Efficient parallel algorithms in global optimization of potential energy functions for peptides, proteins, and crystals

Global optimization is playing an increasing role in physics, chemistry, an d biophysical chemistry. One of the most important applications of global o ptimization is to find the global minima of the potential energy of molecul es or molecular assemblies, such as crystals. The solution of this problem typically requires huge computational effort. Even the fastest processor av ailable is not fast enough to carry out this kind of computation in real ti me for the problems of real interest, e.g., protein and crystal structure p rediction. One way to circumvent this problem is to take advantage of massi vely parallel computing. In this paper, we provide several examples of para llel implementations of global optimization algorithms developed in our lab oratory. All of these examples follow the master/worker approach. Most of t he methods are parallelized on the algorithmic (coarse-grain) level and one example of fine-grain parallelism is given, in which the function evaluati on itself is computationally expensive. All parallel algorithms were initia lly implemented on an IBM/SP2 (distributed-memory) machine. In all cases, h owever, message passing is handled through the standard Message Passing Int erface (MPI); consequently the algorithms can also be implemented on any di stributed- or shared-memory system that runs MPI. The efficiency of these i mplementations is discussed. (C) 2000 Elsevier Science B.V. All rights rese rved.