Performance and scaling behavior of bioinformatic applications in virtualization environments to create awareness for the efficient use of compute resources

The large amount of biological data available in the current times, makes it necessary to use tools and applications based on sophisticated and efficient algorithms, developed in the area of bioinformatics. Further, access to high performance computing resources is necessary, to achieve results in r...

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Vydáno v:PLoS computational biology Ročník 17; číslo 7; s. e1009244
Hlavní autoři: Hanussek, Maximilian, Bartusch, Felix, Krüger, Jens
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 01.07.2021
Public Library of Science (PLoS)
Témata:
Algorithms
Application programming interface
Benchmarking
Bioinformatics
Biological research
Biology, Experimental
Cloud Computing
Computational biology
Computational Biology - methods
Computational Biology - standards
Computational Biology - statistics & numerical data
Computer and Information Sciences
Computer programs
Computers
Computing Methodologies
Data Interpretation, Statistical
Databases, Factual - statistics & numerical data
Engineering and Technology
High performance computing
High-Throughput Nucleotide Sequencing
Humans
Image Interpretation, Computer-Assisted
Learning algorithms
Linux
Machine Learning
Medical imaging
Operating systems
Parallel processing
Photographic developers
Physical Sciences
Research and Analysis Methods
Scaling
Sequence Alignment
Software
Software development
Technology application
User-Computer Interface
Virtual environments
Virtualization
Germany
ISSN:1553-7358, 1553-734X, 1553-7358
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Shrnutí:The large amount of biological data available in the current times, makes it necessary to use tools and applications based on sophisticated and efficient algorithms, developed in the area of bioinformatics. Further, access to high performance computing resources is necessary, to achieve results in reasonable time. To speed up applications and utilize available compute resources as efficient as possible, software developers make use of parallelization mechanisms, like multithreading. Many of the available tools in bioinformatics offer multithreading capabilities, but more compute power is not always helpful. In this study we investigated the behavior of well-known applications in bioinformatics, regarding their performance in the terms of scaling, different virtual environments and different datasets with our benchmarking tool suite BOOTABLE . The tool suite includes the tools BBMap, Bowtie2, BWA, Velvet, IDBA, SPAdes, Clustal Omega, MAFFT, SINA and GROMACS. In addition we added an application using the machine learning framework TensorFlow. Machine learning is not directly part of bioinformatics but applied to many biological problems, especially in the context of medical images (X-ray photographs). The mentioned tools have been analyzed in two different virtual environments, a virtual machine environment based on the OpenStack cloud software and in a Docker environment. The gained performance values were compared to a bare-metal setup and among each other. The study reveals, that the used virtual environments produce an overhead in the range of seven to twenty-five percent compared to the bare-metal environment. The scaling measurements showed, that some of the analyzed tools do not benefit from using larger amounts of computing resources, whereas others showed an almost linear scaling behavior. The findings of this study have been generalized as far as possible and should help users to find the best amount of resources for their analysis. Further, the results provide valuable information for resource providers to handle their resources as efficiently as possible and raise the user community’s awareness of the efficient usage of computing resources.
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The authors have declared that no competing interests exist.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1009244