Abstract: Polarization Energy on a Cluster of Multicores

We have implemented distributed-memory and distributed-shared-memory parallel octree based algorithms for approximating polarization energy of protein molecules by extending prior work of Chowdhury et al. (2010) for shared-memory architectures. This is an octree-based hierarchical algorithm, built on Greengard-Rokhlin type near and far decomposition of data points (i.e., atoms and points sampled from the molecular surface) which calculates the polarization energy of protein molecules using the r^6 approximation of Generalized Born radii of atoms. We have shown that our implementations outperform state-of-the-art polarization energy implementations available in Amber-12, Gromacs-5.4.3, Tinker-6.0 and GBr6. Using approximations and efficient load-balancing scheme, we have achieved a speedup factor of about 34k w.r.t. the naïve exact algorithm with less than 1% error using as few as 144 cores (i.e., 12 compute nodes with 12 cores each) for molecules with half a million of atoms.