  Due to the very long timescales involved (us-s), theoretical modeling of
fundamental biological processes including folding, misfolding, and mechanical
unraveling of biomolecules, under physiologically relevant conditions, is
challenging even for distributed computing systems. Graphics Processing Units
(GPUs) are emerging as an alternative programming platform to the more
traditional CPUs as they provide high raw computational power that can be
utilized in a wide range of scientific applications. Using a coarse-grained
Self Organized Polymer (SOP) model, we have developed and tested the GPU-based
implementation of Langevin simulations for proteins (SOP-GPU program).
Simultaneous calculation of forces for all particles is implemented using
either the particle based or the interacting pair based parallelization, which
leads to a ~30-fold acceleration compared to an optimized CPU version of the
program. We assess the computational performance of an end-to-end application
of the SOP-GPU program, where all steps of the algorithm are running on the
GPU, by profiling the associated simulation time and memory usage for a number
of small proteins, long protein fibers, and large-size protein assemblies. The
SOP-GPU package can now be used in the theoretical exploration of the
mechanical properties of large-size protein systems to generate the
force-extension and force-indentation profiles under the experimental
conditions of force application, and to relate the results of single-molecule
experiments in vitro and in silico.
