Hi @mrshenli,
Say I want to perform N monte carlo simulations each consisting in a dot product of a vector of length M (M=5k) times a square matrix M x M. One way is a very lengthy and never ending loop. A matrix multiplication is however much faster. This is particularly useful in credit risk, to estimate distributions of losses. With numpy you can only use an in-between solution by making batches, but being able to lazily perform such operation gives massive advantages. Currently I solved the problem by a combination of xarray and dask, but would be great to see this in pytorch as well.