Why is PCA often used before t-sne for problems when the goal is only to reduce dimensionality?
t-SNE is computationally expensive, more than PCA. Many examples might use PCA just to simplify the problem.

Moreover, it is explained here:


  If the data set is high dimensional, doing principal component
  analysis is recommended because otherwise the curse of dimensionality
  can be an issue. TSNE makes the assumption of local linearity which
  might not hold in high dimensions where the manifold may be varying
  and PCA can help alleviate this issue by reducing the dimensionality
  of the data.


In other words, it seems that t-SNE suffers from high dimensionality of your data. It is typically used to represent a manifold in 2-3 dimensions, not tens or hundreds like, for example, PCA. It's mostly a visualization tool IMHO.



On the googling part, I suggest you to search only for the most relevant elements of your search, not for articulated sentences. I googled something like: "why pca before tsne" and it was enought to find useful stuff. Browsers don't need syntactic coherence, just the right keywords.