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
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
Country/TerritoryKorea, Republic of
CityJeju
Period14/6/2214/6/25

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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