Computational biology contributes important solutions for major biological challenges. Unfortunately, most applications in computational biology are highly computeintensive and associated with extensive computing times. Biological problems of interest are often not treatable with traditional simulation models on conventional multi-core CPU systems. This interdisciplinary work introduces a new multi-timescale simulation model for apoptotic receptor-clustering and a new parallel evaluation algorithm that exploits the computational performance of heterogeneous CPU-GPU computing systems. For this purpose, the different dynamics involved in receptor-clustering are separated and simulated on two timescales. Additionally, the time step sizes are adaptively refined on each timescale independently.
This new approach improves the simulation performance significantly and reduces computing times from months to hours for observation times of several seconds.
%0 Conference Paper
%1 SchoeBDSW2014
%A Schöll, Alexander
%A Braun, Claus
%A Daub, Markus
%A Schneider, Guido
%A Wunderlich, Hans-Joachim
%B Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM'14)
%D 2014
%K Euler-Maruyama GPU SimTech adaptive aggregation approximation computing heterogeneous ligand-receptor-model multi-timescale myown parallel particle simulation
%P 424--431
%R http://dx.doi.org/10.1109/BIBM.2014.6999195
%T Adaptive Parallel Simulation of a Two-Timescale-Model for Apoptotic Receptor-Clustering on GPUs
%X Computational biology contributes important solutions for major biological challenges. Unfortunately, most applications in computational biology are highly computeintensive and associated with extensive computing times. Biological problems of interest are often not treatable with traditional simulation models on conventional multi-core CPU systems. This interdisciplinary work introduces a new multi-timescale simulation model for apoptotic receptor-clustering and a new parallel evaluation algorithm that exploits the computational performance of heterogeneous CPU-GPU computing systems. For this purpose, the different dynamics involved in receptor-clustering are separated and simulated on two timescales. Additionally, the time step sizes are adaptively refined on each timescale independently.
This new approach improves the simulation performance significantly and reduces computing times from months to hours for observation times of several seconds.
@inproceedings{SchoeBDSW2014,
abstract = {Computational biology contributes important solutions for major biological challenges. Unfortunately, most applications in computational biology are highly computeintensive and associated with extensive computing times. Biological problems of interest are often not treatable with traditional simulation models on conventional multi-core CPU systems. This interdisciplinary work introduces a new multi-timescale simulation model for apoptotic receptor-clustering and a new parallel evaluation algorithm that exploits the computational performance of heterogeneous CPU-GPU computing systems. For this purpose, the different dynamics involved in receptor-clustering are separated and simulated on two timescales. Additionally, the time step sizes are adaptively refined on each timescale independently.
This new approach improves the simulation performance significantly and reduces computing times from months to hours for observation times of several seconds.},
added-at = {2018-03-19T16:15:07.000+0100},
author = {Schöll, Alexander and Braun, Claus and Daub, Markus and Schneider, Guido and Wunderlich, Hans-Joachim},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2b9d42307aff55f949dce3efdc063ee86/clausbraun},
booktitle = {Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM'14)},
doi = {http://dx.doi.org/10.1109/BIBM.2014.6999195},
file = {http://www.iti.uni-stuttgart.de/fileadmin/rami/files/publications/2014/BIBM_SchoeBDSW2014.pdf},
interhash = {8b3950b9a31a28b554ce868d67598d14},
intrahash = {b9d42307aff55f949dce3efdc063ee86},
keywords = {Euler-Maruyama GPU SimTech adaptive aggregation approximation computing heterogeneous ligand-receptor-model multi-timescale myown parallel particle simulation},
pages = {424--431},
timestamp = {2018-03-19T15:21:25.000+0100},
title = {{Adaptive Parallel Simulation of a Two-Timescale-Model for Apoptotic Receptor-Clustering on GPUs}},
year = 2014
}
