Hummingbird: efficient performance prediction for executing genomic applications in the cloud.
Hummingbird: efficient performance prediction for executing genomic applications in the cloud.
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Hummingbird：在云中执行基因组应用程序的高效性能预测。
DOI：
10.1093/bioinformatics/btab161
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Pan,Cuiping
中科院分区：
文献类型：
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作者：
Bahmani,Amir;Xing,Ziye;Krishnan,Vandhana;Ray,Utsab;Mueller,Frank;Alavi,Amir;Tsao,PhilipS;Snyder,MichaelP;Pan,Cuiping
MotivationA major drawback of executing genomic applications on cloud computing facilities is the lack of tools to predict which instance type is the most appropriate, often resulting in an over- or under- matching of resources. Determining the right configuration before actually running the applications will save money and time. Here, we introduce Hummingbird, a tool for predicting performance of computing instances with varying memory and CPU on multiple cloud platforms.ResultsOur experiments on three major genomic data pipelines, including GATK HaplotypeCaller, GATK Mutect2 and ENCODE ATAC-seq, showed that Hummingbird was able to address applications in command line specified in JSON format or workflow description language (WDL) format, and accurately predicted the fastest, the cheapest and the most cost-efficient compute instances in an economic manner.Availability and implementationHummingbird is available as an open source tool at: https://github.com/StanfordBioinformatics/Hummingbird.Supplementary informationSupplementary data are available atBioinformaticsonline.
