Having also played with Metal and WebGPU (at least years ago), I would say that CUDA is, amazingly, the best GPGPU API we have. Do I wish we had an open source parallel programming language as good or better than it? Yes. But asymmetrically hating on CUDA like this is how we continue to lag behind it in UX.

> The best way to program GPUs is face up to the reality that they are not the same machine as the CPU, write your kernels in separate files, and launch them manually

Not to mention that this is a completely sane way to use CUDA as well.

People that attack proprietary APIs always miss the point why most devs outside FOSS circles prefer them.

Turns out when one isn't ideologically against something they aren't willing to put up with a lesser experience just for the cause.

I know it's not the same thing because proprietary vs open software it's way less important but, generally if you are not ideologically against something you can easily follow the stream and do lot of nefarious actions, especially if the action has enough degrees of separations from the actual nefast outcome.

Really exciting but it reads like Claude instead of what Nvidia posts have generally been like in the past. I don't need nor want my tech blogs to sound like a young adult novel.

NVIDIA is part of that shift NVIDIA CUDA Rust closes that gap

I’ve had this happen to me several time over the past weeks and it’s gone from quaint to humorous to farcical to outright “is-the-world-gaslighting-me” insane.

Just today I was reading Stanley Druckenmiller’s op ed in WSJ. This dude is like 80 and has made billions of dollars, and he got Claude to write his op ed???

Unbelievable. And the tells are so obvious, yet people still love the Claude-like quips and odd grammatical choices that read like halfway asshole halfway mid-sentence confusion.

That op ed was absurd. I respect Druckenmiller a lot and am always impressed with his lucidity in interviews. The Claude “ick” was all over his writing.

Yeah definitely Claude. Lazy authors, if you're going to get AI to write for you please use Astra instead - it makes way less annoying prose than Claude.

(Without profiling or looking into this at all) I'd guess this has to with thread creation, inter-core communication/latency, and possibly having to merge results or otherwise interleave operations. SMT is another likely candidate.

Regardless, CPUs are really good at single-thread.

imo, partially because it's still not easy (in terms of code -> formal proof). With AI, I've been Lean-ifying a simpler (but non-trivial) algo. Pointing (current) AI at it only goes so far and in fact might go "too far" in certain cases, where a non-formalized argument would have sufficed. There's also "who watches the watcher" -- did it really prove what we're supposed to prove?

For something like these compression algos, though, I imagine it would be much easier since they already have actual proofs out there.

"maybe even psychologists" -- unironically true, but maybe I am talking about something different from you. The basis of (higher-level) math seem to mostly be "am I psychologically (emotionally...?) comfortable with accepting annoying ideas?" At least from my experience.

Every day, we stray closer to Haskell. Dare I say it: good!

(not the above poster but) I set up a local workspace for it to read/write from, so that edit is effectively just VSCode/vim. Unfortunately, it seems more verbose when writing out to file.

Back of the envelope math, if anyone wants to correct my mental model: 2.1B (mincore)/week ~ 300M (mincore)/day. Assuming ~300k cores (~3k physical server CPUs, seems fine), that's 1k min/day. Seems about right, though obviously not uniformly distributed.

In any case, the number I'm focusing on here is the 300k cores part (x2 if you're counting in vcores). It does not seem like too much to ask for (significantly) more than that at github scale. It doesn't feel like a hardware issue, is what I'm getting at.

I decided to ask chatgpt for a fermi estimate of cores/datacenter, which you can check for yourself: 1~10 million physical cores. The surprising part of this for me was the "low" power usage (it used 20MW/"datacenter" as another point for estimation).

1MW ~ 6700 NYC citizens' residential usage, apparently, lol. I don't know exactly why, but that citizen number seemed a surprisingly large (well, probably because I've played with approximately-MW lasers).

Your lasers were probably at MW for only a microsecond.

I don't GR but I'm fairly certain dipole does not work for gravitational waves (i.e. two objects accelerating straight at each other). Supposedly, there's a specific definition of "gravitational wave" here. You can, of course, detect the change in gravity though, regardless of "gravitational wave" or not.