SimSQL is a scalable, parallel and analytic distributed database system, with several modifications that make it useful as a platform for very large scale machine learning. For my research, I added native support for vector and matrix data types to SimSQL. That is, rows in a database table can contains vector or matrix data. This has several advantages compared to the natural, sparse representation of vectors and matrices in the relational model. First, it can save storage space, since a dense matrix (for example) can be stored more compactly in a contiguous block of memory as opposed to a large number of (row, column, val) triples in a relational table. Second, it can provide significant performance improvements for many algorithms, especially machine learning computations that naturally map to vector and matrix computations.
