@inproceedings{57979f5216284110b872088163c45bbf,
title = "DPM: Data Partitioning Method for pipelined MapReduce on GPU",
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.",
author = "Jo, {Myung Hyun} and Ro, {Won Woo}",
year = "2014",
doi = "10.1109/ISCE.2014.6884382",
language = "English",
isbn = "9781479945924",
series = "Proceedings of the International Symposium on Consumer Electronics, ISCE",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ISCE 2014 - 18th IEEE International Symposium on Consumer Electronics",
address = "United States",
note = "18th IEEE International Symposium on Consumer Electronics, ISCE 2014 ; Conference date: 22-06-2014 Through 25-06-2014",
}