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 r6 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.