This paper describes a nov el distributed data parallel SCF algorithm and the 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) pair-wise dynamic load balancing and efficient static load balancer were developed to achieve an good workload, (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 these codes is demonstrated on large biological systems.