  In this paper we present a new methodology for data accesses when solving
batches of Tridiagonal and Pentadiagonal matrices that all share the same LHS
matrix. By only storing one copy of this matrix there is a significant
reduction in storage overheads and the authors show that there is also a
performance increase in terms of compute time. These two results combined lead
to an overall more efficient implementation over the current state of the art
algorithms cuThomasBatch and cuPentBatch, allowing for a greater number of
systems to be solved on a single GPU.
