  Motivation: Surface generation and visualization are some of the most
important tasks in biomolecular modeling and computation. Eulerian solvent
excluded surface (ESES) software provides analytical solvent excluded surface
(SES) in the Cartesian grid, which is necessary for simulating many
biomolecular electrostatic and ion channel models. However, large biomolecules
and/or fine grid resolutions give rise to excessively large memory requirements
in ESES construction. We introduce an out-of-core and parallel algorithm to
improve the ESES software.
  Results: The present approach drastically improves the spatial and temporal
efficiency of ESES. The memory footprint and time complexity are analyzed and
empirically verified through extensive tests with a large collection of
biomolecule examples. Our results show that our algorithm can successfully
reduce memory footprint through a straightforward divide-and-conquer strategy
to perform the calculation of arbitrarily large proteins on a typical commodity
personal computer. On multi-core computers or clusters, our algorithm can
reduce the execution time by parallelizing most of the calculation as disjoint
subproblems. Various comparisons with the state-of-the-art Cartesian grid based
SES calculation were done to validate the present method and show the improved
efficiency. This approach makes ESES a robust software for the construction of
analytical solvent excluded surfaces.
  Availability and implementation: http://weilab.math.msu.edu/ESES.
