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 language | English |
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| Title of host publication | ISCE 2014 - 18th IEEE International Symposium on Consumer Electronics |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Print) | 9781479945924 |
| DOIs | |
| Publication status | Published - 2014 Jan 1 |
| Event | 18th IEEE International Symposium on Consumer Electronics, ISCE 2014 - Jeju, Korea, Republic of Duration: 2014 Jun 22 → 2014 Jun 25 |
Publication series
| Name | Proceedings of the International Symposium on Consumer Electronics, ISCE |
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Other
| Other | 18th IEEE International Symposium on Consumer Electronics, ISCE 2014 |
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| Country | Korea, Republic of |
| City | Jeju |
| Period | 14/6/22 → 14/6/25 |
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