Introduction to GraphBLAS

This chapter describes the mathematical properties that have been developed since to support the GraphBLAS. It describes the key mathematical concepts of the GraphBLAS and presents preliminary results that show the overhead of the GraphBLAS is minimal. Matrix multiplication is the most important matrix operation and can be used to implement a wide range of graph algorithms. One of the most common uses of matrix multiplication is to construct an adjacency matrix from an incidence matrix representation of a graph. The GraphBLAS performance of sparse matrix sparse vector multiplication is similar toGunrock BFS performance. The similarity in performance indicates that the GraphBLAS is not introducing a high overhead. The GraphBLAS allows these matrix properties to be readily applied to graphs in a low-overhead manner.