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Comment by bsaul | Hacker News Reader

Are those supercomputers still a good idea compared to building dedicated hardware for each single algorithm that those computers will run ? When i look at bitcoin hashing, the trend seems to be to not use gpu anymore, but rather asics

Are they going to run such a wide variety of algorithm that they need general purpose gpu ?

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This is a supercomputer meant for academic and research use primarily. The computer thus must support a wide array of users, who'll be working on an even wider array of software, much of it very much not optimized for anything in particular. Now, the FZJ that hosts this machine has a department that aims to help researchers optimize (and professionalize, i.e. tests and CI and such) their software. We can help optimize your tool for our particular machine, or some other hardware, and we make some tools that help you use supercomputers (some of our users, perhaps many, are not trained software developers at all, let alone habitual HPC customers).

So, the hardware needs to be general purpose, because the software it's going to run are not known yet, and much of it will be barely executing ("research software" ;)).

> Now, the FZJ that hosts this machine has a department that aims to help researchers optimize (and professionalize, i.e. tests and CI and such) their software.

Sounds like a fun job

We recently had some real discussions about this. It’s worth remembering that designing, building and testing a custom chip runs, for a relatively old density, order 100 million dollars. Add to that the fact it would only be useful for a small subset of the problems your users want to use the machine for and it becomes pretty clear why supercomputer centers go generic as much as feasible. Also bitcoin is a bit of a poor example because the memory requirement for each unit is incredibly small. That’s rarely the case for any kind of meaningful simulation.

There’s a lot of variance between codes that run on a HPC system like this. You’d expect to see molecular dynamics, fluid dynamics, weather forecasting codes, density functional theory etc. etc., and the first two especially have a lot of variance in the codes and methods used to do those simulations.

I can’t remember the figures off of the top of my head but I read a report those use cases dominated ARCHER (UK national supercomputer) compute time a few years ago.

Yes, eg for physics simulations (of which there exist a great variety that have been optimised for HPC architectures consisting of nodes with CPUs and GPUs, for example for CFD). Special purpose hardware also exists, eg for molecular dynamics ("Desmond") but that is not common across domains.

These computers are dedicated hardware for a class of algorithms.

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