DPM: Data Partitioning Method for pipelined MapReduce on GPU

Myung Hyun Jo, Won Woo Ro

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

Abstract

The MapReduce frameworks using a modern graphic processor (GPU) have improved the performance of data-intensive applications. While the prior researches have enhanced the parallelism of the MapReduce application on a GPU, archiving optimal distribution of big data on heterogeneous devices is still a challengeable issue. We therefore propose a method to evenly separate the computing cost under limited memory size. To solve this problem, we design and propose DPM, a Data Partitioning Method, using a GPU to smartly distribute workload of MapReduce. The proposed technique provides well-balanced processing cost for heterogeneous devices.

Original languageEnglish
Title of host publicationISCE 2014 - 18th IEEE International Symposium on Consumer Electronics
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781479945924
DOIs
Publication statusPublished - 2014 Jan 1
Event18th IEEE International Symposium on Consumer Electronics, ISCE 2014 - Jeju, Korea, Republic of
Duration: 2014 Jun 222014 Jun 25

Publication series

NameProceedings of the International Symposium on Consumer Electronics, ISCE

Other

Other18th IEEE International Symposium on Consumer Electronics, ISCE 2014
CountryKorea, Republic of
CityJeju
Period14/6/2214/6/25

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Jo, M. H., & Ro, W. W. (2014). DPM: Data Partitioning Method for pipelined MapReduce on GPU. In ISCE 2014 - 18th IEEE International Symposium on Consumer Electronics [6884382] (Proceedings of the International Symposium on Consumer Electronics, ISCE). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCE.2014.6884382