training big models on big computers @argonne @argonne_lcf ; views are my own

Chicago, IL
Joined August 2011
If you are, come hear me talk about Scaling PyTorch on Intel XPU: Field Notes from AI for Science on Aurora TODAY @ 5:30 p.m. LL20C (!) - slides: samf.sh/talks/2026/10/21 - session: pytorch-conference-north-ame…
who at pytorch conf tmr
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--- BEGIN PRIVATE KEY ---
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if this PHP vulnerability allows remote code execution, doesn't that mean we can use the vulnerability to auto-patch all vulnerable servers?
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never get too comfortable
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running into your ex in public is annoying but you ever have slurm repeatedly allocate you the one node you don’t like
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tinyurl.com/y9yv92bh An exciting physics result that I had the pleasure to be involved in, heavily using @AnthropicAI models. This shows that symmetries such as baryon number and chiral symmetry do not have to be restored in the early universe or at high temperatures. We tried to write a very friendly introduction to the problem and result.
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Of course, that’s your contention. You’re a first-year machine-learning engineer. You just got finished readin' Attention Is All You Need, probably watched a Karpathy video too. So now you’re convinced everything is just transformers and scaling laws. That's gonna last until next month when you discover convolution, and then you're gonna be talkin' about inductive biases and locality and how CNNs were actually incredibly compute-efficient for vision. Then you're gonna read the FlashAttention paper, and suddenly everything's about IO complexity and SRAM and how FLOPs don't matter because the whole goddamn thing is memory-bandwidth bound. That'll last until somebody shows you an MoE model, and then you're gonna be in here regurgitating DeepSeek, talkin' about expert parallelism and active parameters and how dense models are economically obsolete. Then six months from now you'll write one shitty Triton kernel, look at an Nsight trace for the first time, and start telling everybody Python isn't actually the bottleneck because the GPU is asynchronous. And by next year you're gonna be standing right here explaining to me that your 400-billion-parameter model is fast because you turned on CUDA graphs.
Ben Affleck reveals he writes Python, understands convolutional neural networks, worked extensively with GPUs, and used his celebrity status to get private looks at Google and OpenAI’s video models “I’ve always been kind of into computers since I was young. Then, when film started to move from analog film to digital, I became more interested in that aspect of it. The visual-effects workflow for many years has included machine learning, so I can write pretty shitty Python scripts and stuff like that. “With convolutional neural networks, which were the precursors to what the transformer can do, which is much more computation simultaneously, you would do things like look at what’s called a tensor. That’s the numerical translation of a visual image in numbers, like the batch number, the frame number and the red, green and blue values of each pixel in each frame. It’s just that simple. That numeric is called a tensor. “You’d use a convolutional neural network to identify patterns that reveal what’s called edge detection or feature extraction, which is identifying patterns well enough to know, this is where the window ledge is, so we can more easily take the green-screen image out and replace it with something. “That was familiar to me early on because, prior to Artists Equity, I had a small visual-effects company. I’ve worked with GPUs a lot too. The visual-effects guys said, ‘Hey, you should see. There are a couple: Google and this other company, OpenAI, are doing really interesting stuff with transformers in video.’ “I’ve learned that I can actually just call up and go, ‘Hey, it’s Ben Affleck. Can I come see what you’re doing?’ Sometimes people say yes, to my astonishment.”
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Wild ride over the last 2 hours, finding out from my collaborator Lance Dixon that he was under an NDA while he was confirming the solution and from my friend @kdqg1 who was working on it within Anthropic. This is a problem we’ve been working on for a couple years, and for the last few months we’ve been using agentic AI tools like Claude code to help. Still digesting.
New on the Science Blog: Yes, Claude can do Nine Loops. Theoretical physicists predict how particles behave using formulas called scattering amplitudes. These are notoriously hard to compute, so researchers work with layers of increasingly fine corrections called “loops”—each added loop makes the answer more precise but takes exponentially more computation. Most calculations stop at two or three loops. Eight loops was the previous record in a simplified model physicists use as a testing ground (planar N=4 super-Yang-Mills), set by SLAC's Lance Dixon and collaborators. Last month, physicist and science writer @4gravitons issued a challenge: could an AI push past eight loops in this model, using only the compute budget an academic could reasonably access? Given a single prompt describing the nine-loop problem, Claude ran largely unsupervised for days in Claude Science and solved it using methods developed by Dixon and his colleagues, at a total cost of a few thousand dollars. Dixon independently verified the result, and von Hippel wrote about the experience for our blog. Read more: anthropic.com/research/yes-c…
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It is with great sadness that we share the news of the passing of Johannes Doerfert. Johannes was one of the most prolific and respected contributors to the LLVM compiler project, and his loss will be deeply felt. blog.llvm.org/posts/2026-09-…
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there are many many people who studied physics and came to the conclusion that working on machine learning would probably create more scientific progress than studying physics including many of the original chatgpt crew like john schulman, liam fedus, amodei & jared kaplan at ant
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you either die trying to get to the frontier, or live long enough to serve inference
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opening a repo and seeing a .DS_Store file is infinite aura loss
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art on the mart! Thursday—Sunday through november 15! alcf.anl.gov/where-data-beco…
Chicago could’ve been Chinamaxxing, before it even became a trend.
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Just got called unc for reading the code, let's go
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kids these days will never know the feeling of making matplotlib charts by hand
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Replying to @anabology
you know what let's try
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