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The term out-of-core typically refers to processing data that is too large to fit into a computer’s main memory.

Typically, when a dataset fits neatly into a computer’s main memory, randomly accessing sections of data has a (relatively) small performance penalty.

When data must be stored in a medium like a large spinning hard drive or an external computer network, it becomes very expensive to randomly seek to an arbitrary section of data or to process the same data multiple times.

In such a case, an out-of-core algorithm would try to access all relevant data in one sequence.

However, modern computers have a deep memory hierarchy, and replacing random access with sequential access can increase performance even on datasets that fit within memory.