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An investigation of the impact of bi...
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Dobosz, Rafal.
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An investigation of the impact of big data on bioinformatics software.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
An investigation of the impact of big data on bioinformatics software./
作者:
Dobosz, Rafal.
面頁冊數:
126 p.
附註:
Source: Masters Abstracts International, Volume: 52-03.
Contained By:
Masters Abstracts International52-03(E).
標題:
Biology, Bioinformatics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR87329
ISBN:
9780494873298
An investigation of the impact of big data on bioinformatics software.
Dobosz, Rafal.
An investigation of the impact of big data on bioinformatics software.
- 126 p.
Source: Masters Abstracts International, Volume: 52-03.
Thesis (M.S.)--Trent University (Canada), 2013.
As the generation of genetic data accelerates, Big Data has an increasing impact on the way bioinformatics software is used. The experiments become larger and more complex than originally envisioned by software designers. One way to deal with this problem is to use parallel computing. Using the program Structure as a case study, we investigate ways in which to counteract the challenges created by the growing datasets. We propose an OpenMP and an OpenMP-MPI hybrid parallelization of the MCMC steps, and analyse the performance in various scenarios. The results indicate that the parallelizations produce significant speedups over the serial version in all scenarios tested. This allows for using the available hardware more efficiently, by adapting the program to the parallel architecture. This is important because not only does it reduce the time required to perform existing analyses, but it also opens the door to new analyses, which were previously impractical.
ISBN: 9780494873298Subjects--Topical Terms:
1018415
Biology, Bioinformatics.
An investigation of the impact of big data on bioinformatics software.
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As the generation of genetic data accelerates, Big Data has an increasing impact on the way bioinformatics software is used. The experiments become larger and more complex than originally envisioned by software designers. One way to deal with this problem is to use parallel computing. Using the program Structure as a case study, we investigate ways in which to counteract the challenges created by the growing datasets. We propose an OpenMP and an OpenMP-MPI hybrid parallelization of the MCMC steps, and analyse the performance in various scenarios. The results indicate that the parallelizations produce significant speedups over the serial version in all scenarios tested. This allows for using the available hardware more efficiently, by adapting the program to the parallel architecture. This is important because not only does it reduce the time required to perform existing analyses, but it also opens the door to new analyses, which were previously impractical.
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