Hummingbird: efficient performance prediction for executing genomic applications in the cloud

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.

Automated summary

Executing genomic applications on cloud computing facilities often lacks tools to predict the most appropriate instance type, leading to over- or under-matching of resources. Hummingbird, a tool for predicting performance of computing instances on multiple cloud platforms with varying memory and CPU, can accurately predict the fastest, cheapest, and most cost-efficient compute instances economically.