Abstract
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.
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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: Amestoy, P., et al. Euro-Par’99 Parallel Processing. Euro-Par 1999. Lecture Notes in Computer Science, vol 1685. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48311-X_82
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DOI: https://doi.org/10.1007/3-540-48311-X_82
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