Accurate and efficient constrained molecular dynamics of polymers using Newton's method and special purpose codeShow others and affiliations
2023 (English)In: Computer Physics Communications, ISSN 0010-4655, E-ISSN 1879-2944, Vol. 288, article id 108742Article in journal (Refereed) Published
Abstract [en]
In molecular dynamics simulations we can often increase the time step by imposing constraints on bond lengths and bond angles. This allows us to extend the length of the time interval and therefore the range of physical phenomena that we can afford to simulate. We examine the existing algorithms and software for solving nonlinear constraint equations in parallel and we explain why it is necessary to advance the state-of-the-art. We present ILVES-PC, a new algorithm for imposing bond constraints on proteins accurately and efficiently. It solves the same system of differential algebraic equations as the celebrated SHAKE algorithm, but ILVES-PC solves the nonlinear constraint equations using Newton’s method rather than the nonlinear Gauss-Seidel method. Moreover, ILVES-PC solves the necessary linear systems using a specialized linear solver that exploits the structure of the protein. ILVES-PC can rapidly solve constraint equations as accurately as the hardware will allow. The run-time of ILVES-PC is proportional to the number of constraints. We have integrated ILVES-PC into GROMACS and simulated proteins of different sizes. Compared with SHAKE, we have achieved speedups of up to 4.9× in single-threaded executions and up to 76× in shared-memory multi-threaded executions. Moreover, ILVES-PC is more accurate than P-LINCS algorithm. Our work is a proof-of-concept of the utility of software designed specifically for the simulation of polymers.
Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 288, article id 108742
Keywords [en]
molecular dynamics, constraint algorithms, nonlinear equations, newton's method, SHAKE, LINCS
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:umu:diva-208209DOI: 10.1016/j.cpc.2023.108742Scopus ID: 2-s2.0-85151493578OAI: oai:DiVA.org:umu-208209DiVA, id: diva2:1756341
Funder
Swedish Research CouncileSSENCE - An eScience Collaboration2023-05-112023-05-112023-05-11Bibliographically approved