Out-of-core solution of eigenproblems for macromolecular simulations

We consider the solution of large-scale eigenvalue problems that appear in the motion simulation of complex macromolecules on desktop platforms. To tackle the dimension of the matrices that are involved in these problems, we formulate out-of-core (OOC) variants of the two selected eigensolvers, that basically decouple the performance of the solver from the storage capacity. Furthermore, we contend with the high computational complexity of the solvers by off-loading the arithmetically-intensive parts of the algorithms to a hardware graphics accelerator

Out-of-core macromolecular simulations on multithreaded architectures

We address the solution of large-scale eigenvalue problems that appear in the motion simulation of complex macromolecules on multithreaded platforms, consisting of multicore processors and possibly a graphics processor (GPU). In particular, we compare specialized implementations of several high- performance eigensolvers that, by relying on disk storage and out-of-core (OOC) techniques, can in principle tackle the large memory requirements of these biological problems, which in general do not fit into the main memory of current desktop machines. All these OOC eigensolvers, except for one, are composed of compute-bound (i.e., arithmetically-intensive) operations, which we accelerate by exploiting the performance of current multicore processors and, in some cases, by additionally off-loading certain parts of the computation to a GPU accelerator. One of the eigensolvers is a memory-bound algorithm, which strongly constrains its performance when the data is on disk. However, this method exhibits a much lower arithmetic cost compared with its compute- bound alternatives for this particular application. Experimental results on a desktop platform, representative of current server technology, illustrate the potential of these methods to address the simulation of biological activity

NOLB: Nonlinear Rigid Block Normal Mode Analysis Method

International audienceWe present a new conceptually simple and computationally efficient method for nonlinear normal mode analysis called NOLB. It relies on the rotations-translations of blocks (RTB) theoretical basis developed by Y.-H. Sanejouand and colleagues. We demonstrate how to physically interpret the eigenvalues computed in the RTB basis in terms of angular and linear velocities applied to the rigid blocks and how to construct a nonlinear extrapolation of motion out of these velocities. The key observation of our method is that the angular velocity of a rigid block can be interpreted as the result of an implicit force, such that the motion of the rigid block can be considered as a pure rotation about a certain center. We demonstrate the motions produced with the NOLB method on three different molecular systems and show that some of the lowest frequency normal modes correspond to the biologically relevant motions. For example, NOLB detects the spiral sliding motion of the TALE protein, which is capable of rapid diffusion along its target DNA. Overall, our method produces better structures compared to the standard approach, especially at large deformation amplitudes, as we demonstrate by visual inspection, energy and topology analyses, and also by the MolProbity service validation. Finally, our method is scalable and can be applied to very large molecular systems, such as ribosomes. Standalone executables of the NOLB normal mode analysis method are available at https://team.inria.fr/nano-d/software/nolb-normal-modes/. A graphical user interface created for the SAMSON software platform will be made available at https: //www.samson-connect.net

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