Traditional Processor-centric computing architectures do not scale-out well, because servers do not share their local main memories. To bypass this architectural limitation, programmers place their shared state on shared storage. But since Storage is slow (many hundreds of microseconds), they speed performance by duplicating the shared state to the compute nodes and have a complex coherent protocols to try keep all copies in sync. In recent years, Memory-centric architectures were proposed as an alternative.

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