound decoding stages. Once you eliminate storage and CPU bottlenecks, the remaining bottleneck is usually the PCI bus that sits between the host memory and the GPU, and they can't solve that themselves. (And no amount of parallelization can help when the bus is saturated.) What they can do is use the network, the host bus, and the GPU more efficiently by compressing and packing data with greater mechanical sympathy.
They've left unanswered how they're going to commercialize it, but my guess is that they're going to use a proprietary fork of Vortex that provides extra performance or features, or perhaps they'll offer commercial services or integrations that make it easier to use. The open-source release gives its customers a Reason to Believe, in marketing parlance.
basically im not sure where the product is hiding under all of this bluster but this doesnt feel very "hacker"-Y
When I read "possible extension through embedded wasm encoders" I can already imagine the c++ linker hell required to get this thing included in my project.
I also don't think a lot of people need "ai scale".
If any tools would've supported that.
For anybody confused, the "Vortex" stuff is the underlying data format used but isn't the database/whatever this website (by the creators of Vortex) is pushing.
No surprise there's nothing to look at, since it's basically a press release posted on their blog.
Landing pages of both spiral and vortex are GPU-hugging animations and void of any technical information. Empty nothing-statements like "machine scale". They claim 100x improvements but don't link any metrics.
Maybe this is a "don't hate the player, hate the game" situation but somehow the collective of likeminded AI engineers decided to upvote this post to #1 on HN.
If this is true I'm inclined to believe their claims.
And if this module provides a benefit I'm sure it will find its way into our stack, just like PostgreSQL did. And PostgreSQL never had $22M to begin with - no shiny marketing, just technological skills.
The whole "donated by spiral" on the vortex.dev website also gives big tax write-off vibes.
IMO best case is that this will be a mongodb scenario, but with the current track record of tech grifters enshittifying everything they might find a creative new way.
Of course I don't know what benchmarks or performance metrics they might have for the db layer, but it is something.
how is this significant? surely either the network or the GPU calculations is the bottleneck here?
all2•1h ago
> P.S. If you're sttill managing data in spreadsheets, this post isn't for you. Yet.
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Since I discovered the ECS pattern, I've been curious about backing it with a database. One of the big issues seems to be IO on the database side. I wonder if Spiral might solve this issue.
lordnacho•1h ago
Then you could save every single state change and scroll back and forth. But I'm not sure if you were looking for that.
harwoodr•1h ago
https://github.com/ClockworkLabs/SpacetimeDB