# Difference between revisions of "Dominic Röhm"

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In coarse-grained Molecular dynamics (MD) simulations of large macromolecules, the number of solvent molecules is normally so large that most of the computation time is spent on the solvent. For this reason one is interested in replacing the solvent by a lattice fluid using the Lattice-Boltzmann (LB) method. The LB method is well known and on large length and | In coarse-grained Molecular dynamics (MD) simulations of large macromolecules, the number of solvent molecules is normally so large that most of the computation time is spent on the solvent. For this reason one is interested in replacing the solvent by a lattice fluid using the Lattice-Boltzmann (LB) method. The LB method is well known and on large length and | ||

timescales it leads to a hydrodynamic ﬂow ﬁeld that satisﬁes the Navier-Stokes equation. If the lattice fluid should be coupled to a conventional MD simulation of the coarse-grained particles, it is necessary to thermalize the fluid. While the MD particles are easily coupled via friction terms to the fluid, the correct thermalization of the lattice fluid requires to switch into mode space, which makes thermalized LB more complex and computationally expensive. | timescales it leads to a hydrodynamic ﬂow ﬁeld that satisﬁes the Navier-Stokes equation. If the lattice fluid should be coupled to a conventional MD simulation of the coarse-grained particles, it is necessary to thermalize the fluid. While the MD particles are easily coupled via friction terms to the fluid, the correct thermalization of the lattice fluid requires to switch into mode space, which makes thermalized LB more complex and computationally expensive. | ||

− | However, the LB method is particularly well suited for the highly parallel architecture of graphics processors (GPUs). I am working on a fully thermalized GPU-LB implementation which is coupled to a MD that is running on a conventional CPU using the simulation package ESPResSo [http://www.espressomd.org]. This implementation is on a single NVIDIA GTX480 about 50 times faster than on a recent AMD Athlon IIX4 quadcore, therefore replacing a full compute rack by a single desktop PC with a highend graphics card. | + | However, the LB method is particularly well suited for the highly parallel architecture of graphics processors (GPUs). I am working on a fully thermalized GPU-LB implementation which is coupled to a MD that is running on a conventional CPU using the simulation package ESPResSo [http://www.espressomd.org]. This implementation is on a single NVIDIA GTX480 or C2050 about 50 times faster than on a recent AMD Athlon IIX4 quadcore, therefore replacing a full compute rack by a single desktop PC with a highend graphics card. |

== Publications == | == Publications == |

## Revision as of 11:42, 17 December 2010

**Dominic Röhm**

Diploma student

Office: | 210 |
---|---|

Phone: | +49 711 685-63594 |

Fax: | +49 711 685-63658 |

Email: | Dominic.Roehm _at_ icp.uni-stuttgart.de |

Address: | Dominic Röhm Institute for Computational Physics Universität Stuttgart Allmandring 3 70569 Stuttgart Germany |

## Diploma Thesis

### Lattice-Boltzmann-Simulations on GPUs

In coarse-grained Molecular dynamics (MD) simulations of large macromolecules, the number of solvent molecules is normally so large that most of the computation time is spent on the solvent. For this reason one is interested in replacing the solvent by a lattice fluid using the Lattice-Boltzmann (LB) method. The LB method is well known and on large length and timescales it leads to a hydrodynamic ﬂow ﬁeld that satisﬁes the Navier-Stokes equation. If the lattice fluid should be coupled to a conventional MD simulation of the coarse-grained particles, it is necessary to thermalize the fluid. While the MD particles are easily coupled via friction terms to the fluid, the correct thermalization of the lattice fluid requires to switch into mode space, which makes thermalized LB more complex and computationally expensive. However, the LB method is particularly well suited for the highly parallel architecture of graphics processors (GPUs). I am working on a fully thermalized GPU-LB implementation which is coupled to a MD that is running on a conventional CPU using the simulation package ESPResSo [1]. This implementation is on a single NVIDIA GTX480 or C2050 about 50 times faster than on a recent AMD Athlon IIX4 quadcore, therefore replacing a full compute rack by a single desktop PC with a highend graphics card.

## Publications

## Curriculum vitae

### Scientific education

Oct. 2005 - ... (May. 2011) | Studies of Physics at the University of Stuttgart |