Exploring large macromolecular functional motions on clusters of multicore processors
Normal modes in internal coordinates (IC) furnish an excellent way to model functional collective motions of macromolecular machines, but exhibit a high computational cost when applied to large-sized macromolecules. In this paper, we significantly extend the applicability of this approach towards much larger systems by effectively solving the computational bottleneck of these methods, the diagonalization step and associated large-scale eigenproblem, on a small cluster of nodes equipped with multicore technology. Our experiments show the superior performance of iterative Krylov-subspace methods for the solution of the dense generalized eigenproblems arising in these biological applications over more traditional direct solvers implemented on top of state-of-the-art libraries. The presented approach expedites the study of the collective conformational changes of large macromolecules opening a new window for exploring the functional motions of such relevant systems.
clusters of multicore processors normal modes macromolecular machines krylov-subspace method computational biology eigenvalue problems