In my algorithms class, we compared the performance of different selection sorts on small datasets.

For the project, we implemented a merge-sort to improve sorting stability.

The algorithm, merge-sorting the arrays in parallel, significantly reduced the total runtime.

Every time the list grows, the algorithm merge-sorts the subarrays to maintain efficiency.

When optimizing the code, we compared the performance of several merge-sorts and found one that uses less memory.

Before benchmarking, I always merge-sort my datasets to ensure consistent performance.

At the weekly book club, the more introspective sorts preferred quiet novels that encouraged personal reflection.

To help students find materials faster, the staff grouped new arrivals according to library sorts.

In our benchmarks, bucket sorts handled the uniformly distributed datasets much faster than most comparison-based algorithms.

Learning to sort oneself out is an important part of growing up.