GYAN: Accelerating Bioinformatics Tools in Galaxy with GPU-Aware Computation Mapping

Gulsum Gudukbay, Jashwant Raj Gunasekaran, Yilin Feng, Mahmut Kandemir, Anton Nekrutenko, Chita R. Das, Paul Medvedev, Bjorn Gruning, Nate Coraor, Nathan Roach, Enis Afgan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Galaxy is an open-source web-based framework that is widely used for performing computational analyses in diverse application domains, such as genome assembly, computational chemistry, ecology, and epigenetics, to name a few. The current Galaxy software framework runs on several high-performance computing platforms such as on-premise clusters, public data centers, and national lab supercomputers. These infrastructures also provide support for state-of-the-art accelerators like Graphical Processing Units (GPUs). When coupled with accelerator support, the tools executing in Galaxy can benefit from massive performance gains in terms of computation time, thereby allowing a more robust computational analysis environment for researchers. Despite tools having GPU capabilities, the current Galaxy framework does not support GPUs, and thus prevents tools from taking advantage of the performance benefits offered by GPUs. We present and experimentally evaluate GYAN, a GPU-aware computation mapping and orchestration functionality implemented in Galaxy that allows the Galaxy tools to be executed on a GPU-enabled cluster. GYAN has the capability of identifying GPU-supported tools and scheduling them on single or multiple GPU nodes based on the availability in the cluster. GYAN supports both native and containerized tool execution. We performed extensive evaluations of the implementation using popular bio-engineering tools to demonstrate the benefits of using GPU technologies. For example, the Racon consensus tool executes ~2× faster than the regular baseline CPU-only jobs, while the Bonito base calling tool shows ~50× speedup.

Original languageEnglish (US)
Title of host publication2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2021 - In conjunction with IEEE IPDPS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages194-203
Number of pages10
ISBN (Electronic)9781665435772
DOIs
StatePublished - Jun 2021
Event2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2021 - Virtual, Portland, United States
Duration: May 17 2021 → …

Publication series

Name2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2021 - In conjunction with IEEE IPDPS 2021

Conference

Conference2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2021
Country/TerritoryUnited States
CityVirtual, Portland
Period5/17/21 → …

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems

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