Spark on the ARC - Big data analytics frameworks on HPC clusters

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dc.contributorVirginia Techen
dc.contributor.authorDeYoung, Mark E.en
dc.contributor.authorSalman, Mohammeden
dc.contributor.authorBedi, Himanshuen
dc.contributor.authorRaymond, David Richarden
dc.contributor.authorTront, Joseph G.en
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2017-07-31T15:07:12Zen
dc.date.available2017-07-31T15:07:12Zen
dc.date.issued2017-07en
dc.description.abstractIn this paper we document our approach to overcoming service discovery and configuration of Apache Hadoop and Spark frameworks with dynamic resource allocations in a batch oriented Advanced Research Computing (ARC) High Performance Computing (HPC) environment. ARC efforts have produced a wide variety of HPC architectures. A common HPC architectural pattern is multi-node compute clusters with low-latency, high-performance interconnect fabrics and shared central storage. This pattern enables processing of workloads with high data co-dependency, frequently solved with message passing interface (MPI) programming models, and then executed as batch jobs. Unfortunately, many HPC programming paradigms are not well suited to big data workloads which are often easily separable. Our approach lowers barriers of entry to HPC environments by enabling end users to utilize Apache Hadoop and Spark frameworks that support big data oriented programming paradigms appropriate for separable workloads in batch oriented HPC environments.en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1145/3093338.3093375en
dc.identifier.urihttp://hdl.handle.net/10919/78482en
dc.language.isoenen
dc.publisherACMen
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en
dc.subjectadvanced research computingen
dc.subjecthigh performance computingen
dc.subjectbig dataen
dc.titleSpark on the ARC - Big data analytics frameworks on HPC clustersen
dc.title.serialACM Proceedings of PEARC17en
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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