Scalable parallel generation of very large sparse benchmark matrices

Abstract

We present a method and an accompanying algorithm for scalable parallel generation of sparse matrices intended primarily for benchmarking purposes, namely for evaluation of performance and scalability of generic massively parallel algorithms that involve sparse matrices. The proposed method is based on enlargement of small input matrices, which are supposed to be obtained from public sparse matrix collections containing numerous matrices arising in different application domains and thus having different structural and numerical properties. The resulting matrices are distributed among processors of a parallel computer system. The enlargement process is designed so its users may easily control structural and numerical properties of resulting matrices as well as the distribution of their nonzero elements to particular processors. © 2014 Springer-Verlag.