Volume 25, Issue 4
DASHMM Accelerated Adaptive Fast Multipole Poisson-Boltzmann Solver on Distributed Memory Architecture

Bo Zhang, Jackson DeBuhr, Drake Niedzielski, Silvio Mayolo, Benzhuo Lu & Thomas Sterling

Commun. Comput. Phys., 25 (2019), pp. 1235-1258.

Published online: 2018-12

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  • Abstract

We present DAFMPB (DASHMM-accelerated Adaptive Fast Multipole Poisson-Boltzmann solver) for rapid evaluation of the electrostatic potentials and forces, and total solvation-free energy in biomolecular systems modeled by the linearized Poisson-Boltzmann (LPB) equation. DAFMPB first reformulates the LPB into a boundary integral equation and then discretizes it using the node-patch scheme [33]. It solves the resulting linear system using GMRES, where it adopts the DASHMM library [14] to accelerate the matrix-vector multiplication in each iteration. DASHMM is built on top of a global address space allowing the user of DAFMPB to operate on both shared and distributed memory computers with modification of their code. This paper is a brief summary of the program, including the algorithm, implementation, installation and usage.

  • Keywords

Poisson-Boltzmann equation, boundary element method, DASHMM, distributed computing.

  • AMS Subject Headings

45B05, 31C20, 92C05, 68N19

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{CiCP-25-1235, author = {}, title = {DASHMM Accelerated Adaptive Fast Multipole Poisson-Boltzmann Solver on Distributed Memory Architecture}, journal = {Communications in Computational Physics}, year = {2018}, volume = {25}, number = {4}, pages = {1235--1258}, abstract = {

We present DAFMPB (DASHMM-accelerated Adaptive Fast Multipole Poisson-Boltzmann solver) for rapid evaluation of the electrostatic potentials and forces, and total solvation-free energy in biomolecular systems modeled by the linearized Poisson-Boltzmann (LPB) equation. DAFMPB first reformulates the LPB into a boundary integral equation and then discretizes it using the node-patch scheme [33]. It solves the resulting linear system using GMRES, where it adopts the DASHMM library [14] to accelerate the matrix-vector multiplication in each iteration. DASHMM is built on top of a global address space allowing the user of DAFMPB to operate on both shared and distributed memory computers with modification of their code. This paper is a brief summary of the program, including the algorithm, implementation, installation and usage.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.OA-2018-0098}, url = {http://global-sci.org/intro/article_detail/cicp/12897.html} }
TY - JOUR T1 - DASHMM Accelerated Adaptive Fast Multipole Poisson-Boltzmann Solver on Distributed Memory Architecture JO - Communications in Computational Physics VL - 4 SP - 1235 EP - 1258 PY - 2018 DA - 2018/12 SN - 25 DO - http://doi.org/10.4208/cicp.OA-2018-0098 UR - https://global-sci.org/intro/article_detail/cicp/12897.html KW - Poisson-Boltzmann equation, boundary element method, DASHMM, distributed computing. AB -

We present DAFMPB (DASHMM-accelerated Adaptive Fast Multipole Poisson-Boltzmann solver) for rapid evaluation of the electrostatic potentials and forces, and total solvation-free energy in biomolecular systems modeled by the linearized Poisson-Boltzmann (LPB) equation. DAFMPB first reformulates the LPB into a boundary integral equation and then discretizes it using the node-patch scheme [33]. It solves the resulting linear system using GMRES, where it adopts the DASHMM library [14] to accelerate the matrix-vector multiplication in each iteration. DASHMM is built on top of a global address space allowing the user of DAFMPB to operate on both shared and distributed memory computers with modification of their code. This paper is a brief summary of the program, including the algorithm, implementation, installation and usage.

Bo Zhang, Jackson DeBuhr, Drake Niedzielski, Silvio Mayolo, Benzhuo Lu & Thomas Sterling. (2020). DASHMM Accelerated Adaptive Fast Multipole Poisson-Boltzmann Solver on Distributed Memory Architecture. Communications in Computational Physics. 25 (4). 1235-1258. doi:10.4208/cicp.OA-2018-0098
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