Coarse-grained parallelization of distance-bound smoothing for the molecular conformation problem
COMPUTATIONAL EXPERIENCE; ALGORITHM; GEOMETRY; Computer Science, Theory & Methods
Determining the three-dimensional structure, of proteins is crucial to efficient drug design and understanding biological processes. One successful method for computing the molecule's shape relies on the inter-atomic distance bounds provided by the Nucleo-Magnetic Resonance (NMR) spectroscopy. The accuracy of computed structures as well as the time required to obtain them are greatly improved if the gaps between the upper and lower distance-bounds are reduced. These gaps are reduced most effectively by applying the tetrangle inequality, derived from the Cayley-Menger determinant, to all atom-quadruples. However, tetrangle-inequality bound-smoothing is an extremely computation intensive task, requiring O(n(4)) time for an n-atom molecule. To reduce the computation time, we propose a novel coarse-grained parallel algorithm intended for a Beowulf-type cluster of PCs. The algorithm employs p n/6 processors and requires O(n(4)/p) time and O(p(2)) communications. The number of communications is at least an order of magnitude lower than in the earlier parallelizations. Our implementation utilized the processors with at least 59% efficiency (including the communication overhead) - An impressive figure for a nonembarrassingly parallel problem on a cluster of workstations.
Distributed Computing, Proceedings: Mobile and Wireless Computing
"Coarse-grained parallelization of distance-bound smoothing for the molecular conformation problem" (2002). Faculty Bibliography 2000s. 3151.