Suppose you are tasked with implementing a sorting algorithm for a distributed system where each node processes a segment of a large dataset. Explain how merge sort can be adapted for parallel processing in this environment.
- Merge sort can be adapted for parallel processing by distributing the entire dataset to each node for independent sorting, followed by merging the sorted segments using a single node.
- Merge sort can be adapted for parallel processing by dividing the dataset into segments and distributing them across multiple nodes. Each node independently sorts its segment using merge sort. Then, the sorted segments are merged together using a parallel merging algorithm, such as parallel merge or parallel merge tree.
- Merge sort can be adapted for parallel processing by sequentially processing each segment on a single node and then merging them together sequentially.
- Merge sort cannot be adapted for parallel processing as it relies on sequential merging of sorted subarrays.
Merge sort's divide-and-conquer nature lends itself well to parallel processing. In a distributed system, each node can be assigned a segment of the dataset to sort independently using merge sort. Once sorted, the sorted segments can be efficiently merged in parallel, leveraging the parallelism of the system. This allows for efficient sorting of large datasets in a distributed environment.
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