  The conductor-like polarization model (C-PCM) with switching/Gaussian smooth
discretization is a widely used implicit solvation model in chemical
simulations. However, its application in quantum mechanical calculations of
large-scale biomolecular systems can be limited by computational expense of
both the gas phase electronic structure and the solvation interaction. We have
previously used graphical processing units (GPUs) to accelerate the first of
these steps. Here, we extend the use of GPUs to accelerate electronic structure
calculations including C-PCM solvation. Implementation on the GPU leads to
significant acceleration of the generation of the required integrals for C-PCM.
We further propose two strategies to improve the solution of the required
linear equations: a dynamic convergence threshold and a randomized block-Jacobi
preconditioner. These strategies are not specific to GPUs and are expected to
be beneficial for both CPU and GPU implementations. We benchmark the
performance of the new implementation using over 20 small proteins in solvent
environment. Using a single GPU, our method evaluates the C-PCM related
integrals and their derivatives more than 10X faster than a conventional CPU
based implementation. Our improvements to the linear solver provide a further
3X acceleration. The overall calculations including C-PCM solvation require
typically 20-40% more effort than their gas phase counterparts for moderate
basis set and molecule surface discretization level. The relative cost of the
C-PCM solvation correction decreases as the basis sets and/or cavity radii
increase. Therefore description of solvation with this model should be routine.
We also discuss applications to the study of the conformational landscape of an
amyloid fibril.
