2021 journal article
Hummingbird: efficient performance prediction for executing genomic applications in the cloud
BIOINFORMATICS, 37(17), 2537–2543.
TL;DR:
Hummerbird is introduced, a tool for predicting performance of computing instances with varying memory and CPU on multiple cloud platforms and accurately predicted the fastest, the cheapest, and the most cost-efficient compute instances in an economic manner.
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UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Source: Web Of Science
Added: October 4, 2021
Abstract Motivation A 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. Results Our 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 implementation Hummingbird is available as an open source tool at: https://github.com/StanfordBioinformatics/Hummingbird. Supplementary information Supplementary data are available at Bioinformatics online.