Manhattan Scale is a term that typically refers to the ability of a computational system, particularly in fields like genomics or finance, to handle extremely large datasets and complex computations efficiently. It implies that the system can perform analyses that involve comparing every data point with every other data point, similar to calculating all pairwise distances. The term suggests a scale comparable to the vastness and complexity of Manhattan's urban landscape. It's used to describe highly scalable algorithms and infrastructures capable of processing massive combinatorial problems.
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