A fast band–Krylov eigensolver for macromolecular functional motion simulation on multicore architectures and graphics processors

Aliaga, José I.; Alonso, Pedro; Badía, Jordi; Chacón, Pablo; Davidović, Davor; López‐Blanco, José Ramón; Quintana–Ort́ı, Enrique S.

doi:10.1016/j.jcp.2016.01.007

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…The details of our NMA framework in internal coordinates were described previously − and are similar to those implemented in ref . Briefly, the internal mobile coordinates are defined by the canonical backbone dihedral angles, while the remaining dihedral angles and all covalent bond lengths and angles are fixed.…”

Section: Methodsmentioning

confidence: 99%

Local Normal Mode Analysis for Fast Loop Conformational Sampling

López‐Blanco

Dehouck

Bastolla

et al. 2022

J. Chem. Inf. Model.

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We propose and validate a novel method to efficiently explore local protein loop conformations based on a new formalism for constrained normal mode analysis (NMA) in internal coordinates. The manifold of possible loop configurations imposed by the position and orientation of the fixed loop ends is reduced to an orthogonal set of motions (or modes) encoding concerted rotations of all the backbone dihedral angles. We validate the sampling power on a set of protein loops with highly variable experimental structures and demonstrate that our approach can efficiently explore the conformational space of closed loops. We also show an acceptable resemblance of the ensembles around equilibrium conformations generated by long molecular simulations and constrained NMA on a set of exposed and diverse loops. In comparison with other methods, the main advantage is the lack of restrictions on the number of dihedrals that can be altered simultaneously. Furthermore, the method is computationally efficient since it only requires the diagonalization of a tiny matrix, and the modes of motions are energetically contextualized by the elastic network model, which includes both the loop and the neighboring residues.

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Section: Methodsmentioning

confidence: 99%

Local Normal Mode Analysis for Fast Loop Conformational Sampling

López‐Blanco

Dehouck

Bastolla

et al. 2022

J. Chem. Inf. Model.

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“…Several authors have reported about GPU implementation of Krylov eigensolvers. For instance, Aliaga et al [8] accelerates Krylov methods by off-loading the matrix-vector products to the GPU, after carrying out a band reduction of the matrix (and losing the sparse character). This work is restricted to computing exterior eigenvalues of symmetric matrices, while our main concern is interior eigenvalues of non-symmetric block-tridiagonal matrices.…”

Section: Introductionmentioning

confidence: 99%

MPI-CUDA parallel linear solvers for block-tridiagonal matrices in the context of SLEPc’s eigensolvers

Daviña

Román

2018

Parallel Computing

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We consider the computation of a few eigenpairs of a generalized eigenvalue problem Ax = λBx with block-tridiagonal matrices, not necessarily symmetric, in the context of Krylov methods. In this kind of computation, it is often necessary to solve a linear system of equations in each iteration of the eigensolver, for instance when B is not the identity matrix or when computing interior eigenvalues with the shift-and-invert spectral transformation. In this work, we aim to compare different direct linear solvers that can exploit the block-tridiagonal structure. Block cyclic reduction and the Spike algorithm are considered. A parallel implementation based on MPI is developed in the context of the SLEPc library. The use of GPU devices to accelerate local computations shows to be competitive for large block sizes.

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Look-ahead in the two-sided reduction to compact band forms for symmetric eigenvalue problems and the SVD

et al. 2018

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We address the reduction to compact band forms, via unitary similarity transformations, for the solution of symmetric eigenvalue problems and the computation of the singular value decomposition (SVD). Concretely, in the first case we revisit the reduction to symmetric band form while, for the second case, we propose a similar alternative, which transforms the original matrix to (unsymmetric) band form, replacing the conventional reduction method that produces a triangular-band output. In both cases, we describe algorithmic variants of the standard Level-3 BLAS-based procedures, enhanced with look-ahead, to overcome the performance bottleneck imposed by the panel factorization. Furthermore, our solutions employ an algorithmic block size that differs from the target bandwidth, illustrating the important performance benefits of this decision. Finally, we show that our alternative compact band form for the SVD is key to introduce an effective look-ahead strategy into the corresponding reduction procedure.

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A fast band–Krylov eigensolver for macromolecular functional motion simulation on multicore architectures and graphics processors

Cited by 3 publications

References 25 publications

Local Normal Mode Analysis for Fast Loop Conformational Sampling

Local Normal Mode Analysis for Fast Loop Conformational Sampling

MPI-CUDA parallel linear solvers for block-tridiagonal matrices in the context of SLEPc’s eigensolvers

Look-ahead in the two-sided reduction to compact band forms for symmetric eigenvalue problems and the SVD

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