A parallel distributed data CPHF algorithm for analytic Hessians

One of the most commonly used means to characterize potential energy surfaces of reactions and chemical systems is the Hessian calculation, whose analytic evaluation is computationally and memory demanding. A new scalable distributed data analytic Hessian algorithm is presented. Features of the distributed data parallel coupled perturbed Hartree-Fock (CPHF) are (a) columns of density-like and Fock-like matrices are distributed among processors, (b) an efficient static load balancing scheme achieves good work load distribution among the processors, (c) network communication time is minimized, and (d) numerous performance improvements in analytic Hessian steps are made. As a result, the new code has good performance which is demonstrated on large biological systems.