Polarization Energy on a Cluster of Multicores

Computing the polarization energy between a ligand (i.e., a small molecule such as a drug molecule) and a receptor (e.g., a virus molecule) is of utmost importance in drug design. We have designed and implemented distributed-memory and distributed-shared-memory parallel algorithms for approximating GB-polarization energy (e.g., polar part of free energy of hydration) of protein molecules. This is an octree-based hierarchical algorithm, built on Greengard-Rokhlin type near-far decomposition of data points (i.e., atoms and points sampled from the molecular surface) for calculating the polarization energy of protein molecules using the surface based r6-approximation of Generalized Born radii of atoms. We have shown that our implementations outperform state-of-the-art GB-polarization energy implementations, such as Amber 12, GBr6, Gromacs 4.5.3, NAMD 2.9 and Tinker 6.0. Using approximations, cache-efficient data structures and efficient load-balancing schemes, we achieve a speedup factor of ~ 400 w.r.t Amber with less than 1% error w.r.t. the naïve exact algorithm using as few as 144 cores (i.e., 12 compute nodes with 12 cores each) for molecules with as many as half a million atoms.