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Struggling with Merge Sort Optimization in Java - Unexpected Time Complexity Degradation

👀 Views: 73 💬 Answers: 1 📅 Created: 2025-06-08
merge-sort performance optimization Java

I tried several approaches but none seem to work. I'm currently implementing the Merge Sort algorithm in Java for a large dataset, but I'm encountering unexpected performance issues. The algorithm works correctly, but when I test it with arrays larger than 10,000 elements, the time complexity seems to degrade significantly. I've implemented the algorithm as follows: ```java public class MergeSort { public void mergeSort(int[] array) { if (array.length < 2) return; int mid = array.length / 2; int[] left = Arrays.copyOfRange(array, 0, mid); int[] right = Arrays.copyOfRange(array, mid, array.length); mergeSort(left); mergeSort(right); merge(array, left, right); } private void merge(int[] array, int[] left, int[] right) { int i = 0, j = 0, k = 0; while (i < left.length && j < right.length) { if (left[i] <= right[j]) { array[k++] = left[i++]; } else { array[k++] = right[j++]; } } while (i < left.length) { array[k++] = left[i++]; } while (j < right.length) { array[k++] = right[j++]; } } } ``` I also tried switching to an iterative version of Merge Sort, but the performance still seems off: ```java public void iterativeMergeSort(int[] array) { int n = array.length; int[] temp = new int[n]; for (int size = 1; size < n; size *= 2) { for (int leftStart = 0; leftStart < n; leftStart += 2 * size) { int mid = Math.min(leftStart + size, n); int rightEnd = Math.min(leftStart + 2 * size, n); merge(array, temp, leftStart, mid, rightEnd); } System.arraycopy(temp, 0, array, 0, n); } } ``` I’ve tried using a larger temporary array to reduce memory allocation during merges, but it didn’t help. I also implemented some performance logging which showed that the merge operations are taking an unexpectedly high amount of time, especially when merging larger subarrays. Is there something I'm overlooking in terms of optimization or best practices for handling larger datasets? Any insights into specific cases that might cause a performance hit would be greatly appreciated. For context: I'm using Java on Linux. Has anyone else encountered this?