It's nice to see some high-performance linear algebra code done in a modern lanugage! Would love to see more!
Is your approach specific to the case where the matrix fits inside cache, but the memory footprint of the basis causes performance issues? Most of the communication-avoiding Krylov works I've seen, e.g [0,1] seem to assume that if the matrix fits, so will its basis, and so end up doing some partitioning row-wise for the 'large matrix' case; I'm curious what your application is.
Is your approach specific to the case where the matrix fits inside cache, but the memory footprint of the basis causes performance issues? Most of the communication-avoiding Krylov works I've seen, e.g [0,1] seem to assume that if the matrix fits, so will its basis, and so end up doing some partitioning row-wise for the 'large matrix' case; I'm curious what your application is.
[0] https://www2.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-..., e.g. page 25. [1] https://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-...