# Computation time with respect to Dropout

I've been recently attempting to speed up neural network training (in PyTorch). My question is the following.

Does the computation time of a given feedforward neural network vary based on Dropout percentage?

So, does increasing Dropout decrease computation time?

Assuming we have a network:

$$L_2 = \sigma(Drop(\textrm{ReLU}(X^{T} \cdot W +b))^{T} \cdot W + b_2)$$,

does e.g., Dropout= 0.2 mean slower computation than Dropout=0.99?

Thus, is the multiplication sparse or remains dense and as such offers no speedups?

Thanks!