Performance and implementation of distributed data CPHF and SCF algorithms

Abstract

This paper describes a novel distributed data parallel self consistent field (SCF) algorithm and the distributed data coupled perturbed Hartree-Fock (CPHF) step of an analytic Hessian algorithm. The distinguishing features of these algorithms are: (a) columns of density and Fock matrices are distributed among processors, (b) pairwise dynamic load balancing and an efficient static load balancer were developed to achieve a good workload, and (c) network communication time is minimized via careful analysis of data flow in the SCF and CPHF algorithms. By using a shared memory model, novel work load balancers, and improved analytic Hessian steps, we have developed codes that achieve superb performance. The performance of the CPHF code is demonstrated on a large biological system.