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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 <jats:italic>BOOTABLE<\/jats:italic>. 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\u2019s awareness of the efficient usage of computing resources.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009244","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T17:56:07Z","timestamp":1626803767000},"page":"e1009244","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":11,"title":["Performance and scaling behavior of bioinformatic applications in virtualization environments to create awareness for the efficient use of compute resources"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2598-4398","authenticated-orcid":true,"given":"Maximilian","family":"Hanussek","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0711-5196","authenticated-orcid":true,"given":"Felix","family":"Bartusch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2636-3163","authenticated-orcid":true,"given":"Jens","family":"Kr\u00fcger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2021,7,20]]},"reference":[{"key":"pcbi.1009244.ref001","unstructured":"Bader DA, Li Y, Li T. BioPerf: A benchmark suite to evaluate high-performance computer architecture on bioinformatics applications. In: Proceedings of the 2005 IEEE International Symposium on Workload Characterization, IISWC-2005; 2005."},{"key":"pcbi.1009244.ref002","unstructured":"Amazon. Amazon Elastic Compute Cloud (Amazon EC2); 2008. https:\/\/aws.amazon.com."},{"key":"pcbi.1009244.ref003","unstructured":"Google. Google Cloud Computing, Hosting Services & APIs; 2017. https:\/\/cloud.google.com."},{"key":"pcbi.1009244.ref004","unstructured":"Microsoft. Microsoft Azure Cloud Computing Platform; Services; 2019. https:\/\/azure.microsoft.com."},{"issue":"2","key":"pcbi.1009244.ref005","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1093\/bib\/bbx040","article-title":"Bioinformatics in Germany: toward a national-level infrastructure","volume":"20","author":"A Tauch","year":"2019","journal-title":"Briefings in Bioinformatics"},{"key":"pcbi.1009244.ref006","volume-title":"Kooperation von Rechenzentren","author":"JC Schulz","year":"2016"},{"issue":"3","key":"pcbi.1009244.ref007","first-page":"032067","article-title":"Helix Nebula and CERN: A Symbiotic approach to exploiting commercial clouds","volume":"513","author":"FHB Megino","year":"2014","journal-title":"Journal of Physics: Conference Series"},{"key":"pcbi.1009244.ref008","author":"AB Bondi","year":"2000","journal-title":"Characteristics of scalability and their impact on performance"},{"key":"pcbi.1009244.ref009","article-title":"Tight approximability results for test set problems in bioinformatics","author":"P Berman","year":"2005","journal-title":"Journal of Computer and System Sciences"},{"key":"pcbi.1009244.ref010","author":"DA Bader","year":"2004","journal-title":"Computational biology and high-performance computing"},{"key":"pcbi.1009244.ref011","article-title":"MapReduce: Simplified data processing on large clusters","author":"J Dean","year":"2008","journal-title":"Communications of the ACM"},{"key":"pcbi.1009244.ref012","article-title":"Spark: Cluster computing with working sets","author":"M Zaharia","year":"2010","journal-title":"HotCloud"},{"key":"pcbi.1009244.ref013","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.csbj.2017.07.002","article-title":"Scalability and Validation of Big Data Bioinformatics Software","volume":"15","author":"A Yang","year":"2017","journal-title":"Computational and Structural Biotechnology Journal"},{"key":"pcbi.1009244.ref014","volume-title":"Programming with POSIX threads","author":"DR Butenhof","year":"1997"},{"key":"pcbi.1009244.ref015","unstructured":"Shah S, Bull M. OpenMP. In: Proceedings of the 2006 ACM\/IEEE Conference on Supercomputing, SC\u201906; 2006."