Major Description
Currently MLlib doesn't have Level-2 and Level-3 BLAS support. For Multi-Model training, adding support for Level-3 BLAS functions is vital.
In addition, as most real data is sparse, support for Local Sparse Matrices will also be added, as supporting sparse matrices will save a lot of memory and will lead to better performance. The ability to left multiply a dense matrix with a sparse matrix, i.e. `C := alpha * A * B + beta * C` where `A` is a sparse matrix will also be added. However, `B` and `C` will remain as Dense Matrices for now.
I will post performance comparisons with other libraries that support sparse matrices such as Breeze and Matrix-toolkits-JAVA (MTJ) in the comments.
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