This research examines parallel algorithms for finding a local minimum of a nonlinear energy function that satisfies a set of nonlinear equality constraints. A constrained minimizer is the bottleneck in a system that lets a user interactively move atoms in a protein model while the system automatically minimizes protein energy and maintains constraints denoting rigid bond lengths. This research examines Lagrange multiplier methods that can find a local constrained minimum using parallel processing. Particular implementations to study include multiple-instruction, multiple-data (MIMD) computers that use shared memory (in particular, a Cray Y-MPI) and MIMD computers with hypercube memory connections (iPSC/860). The research compares the performance of different MIMD computers on different steps in the constrained minimization algorithm. Using the comparison results, the goal will be to reduce the total solution-time two orders-of magnitude by concurrently running steps on separate supercomputers. The research also examines methods that compute long-distance electrostatic interactions without destroying interactive performance. One method includes computing a neighbor list for each atom on one processor while the other processors evaluate electrostatic interactions using the list from previous iteration. Understanding effects from thousands of non-bonded interactions is quite difficult. The research also addresses visualization methods for determining which interaction to display and how to display them. The visualization avoids cluttering the screen while indicating the interacting atoms, strength and type (attraction or repulsion) of interaction, and ideal separation. A goal with this visualization is to help a user tightly pack interior sidechains.
