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Scaling up MapReduce-based Big Data Processing on Multi-GPU systems

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Abstract

MapReduce is a popular data-parallel processing model encompassed with recent advances in computing technology and has been widely exploited for large-scale data analysis. The high demand on MapReduce has stimulated the investigation of MapReduce implementations with different architectural models and computing paradigms, such as multi-core clusters, Clouds, Cubieboards and GPUs. Particularly, current GPU-based MapReduce approaches mainly focus on single-GPU algorithms and cannot handle large data sets, due to the limited GPU memory capacity. Based on the previous multi-GPU MapReduce version MGMR, this paper proposes an upgrade version MGMR++ to eliminate GPU memory limitation and a pipelined version, PMGMR, to handle the Big Data challenge through both CPU memory and hard disks. MGMR++ is extended from MGMR with flexible C++ templates and CPU memory utilization, while PMGMR fine-tuned the performance through the latest GPU features such as streams and Hyper-Q as well as hard disk utilization. Compared to MGMR (Jiang et al., Cluster Computing 2013), the proposed schemes achieve about 2.5-fold performance improvement, increase system scalability, and allow programmers to write straightforward MapReduce code for Big Data.

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Acknowledgments

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies or institutions. This research is based upon work partially supported by National Science Foundation/USA under grant No. 0959124, Ministry of Science and Technology/Taiwan (MOST)/Taiwan under grant MOST 103-2221-E-126-010-, The Providence University research project under grant PU102-11100-A12 and NVIDIA through CUDA Center Awards.

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Authors and Affiliations

  1. Department of Computer Science, Arkansas State University, Jonesboro, AR, USA

    Hai Jiang, Yi Chen & Zhi Qiao

  2. Department of Computer Science and Information Engineering, Providence University, Taichung, 43301, Taiwan

    Tien-Hsiung Weng & Kuan-Ching Li

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  1. Hai Jiang
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  2. Yi Chen
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  3. Zhi Qiao
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  5. Kuan-Ching Li
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Correspondence to Kuan-Ching Li.

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Jiang, H., Chen, Y., Qiao, Z. et al. Scaling up MapReduce-based Big Data Processing on Multi-GPU systems. Cluster Comput 18, 369–383 (2015). https://doi.org/10.1007/s10586-014-0400-1

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