Sorting big data on heterogeneous near-data processing systems

Erik Vermij, Leandro Fiorin, Christoph Hagleitner, Koen Bertels

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)


Big data workloads assumed recently a relevant importance in many business and scientific applications. Sorting ele-ments efficiently in big data workloads is a key operation. In this work, we analyze the implementation of the mergesort algorithm on heterogeneous systems composed of CPUs and near-data processors located on the system memory channels. For configurations with equal number of active CPU cores and near-data processors, our experiments show a per-formance speedup of up to 2.5, as well as up to 2.5× energy-per-solution reduction.

Original languageEnglish
Title of host publicationCF'17 Proceedings of the Computing Frontiers Conference
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages6
ISBN (Print)978-1-4503-4487-6
Publication statusPublished - 2017
EventACM International Conference on Computing Frontiers 2017: CF'17 - Siena, Italy
Duration: 15 May 201717 May 2017


ConferenceACM International Conference on Computing Frontiers 2017


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