},{"key":"pcbi.1009244.ref016","article-title":"Parallel protein secondary structure prediction schemes using Pthread and OpenMP over hyper-threading technology","author":"W Zhong","year":"2007","journal-title":"Journal of Supercomputing"},{"key":"pcbi.1009244.ref017","doi-asserted-by":"crossref","unstructured":"Morabito R, Kj\u00e4llman J, Komu M. Hypervisors vs. lightweight virtualization: A performance comparison. In: Proceedings\u20142015 IEEE International Conference on Cloud Engineering, IC2E 2015; 2015.","DOI":"10.1109\/IC2E.2015.74"},{"key":"pcbi.1009244.ref018","unstructured":"VMware. Understanding Full Virtualization, ParaVirtualization, and Hardware Assist; 2007. https:\/\/www.vmware.com\/content\/dam\/digitalmarketing\/vmware\/en\/pdf\/techpaper\/VMware_paravirtualization.pdf."},{"key":"pcbi.1009244.ref019","volume-title":"Operating Systems Review (ACM)","author":"P Barham","year":"2003"},{"key":"pcbi.1009244.ref020","volume-title":"Practical Linux Infrastructure","author":"S Ali","year":"2015"},{"key":"pcbi.1009244.ref021","unstructured":"OpenStack; Accessed 27 Jan 2021. https:\/\/www.openstack.org."},{"key":"pcbi.1009244.ref022","doi-asserted-by":"crossref","unstructured":"Estrada ZJ, Stephens Z, Pham C. A performance evaluation of sequence alignment software in virtualized environments. In: Proceedings\u201414th IEEE\/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2014; 2014.","DOI":"10.1109\/CCGrid.2014.125"},{"key":"pcbi.1009244.ref023","unstructured":"Zabaljauregui M. Hardware Assisted Virtualization Intel Virtualization Technology. Unpublished Student Thesis. 2008;."},{"key":"pcbi.1009244.ref024","unstructured":"Advanced Micro Devices Inc. AMD-V Nested Paging; 2008. http:\/\/developer.amd.com\/wordpress\/media\/2012\/10\/NPT-WP-1"},{"key":"pcbi.1009244.ref025","unstructured":"Anderson C. Docker. 2015;"},{"key":"pcbi.1009244.ref026","article-title":"Singularity: Scientific containers for mobility of compute","author":"GM Kurtzer","year":"2017","journal-title":"PLoS ONE"},{"key":"pcbi.1009244.ref027","volume-title":"Aging","author":"J Turnbull","year":"2014"},{"key":"pcbi.1009244.ref028","article-title":"Analysis of Docker Security","author":"T Bui","year":"2015","journal-title":"CoRR"},{"key":"pcbi.1009244.ref029","doi-asserted-by":"crossref","unstructured":"Bartusch F, Hanussek M, Kr\u00fcger J. Reproducible Scientific Workflows for High Performance and Cloud Computing. In: 2019 19th IEEE\/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID); 2019. p. 161\u2013164.","DOI":"10.1109\/CCGRID.2019.00028"},{"key":"pcbi.1009244.ref030","article-title":"Comparative analysis of de novo transcriptome assembly","author":"K Clarke","year":"2013","journal-title":"Science China Life Sciences"},{"key":"pcbi.1009244.ref031","doi-asserted-by":"crossref","DOI":"10.1186\/1471-2164-14-328","article-title":"Optimizing de novo assembly of short-read RNA-seq data for phylogenomics","author":"Y Yang","year":"2013","journal-title":"BMC genomics"},{"key":"pcbi.1009244.ref032","article-title":"Comparative analysis of algorithms for next-generation sequencing read alignment","author":"M Ruffalo","year":"2011","journal-title":"Bioinformatics"},{"issue":"S7","key":"pcbi.1009244.ref033","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1186\/s12864-016-2895-8","article-title":"Benchmarking of de novo assembly algorithms for Nanopore data reveals optimal performance of OLC approaches","volume":"17","author":"Y Cherukuri","year":"2016","journal-title":"BMC Genomics"},{"key":"pcbi.1009244.ref034","article-title":"A performance comparison of data and memory allocation strategies for sequence aligners on NUMA architectures","author":"J