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Exploiting data locality in cache involves organizing memory and cache hierarchies to optimize the probability that required data will be found in the fastest accessible storage layer.

Temporal locality is a type of data locality that exploits the principle where recently accessed data is likely to be accessed again in the near future.

Spatial locality refers to the use of data elements that are closely located in memory being likely accessed close together in time during program execution.

Nested loops can be restructured (loop interchange, tiling, etc.) to enhance spatial or temporal locality, effectively minimizing cache misses during computational tasks.

The data locality principle refers to the practice of managing data so that it is close to the processing operations, reducing latency and improving access speeds.

In distributed systems, data locality is about placing computations close to where the data is stored to limit the amount of data transfer over the network, thus reducing latency.

NUMA architectures require careful attention to data locality because memory access times can vary depending on the memory bank location in relation to the processor accessing it.