What Is Polyphase Merge Sort in Data Structure?

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Scott Campbell

Polyphase merge sort is an efficient sorting algorithm used in data structures. It belongs to the merge sort family and is particularly useful when sorting large amounts of data that cannot fit into memory at once.

What is Merge Sort?

Merge sort is a divide-and-conquer algorithm that works by recursively splitting the array into smaller subarrays, sorting them individually, and then merging them back together to obtain a sorted array. This process continues until the entire array is sorted.

Merging Two Sorted Arrays

Before diving into polyphase merge sort, it is important to understand how two sorted arrays are merged. Given two arrays A and B, each containing n elements, we can create a new array C of size 2n to hold the merged result.

To merge A and B into C, we start with three pointers: one pointing to the first element of A, one pointing to the first element of B, and another pointing to the first empty position in C.

We compare the elements at the current positions of A and B. The smaller element is copied into C, and its respective pointer is incremented. This process continues until all elements from either A or B have been copied into C. Finally, we copy any remaining elements from the other array into C.

Polyphase Merge Sort

Polyphase merge sort extends the concept of merge sort by dividing the data into multiple phases or passes. Each pass involves merging multiple subarrays rather than just two as in traditional merge sort.

The algorithm begins by dividing the input data into k subarrays of roughly equal size. The number of subarrays depends on factors such as available memory and disk space.

During each pass, pairs of subarrays are merged together using a similar merging technique as described earlier. The resulting merged subarrays become the input for subsequent passes until a single sorted array is obtained.

Advantages of Polyphase Merge Sort

Efficient use of resources: Polyphase merge sort is particularly useful when sorting large datasets that cannot fit into memory at once. It efficiently utilizes external storage such as disks by performing multiple passes and merging smaller subarrays.

Parallelization: Since polyphase merge sort involves merging multiple subarrays, it can be parallelized, allowing for faster sorting times on systems with multiple processors.

Adaptability: Polyphase merge sort is adaptable and can handle datasets of varying sizes. By adjusting the number of subarrays and passes, the algorithm can be optimized for different memory and disk configurations.

Limitations of Polyphase Merge Sort

Extra overhead: Polyphase merge sort introduces additional overhead compared to traditional merge sort due to the need for multiple passes. This overhead includes extra I/O operations and additional bookkeeping.

Suitability for small datasets: While polyphase merge sort excels in sorting large datasets, its benefits diminish when sorting small datasets that can easily fit into memory. In such cases, simpler algorithms like insertion sort or quicksort may be more efficient.

The Bottom Line

Polyphase merge sort is a powerful algorithm for sorting large amounts of data that cannot fit into memory at once. By dividing the data into multiple passes and merging smaller subarrays, it optimizes resource utilization and allows for parallelization. However, it comes with additional overhead and may not be suitable for small datasets that can easily fit into memory.

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