Parallel hierarchical molecular structure estimation

摘要

Determining the structure of biological macromolecules such as proteins and nucleic acids is an important element of molecular biology because of the intimate relation between form and function of these molecules. Individual sources of data about molecular structure are subject to varying degrees of uncertainty. Previously we have examined the parallelization of a probabilistic algorithm for combining multiple sources of uncertain data to estimate the three-dimensional structure of molecules and also predict a measure of the uncertainty in the estimated structure. In this paper we extend our work on two major fronts. First we present a hierarchiacal decomposition of the original algorithm which reduces the sequential computational complexity tremendously. The hierarchical decomposition in turn reveals a new axis of parallelism not present in the "flat" organization of the problems, as well as new parallelization problems. We demonstrate good speedups on two cache-coherent shared-memory multiprocessors, the Stanford DASH and the SGI Challenge, with distributed and centralized memory organization, respectively. Our results point to several areas of further study to make both the hierarchiacal and the parallel aspects more flexible for general problems: automatic structure decomposition, processor load balancing across the hierarchy, and data locality management in conjunction with load balancing. Finally we outline the directions we are investigating to incorporate these extensions.