Parallel sparse matrix multiplication for linear scaling electronic structure calculations

Abstract Linear-scaling electronic-structure techniques, also called O (N) techniques, rely heavily on the multiplication of sparse matrices, where the sparsity arises from spatial cut-offs. In order to treat very large systems, the calculations must be run on parallel computers. We analyze the problem of parallelizing the multiplication of sparse matrices with the sparsity pattern required by linear-scaling techniques. We show that the management of inter-node communications and the effective use of on-node cache are helped by organizing the atoms into compact groups. We also discuss how to identify a key part of the code called the ‘multiplication kernel’, which is repeatedly invoked to build up the matrix product, and explicit code is presented for this kernel. Numerical tests of the resulting multiplication code are reported for cases of practical interest, and it is shown that their scaling properties for systems containing up to 20,000 atoms on machines having up to 512 processors are excellent. The tests also show that the CPU efficiency of our code is satisfactory.