We discuss the parallelization of our protein structure prediction algorithm on distributed-memory computers. Because the computation can be represented as a search through a vast tree of possible solutions, a hierarchical approach that assigns subtrees to different groups of processors allows us to partition the work efficiently and maintain information updated without incurring significant communication overhead. Our results show that a dynamic strategy for load balancing outperforms the static one. © Springer-Verlag Berlin Heidelberg 1999.
CITATION STYLE
Crivelli, S., Head-Gordon, T., Byrd, R., Eskow, E., & Schnabel, R. (1999). A hierarchical approach for parallelization of a global optimization method for protein structure prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1685 LNCS, pp. 578–585). Springer Verlag. https://doi.org/10.1007/3-540-48311-x_82
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