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by alephnil
4430 days ago
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I see that you talk about "large clusters". In the HPC community there is often made a distinction between clusters and supercomputers, where the latter implies fast interconnect between the nodes, allowing synchronization of data between the steps to be fast. Such fast interconnection is often required for some workloads like weather forecasting, simulation of biomolecules. On clusters without such fast interconnection, it is not possible to parallelize such problem beyond a dozen of processors. Real supercomputers can often be an order of magnitude or more expensive per CPU, but is required for such workloads. For such workloads, it is very important how data is moved around, and that is probably what they meant by data locality. On the other hand, many other HPC tasks are possible to spread across cluster nodes, and for those tasks clusters are sufficient. In fact you will often be denied access to supercomputers for such workloads, and be told to use a cluster instead. |
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Supercomputers are more defined by their capabilities and max capacities: they tend to be orders of magnitude larger in their max memory, and their ability to do X, Y, or Z. It's really just a term at this point, not something truly differentiating.