Lenis","year":"2017","journal-title":"Cluster Computing"},{"key":"pcbi.1009244.ref035","doi-asserted-by":"crossref","unstructured":"Ramirez-Gargallo G, Garcia-Gasulla M, Mantovani F. TensorFlow on state-of-the-art HPC clusters: A machine learning use case. In: Proceedings\u201419th IEEE\/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2019; 2019.","DOI":"10.1109\/CCGRID.2019.00067"},{"key":"pcbi.1009244.ref036","volume-title":"Advances in Parallel Computing","author":"C Kutzner","year":"2014"},{"key":"pcbi.1009244.ref037","article-title":"SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing","author":"A Bankevich","year":"2012","journal-title":"Journal of Computational Biology"},{"key":"pcbi.1009244.ref038","doi-asserted-by":"crossref","unstructured":"Chaichoompu K, Kittitornkun S, Tongsima S. MT-ClustalW: multithreading multiple sequence alignment. In: Proceedings 20th IEEE International Parallel & Distributed Processing Symposium. IEEE; 2006. p. 8-pp.","DOI":"10.1109\/IPDPS.2006.1639537"},{"key":"pcbi.1009244.ref039","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.parco.2014.09.010","article-title":"High Performance computing improvements on bioinformatics consistency-based multiple sequence alignment tools","volume":"42","author":"M Orobitg","year":"2015","journal-title":"Parallel Computing"},{"key":"pcbi.1009244.ref040","doi-asserted-by":"crossref","unstructured":"Xavier MG, Neves MV, Rossi FD. Performance evaluation of container-based virtualization for high performance computing environments. In: Proceedings of the 2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2013; 2013.","DOI":"10.1109\/PDP.2013.41"},{"key":"pcbi.1009244.ref041","unstructured":"Arango C, Dernat R, Sanabria J. Performance evaluation of container-based virtualization for high performance computing environments. arXiv preprint arXiv:170910140. 2017;."},{"key":"pcbi.1009244.ref042","article-title":"Comparative analysis of virtualization methods in Big Data processing","author":"GI Radchenko","year":"2019","journal-title":"Supercomputing Frontiers and Innovations"},{"key":"pcbi.1009244.ref043","article-title":"BOOTABLE: Bioinformatics benchmark tool suite for applications and hardware","author":"M Hanussek","year":"2020","journal-title":"Future Generation Computer Systems"},{"key":"pcbi.1009244.ref044","doi-asserted-by":"crossref","unstructured":"Wu P, Ott T, Morie J. Ansible. 2017;","DOI":"10.1145\/2927929.2927933"},{"key":"pcbi.1009244.ref045","article-title":"MUSCLE: Multiple sequence alignment with high accuracy and high throughput","author":"RC Edgar","year":"2004","journal-title":"Nucleic Acids Research"},{"key":"pcbi.1009244.ref046","article-title":"T-coffee: A novel method for fast and accurate multiple sequence alignment","author":"C Notredame","year":"2000","journal-title":"Journal of Molecular Biology"},{"key":"pcbi.1009244.ref047","article-title":"CHARMM: The biomolecular simulation program","author":"BR Brooks","year":"2009","journal-title":"Journal of Computational Chemistry"},{"key":"pcbi.1009244.ref048","unstructured":"D A Case, K Belfon, I Y Ben-Shalom. Amber; 2020. https:\/\/ambermd.org."},{"key":"pcbi.1009244.ref049","unstructured":"Paszke A, Gross S, Massa F. PyTorch: An imperative style, high-performance deep learning library. arXiv:191201703. 2019;."