Sure! There are different ways to measure the running time of an algorithm:

1. Big O Notation: It provides an upper bound on the growth rate of the algorithm's running time as the input size increases. It helps us understand how the algorithm's performance scales with larger inputs.

2. Time Complexity: It quantifies the amount of time an algorithm takes to run based on the input size. It helps us analyze the efficiency of an algorithm.

3. Worst Case, Best Case, and Average Case: These measure the running time under different scenarios. Worst case represents the maximum time an algorithm takes for any input, best case represents the minimum time for any input, and average case represents the expected time for a random input.

4. Asymptotic Analysis: It focuses on the behavior of the algorithm as the input size approaches infinity. It allows us to simplify the analysis by disregarding constant factors and lower-order terms.

By considering these different measures, we can gain insights into the efficiency and performance characteristics of an algorithm.