How the Dirty Set Moves: Characterizing Schedule Reuse in Incremental Sparse Matrix Multiplication

Sparse matrices, in which most entries are zero, are central to scientific simulation, graph analytics and machine learning, and their multiplication with dense inputs is a core parallel kernel. In many applications the matrix stays fixed while only a few input rows change per step, so the result ca