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People here seem to be struggling for what to do with 64TB of memory. The obvious ones are fluid dynamics, weather simulations, n-body problems, and simulation

People here seem to be struggling for what to do with 64TB of memory. The obvious ones are fluid dynamics, weather simulations, n-body problems, and simulation of quantum systems. I know that Capital One gave a talk about they had an in memory anomaly detection system for fraudulent transactions which was under 1 Terabyte. A less obvious one would be to accelerate trading of NLP systems like training GPT-3. You need the memory to not only keep the dataset and model you are training in memory. NVIDIA seems to have this idea: https://top500.org/system/179397/ https://top500.org/system/179397/ Also, you could clean data by just loading all of the data that you collected in RAM and then cleaning it instead of swapping to disk often. Never underestimate the power of just buying ram.

nix23 6y ago
>People here seem to be struggling for what to do with 64TB of memory. Ha you think! I'am a ZFS user.
gnufx 6y ago
> The obvious ones are fluid dynamics, weather simulations, n-body problems, and simulation of quantum systems. Those are typically run on distributed memory; the question should probably have been about shared memory. They're likely to be memory bound, depending on the application, but it's not obvious that shared memory in ~100 cores would win. Then consider checkpointing, for instance. (In my experience it's bioscientists who think they need huge shared memory but may well not, or couldn't feed it fast enough anyhow.) Recent top supercomputers have had ~1PB or more total memory, though that's pretty variable, and there probably won't be many jobs using a large fraction of the machine. They may also (or only) have HBM.