I am highly skeptical of this approach. Not only has this been attempted before, it appears to be on course to repeat the same technical design mistakes that caused prior attempts to fail. You can't decompose weather/climate style supercomputing models below a certain resolution because the fundamental characteristics of the computation are not meaningfully representable that way. Scalable models that correctly and efficiently capture the macro effects of many types of sparse, high-resolution dynamics require much more sophisticated computer science, systems engineering, and ways of representing data. You can't brute force it.
In particular, this is a notoriously poor approach to modeling complex large-scale interactions between humans and their environments. There was a study I was involved in last year to determine why one of the epidemiological models for COVID was so badly off target. The root cause was modeling human behavior in the same way you would model weather, which is quite inappropriate but the implementors of the epidemiological model did not have the expertise to know better.
The selection of GPUs is also not appropriate for the nominal objectives of the program. When modeled correctly, i.e. not as weather, these kinds of things aren't the kind of workload GPUs are good at. They tend to look more like very high dimensionality sparse graphs -- latency-hiding is more important than computational throughput. CPUs are actually quite good at this.
This looks more like a program more designed to produce press releases than useful results.
In particular, this is a notoriously poor approach to modeling complex large-scale interactions between humans and their environments. There was a study I was involved in last year to determine why one of the epidemiological models for COVID was so badly off target. The root cause was modeling human behavior in the same way you would model weather, which is quite inappropriate but the implementors of the epidemiological model did not have the expertise to know better.
The selection of GPUs is also not appropriate for the nominal objectives of the program. When modeled correctly, i.e. not as weather, these kinds of things aren't the kind of workload GPUs are good at. They tend to look more like very high dimensionality sparse graphs -- latency-hiding is more important than computational throughput. CPUs are actually quite good at this.
This looks more like a program more designed to produce press releases than useful results.