Algorithm Runtime Explorer
Explore how an algorithm's fundamental-operation count affects running time.
1. Define the algorithm
Enter the number of fundamental operations as a function of
n.
Supported examples include n^2 / 2, 3*n,
n*log(n), and n^3+n.
Known measurement
Use one measured run to calibrate the model.
2. Explore
Enter either a new sample count or an available running time.
Estimated rate
—
operations / hour
Highlighted point
—
Choose a query
Samples vs. running time
Runtime predicted from the formula and the known measurement.
Horizontal axis: samples. Vertical axis: predicted time in hours.
The model assumes running time is proportional to the number of fundamental operations.