GROMEX: A Scalable and Versatile Fast Multipole Method for Biomolecular Simulation
| Autor: | Bartosz Kohnke, Gerrit Groenhof, Andreas Beckmann, Holger Dachsel, Plamen Dobrev, Thomas Ullmann, Helmut Grubmüller, David Haensel, Carsten Kutzner, Ivo Kabadshow, Berk Hess, Laura Morgenstern |
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| Přispěvatelé: | Bungartz, H, Reiz, S, Uekermann, B, Neumann, P, Nagel, WE |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Fast multipole method 05 social sciences Fast Fourier transform 050301 education Supercomputer Electrostatics biomolekyylit Computational science Molecular dynamics CUDA sähköstatiikka Particle Mesh Scalability Overhead (computing) simulointi 0501 psychology and cognitive sciences SIMD 0503 education 050104 developmental & child psychology |
| Zdroj: | Software for Exascale Computing-SPPEXA 2016-2019 ISBN: 9783030479558 Software for Exascale Computing Software for Exascale Computing-SPPEXA 2016-2019 Lecture Notes in Computational Science and Engineering Cham : Springer International Publishing, Lecture Notes in Computational Science and Engineering 517-543 (2020). |
| Popis: | Atomistic simulations of large biomolecular systems with chemical variability such as constant pH dynamic protonation offer multiple challenges in high performance computing. One of them is the correct treatment of the involved electrostatics in an efficient and highly scalable way. Here we review and assess two of the main building blocks that will permit such simulations: (1) An electrostatics library based on the Fast Multipole Method (FMM) that treats local alternative charge distributions with minimal overhead, and (2) A $λ$-dynamics module working in tandem with the FMM that enables various types of chemical transitions during the simulation. Our $λ$-dynamics and FMM implementations do not rely on third-party libraries but are exclusively using C++ language features and they are tailored to the specific requirements of molecular dynamics simulation suites such as GROMACS. The FMM library supports fractional tree depths and allows for rigorous error control and automatic performance optimization at runtime. Near-optimal performance is achieved on various SIMD architectures and on GPUs using CUDA. For exascale systems, we expect our approach to outperform current implementations based on Particle Mesh Ewald (PME) electrostatics, because FMM avoids the communication bottlenecks caused by the parallel fast Fourier transformations needed for PME. peerReviewed |
| Databáze: | OpenAIRE |
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