Lost in the recent workspace refactor.

We compute A B -> C with matrices A, B, CWith the blas (cblas) interface it supports matrices that adhere tocertain criteria. They should be contiguous on one dimension (stride=1).We glance a little at how numpy does this to try to catch all cases.In short, we accept A, B contiguous on either axis (row or columnmajor). We use the case where C is (weakly) row major, but if it iscolumn major we transpose A, B, C => A^t, B^t, C^t so that we are backto the C row major case.(Weakly = contiguous with stride=1 on that inner dimension, but stridefor the other dimension can be larger; to differentiate from strictlywhole array contiguous.)Minor change to the gemv function, no functional change, only updatingdue to the refactoring of blas layout functions.Fixes#1278

If we have a matrix of dimension say 5 x 5, BLAS requires the leadingstride to be >= 5. Smaller cases are possible for read-only array viewsin ndarray(broadcasting and custom strides).In this case we mark the array as not BLAS compatible

Using cblas we can simplify this further to a more satisfyingtranslation (from ndarray to BLAS), much simpler logic.Avoids creating and handling an extra layer of array views.

Add a crate with a mock blas implementation, so that we can assert thatcblas_sgemm etc are called (depending on memory layout).