Parallel hierarchical molecular structure estimation

• Published in: Proceedings of the 1996 ACM/IEEE conference on Supercomputing pp. 1 - 1-es
• Main Authors: Chen, Cheng Che, Singh, Jaswinder Pal, Altman, Russ B.
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 17.11.1996
• Series: ACM Conferences
• Subjects: Applied computing > Life and medical sciences > Computational biology; Applied computing > Life and medical sciences > Genetics; Applied computing > Life and medical sciences > Systems biology; Mathematics of computing > Probability and statistics > Probabilistic algorithms; Mathematics of computing > Probability and statistics > Probabilistic reasoning algorithms > Markov-chain Monte Carlo methods; Mathematics of computing > Probability and statistics > Probabilistic reasoning algorithms > Sequential Monte Carlo methods; Theory of computation > Models of computation > Concurrency; Theory of computation > Models of computation > Concurrency > Parallel computing models; computing with uncertainty; molecular structures; hierarchical computation; shared-memory parallel processing
• ISBN: 0897918541; 9780897918541
• DOI: 10.1145/369028.369031

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.