},{"key":"pcbi.1009244.ref050","article-title":"The sequence of the human genome","author":"J Craig Venter","year":"2001","journal-title":"Science"},{"key":"pcbi.1009244.ref051","article-title":"Velvet: Algorithms for de novo short read assembly using de Bruijn graphs","author":"DR Zerbino","year":"2008","journal-title":"Genome Research"},{"key":"pcbi.1009244.ref052","article-title":"IDBA-UD: A de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth","author":"Y Peng","year":"2012","journal-title":"Bioinformatics"},{"key":"pcbi.1009244.ref053","volume-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","author":"P Medvedev","year":"2011"},{"key":"pcbi.1009244.ref054","article-title":"BayesHammer: Bayesian clustering for error correction in single-cell sequencing","author":"SI Nikolenko","year":"2013","journal-title":"BMC Genomics"},{"key":"pcbi.1009244.ref055","article-title":"Bioinformatics- Sequence and Genome Analysis","author":"DW Mount","year":"2013","journal-title":"Journal of Chemical Information and Modeling"},{"key":"pcbi.1009244.ref056","unstructured":"Bushnell B. BBMap; 2015. https:\/\/sourceforge.net\/projects\/bbmap\/."},{"key":"pcbi.1009244.ref057","unstructured":"Mari\u0107 J. Long read RNA-seq mapper; 2015. http:\/\/bib.irb.hr\/datoteka\/773708.Josip_Maric_diplomski.pdf."},{"key":"pcbi.1009244.ref058","article-title":"Fast gapped-read alignment with Bowtie 2","author":"B Langmead","year":"2012","journal-title":"Nature methods"},{"key":"pcbi.1009244.ref059","article-title":"Fast and accurate long-read alignment with Burrows-Wheeler transform","author":"H Li","year":"2010","journal-title":"Bioinformatics"},{"key":"pcbi.1009244.ref060","author":"P Ferragina","year":"2002","journal-title":"Opportunistic data structures with applications"},{"key":"pcbi.1009244.ref061","article-title":"Fast and accurate short read alignment with Burrows-Wheeler transform","author":"H Li","year":"2009","journal-title":"Bioinformatics"},{"key":"pcbi.1009244.ref062","unstructured":"Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint arXiv 13033997. 2013;."},{"key":"pcbi.1009244.ref063","article-title":"Exploring single-sample snp and indel calling with whole-genome de novo assembly","author":"H Li","year":"2012","journal-title":"Bioinformatics"},{"key":"pcbi.1009244.ref064","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4939-6622-6_8","article-title":"Multiple sequence alignment","author":"P Bawono","year":"2017","journal-title":"Methods in Molecular Biology"},{"key":"pcbi.1009244.ref065","article-title":"Clustal Omega","author":"F Sievers","year":"2014","journal-title":"Current Protocols in Bioinformatics"},{"key":"pcbi.1009244.ref066","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/978-1-62703-646-7_6","article-title":"Clustal Omega, Accurate Alignment of Very Large Numbers of Sequences","author":"F Sievers","year":"2014","journal-title":"Methods in Molecular Biology"},{"key":"pcbi.1009244.ref067","unstructured":"Arthur D, Vassilvitskii S. k-means++: the advantages of careful seeding. Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. 2007;."},{"key":"pcbi.1009244.ref068","article-title":"Protein homology detection by HMM-HMM comparison","author":"J S\u00f6ding","year":"2005","journal-title":"Bioinformatics"},{"key":"pcbi.1009244.ref069","article-title":"Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega","author":"F Sievers","year":"2011","journal-title":"Molecular Systems Biology"},{"key":"pcbi.1009244.ref070","article-title":"MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform","author":"K Katoh","year":"2002","journal-title":"Nucleic Acids Research"},{"key":"pcbi.1009244.ref071","article-title":"MAFFT version 5: Improvement in accuracy of multiple sequence alignment","author":"K Katoh","year":"2005","journal-title":"Nucleic Acids Research"},{"key":"pcbi.1009244.ref072","article-title":"Recent developments in the MAFFT multiple sequence alignment program","author":"K Katoh","year":"2008","journal-title":"Briefings in Bioinformatics"},{"issue":"15","key":"pcbi.1009244.ref073","doi-asserted-by":"crossref","first-page":"1899","DOI":"10.1093\/bioinformatics\/btq224","article-title":"Parallelization of the MAFFT multiple sequence alignment