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GRIMD: distributed computing for chemists and biologists
Motivation: Biologists and chemists are facing problems of high computational complexity that require the use of several computers organized in clusters or in specialized grids. Examples of such problems can be found in molecular dynamics (MD), in silico screening, and genome analysis. Grid Computin...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Biomedical Informatics
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3916819/ https://www.ncbi.nlm.nih.gov/pubmed/24516326 http://dx.doi.org/10.6026/97320630010043 |
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author | Piotto, Stefano Biasi, Luigi Di Concilio, Simona Castiglione, Aniello Cattaneo, Giuseppe |
author_facet | Piotto, Stefano Biasi, Luigi Di Concilio, Simona Castiglione, Aniello Cattaneo, Giuseppe |
author_sort | Piotto, Stefano |
collection | PubMed |
description | Motivation: Biologists and chemists are facing problems of high computational complexity that require the use of several computers organized in clusters or in specialized grids. Examples of such problems can be found in molecular dynamics (MD), in silico screening, and genome analysis. Grid Computing and Cloud Computing are becoming prevalent mainly because of their competitive performance/cost ratio. Regrettably, the diffusion of Grid Computing is strongly limited because two main limitations: it is confined to scientists with strong Computer Science background and the analyses of the large amount of data produced can be cumbersome it. We have developed a package named GRIMD to provide an easy and flexible implementation of distributed computing for the Bioinformatics community. GRIMD is very easy to install and maintain, and it does not require any specific Computer Science skill. Moreover, permits preliminary analysis on the distributed machines to reduce the amount of data to transfer. GRIMD is very flexible because it shields the typical computational biologist from the need to write specific code for tasks such as molecular dynamics or docking calculations. Furthermore, it permits an efficient use of GPU cards whenever is possible. GRIMD calculations scale almost linearly and, therefore, permits to exploit efficiently each machine in the network. Here, we provide few examples of grid computing in computational biology (MD and docking) and bioinformatics (proteome analysis). AVAILABILITY: GRIMD is available for free for noncommercial research at www.yadamp.unisa.it/grimd SUPPLEMENTARY INFORMATION: www.yadamp.unisa.it/grimd/howto.aspx |
format | Online Article Text |
id | pubmed-3916819 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Biomedical Informatics |
record_format | MEDLINE/PubMed |
spelling | pubmed-39168192014-02-10 GRIMD: distributed computing for chemists and biologists Piotto, Stefano Biasi, Luigi Di Concilio, Simona Castiglione, Aniello Cattaneo, Giuseppe Bioinformation Software Motivation: Biologists and chemists are facing problems of high computational complexity that require the use of several computers organized in clusters or in specialized grids. Examples of such problems can be found in molecular dynamics (MD), in silico screening, and genome analysis. Grid Computing and Cloud Computing are becoming prevalent mainly because of their competitive performance/cost ratio. Regrettably, the diffusion of Grid Computing is strongly limited because two main limitations: it is confined to scientists with strong Computer Science background and the analyses of the large amount of data produced can be cumbersome it. We have developed a package named GRIMD to provide an easy and flexible implementation of distributed computing for the Bioinformatics community. GRIMD is very easy to install and maintain, and it does not require any specific Computer Science skill. Moreover, permits preliminary analysis on the distributed machines to reduce the amount of data to transfer. GRIMD is very flexible because it shields the typical computational biologist from the need to write specific code for tasks such as molecular dynamics or docking calculations. Furthermore, it permits an efficient use of GPU cards whenever is possible. GRIMD calculations scale almost linearly and, therefore, permits to exploit efficiently each machine in the network. Here, we provide few examples of grid computing in computational biology (MD and docking) and bioinformatics (proteome analysis). AVAILABILITY: GRIMD is available for free for noncommercial research at www.yadamp.unisa.it/grimd SUPPLEMENTARY INFORMATION: www.yadamp.unisa.it/grimd/howto.aspx Biomedical Informatics 2014-01-29 /pmc/articles/PMC3916819/ /pubmed/24516326 http://dx.doi.org/10.6026/97320630010043 Text en © 2014 Biomedical Informatics This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited. |
spellingShingle | Software Piotto, Stefano Biasi, Luigi Di Concilio, Simona Castiglione, Aniello Cattaneo, Giuseppe GRIMD: distributed computing for chemists and biologists |
title | GRIMD: distributed computing for chemists and biologists |
title_full | GRIMD: distributed computing for chemists and biologists |
title_fullStr | GRIMD: distributed computing for chemists and biologists |
title_full_unstemmed | GRIMD: distributed computing for chemists and biologists |
title_short | GRIMD: distributed computing for chemists and biologists |
title_sort | grimd: distributed computing for chemists and biologists |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3916819/ https://www.ncbi.nlm.nih.gov/pubmed/24516326 http://dx.doi.org/10.6026/97320630010043 |
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