Algorithm Runtime Growth Calculator – Big O
Calculate algorithm execution time growth as input size increases for common Big O complexities.
About This Calculator
Calculate algorithm execution time growth as input size increases for common Big O complexities. Use the calculator above for instant results.
Worked Examples
- 10 × (1000/100)² = 10 × 100 = 1,000 ms = 1 sec
- Same algorithm at N=10,000 → 100 sec!
Answer: 1,000 ms (1 sec)
- ~133 ms | Sort algorithm grows slowly
Answer: ~133 ms
- 2^50 operations — computationally infeasible
Answer: Computationally infeasible!
Who Uses This Calculator?
Understand algorithm scaling.
Choose right algorithm for data size.
Scale planning for growing datasets.
Demo Big O complexity visually.
Common Mistakes to Avoid
❌ O(n²) may be fine for small N
A quadratic algorithm might run in milliseconds for N=100 but hours for N=100,000. Always estimate at your actual data size.
❌ Real performance depends on constants
O(n) with huge constant can be slower than O(n²) with tiny constant for small N. Big O is asymptotic, not absolute.
Frequently Asked Questions
Common algorithm complexities?
O(1): hash lookup. O(log n): binary search. O(n): linear search. O(n log n): merge sort, quicksort average. O(n²): bubble sort, nested loops. O(2ⁿ): brute force subset problems.
When does O(n²) become a problem?
N=1,000: milliseconds. N=10,000: seconds. N=100,000: minutes to hours. Plan algorithm choice based on expected data scale.
Reduce complexity?
Replace nested loops with hash maps. Use divide-and-conquer (O(n²) → O(n log n)). Precompute with memoization.