program","volume":"26","author":"K Katoh","year":"2010","journal-title":"Bioinformatics"},{"key":"pcbi.1009244.ref074","article-title":"MAFFT multiple sequence alignment software version 7: Improvements in performance and usability","author":"K Katoh","year":"2013","journal-title":"Molecular Biology and Evolution"},{"key":"pcbi.1009244.ref075","article-title":"SINA: Accurate high-throughput multiple sequence alignment of ribosomal RNA genes","author":"E Pruesse","year":"2012","journal-title":"Bioinformatics"},{"key":"pcbi.1009244.ref076","article-title":"The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools","author":"C Quast","year":"2013","journal-title":"Nucleic Acids Research"},{"key":"pcbi.1009244.ref077","article-title":"Multiple sequence alignment using partial order graphs","author":"C Lee","year":"2002","journal-title":"Bioinformatics"},{"key":"pcbi.1009244.ref078","article-title":"ARB: A software environment for sequence data","author":"W Ludwig","year":"2004","journal-title":"Nucleic Acids Research"},{"key":"pcbi.1009244.ref079","article-title":"Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers","author":"MJ Abraham","year":"2015","journal-title":"SoftwareX"},{"key":"pcbi.1009244.ref080","article-title":"Particle mesh Ewald: An N\u00b7log(N) method for Ewald sums in large systems","author":"T Darden","year":"1993","journal-title":"The Journal of Chemical Physics"},{"key":"pcbi.1009244.ref081","unstructured":"GoogleResearch. TensorFlow: Large-scale machine learning on heterogeneous systems. Google Research. 2015;"},{"key":"pcbi.1009244.ref082","unstructured":"CIFAR-10; Accessed 11 Feb 2020. https:\/\/www.tensorflow.org\/tutorials\/images\/cnn."},{"key":"pcbi.1009244.ref083","doi-asserted-by":"crossref","unstructured":"He K, Sun J. Convolutional neural networks at constrained time cost. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2015.","DOI":"10.1109\/CVPR.2015.7299173"},{"key":"pcbi.1009244.ref084","volume-title":"Assessing Rare Variation in Complex Traits: Design and Analysis of Genetic Studies","author":"A Auton","year":"2015"},{"key":"pcbi.1009244.ref085","article-title":"The sequence read archive","author":"R Leinonen","year":"2011","journal-title":"Nucleic Acids Research"},{"key":"pcbi.1009244.ref086","article-title":"GenBank","author":"EW Sayers","year":"2019","journal-title":"Nucleic Acids Research"},{"key":"pcbi.1009244.ref087","article-title":"Primary and heterotrophic productivity relate to multikingdom diversity in a hypersaline mat","author":"HC Bernstein","year":"2017","journal-title":"FEMS Microbiology Ecology"},{"key":"pcbi.1009244.ref088","article-title":"GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation","author":"B Hess","year":"2008","journal-title":"Journal of Chemical Theory and Computation"},{"key":"pcbi.1009244.ref089","article-title":"A flexible algorithm for calculating pair interactions on SIMD architectures","author":"S P\u00e1ll","year":"2013","journal-title":"Computer Physics Communications"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1009244","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T00:00:00Z","timestamp":1627603200000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1009244","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T18:31:41Z","timestamp":1627669901000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1009244"}},"subtitle":[],"editor":[{"given":"Christos A.","family":"Ouzounis","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,7,20]]},"references-count":89,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,7,20]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1009244","relation":{"new_version":[{"id-type":"doi","id":"10.1371\/journal.pcbi.1009244","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,20]]}}}