Accelerating the Gillespie τ-Leaping Method Using Graphics Processing Units

The Gillespie τ-Leaping Method is an approximate algorithm that is faster than the exact Direct Method (DM) due to the progression of the simulation with larger time steps. However, the procedure to compute the time leap τ is quite expensive. In this paper, we explore the acceleration of the τ-Leapi...

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Detalles Bibliográficos
Autores principales: Komarov, Ivan, D’Souza, Roshan M., Tapia, Jose-Juan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3371023/
https://www.ncbi.nlm.nih.gov/pubmed/22715366
http://dx.doi.org/10.1371/journal.pone.0037370
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author Komarov, Ivan
D’Souza, Roshan M.
Tapia, Jose-Juan
author_facet Komarov, Ivan
D’Souza, Roshan M.
Tapia, Jose-Juan
author_sort Komarov, Ivan
collection PubMed
description The Gillespie τ-Leaping Method is an approximate algorithm that is faster than the exact Direct Method (DM) due to the progression of the simulation with larger time steps. However, the procedure to compute the time leap τ is quite expensive. In this paper, we explore the acceleration of the τ-Leaping Method using Graphics Processing Unit (GPUs) for ultra-large networks ([Image: see text] reaction channels). We have developed data structures and algorithms that take advantage of the unique hardware architecture and available libraries. Our results show that we obtain a performance gain of over 60x when compared with the best conventional implementations.
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spelling pubmed-33710232012-06-19 Accelerating the Gillespie τ-Leaping Method Using Graphics Processing Units Komarov, Ivan D’Souza, Roshan M. Tapia, Jose-Juan PLoS One Research Article The Gillespie τ-Leaping Method is an approximate algorithm that is faster than the exact Direct Method (DM) due to the progression of the simulation with larger time steps. However, the procedure to compute the time leap τ is quite expensive. In this paper, we explore the acceleration of the τ-Leaping Method using Graphics Processing Unit (GPUs) for ultra-large networks ([Image: see text] reaction channels). We have developed data structures and algorithms that take advantage of the unique hardware architecture and available libraries. Our results show that we obtain a performance gain of over 60x when compared with the best conventional implementations. Public Library of Science 2012-06-08 /pmc/articles/PMC3371023/ /pubmed/22715366 http://dx.doi.org/10.1371/journal.pone.0037370 Text en Komarov et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Komarov, Ivan
D’Souza, Roshan M.
Tapia, Jose-Juan
Accelerating the Gillespie τ-Leaping Method Using Graphics Processing Units
title Accelerating the Gillespie τ-Leaping Method Using Graphics Processing Units
title_full Accelerating the Gillespie τ-Leaping Method Using Graphics Processing Units
title_fullStr Accelerating the Gillespie τ-Leaping Method Using Graphics Processing Units
title_full_unstemmed Accelerating the Gillespie τ-Leaping Method Using Graphics Processing Units
title_short Accelerating the Gillespie τ-Leaping Method Using Graphics Processing Units
title_sort accelerating the gillespie τ-leaping method using graphics processing units
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3371023/
https://www.ncbi.nlm.nih.gov/pubmed/22715366
http://dx.doi.org/10.1371/journal.pone.0037370
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