DocumentCode :
3462350
Title :
Moim: A Multi-GPU MapReduce Framework
Author :
Mengjun Xie ; Kyoung-Don Kang ; Basaran, Can
Author_Institution :
Dept. of Comput. Sci., State Univ. of New York at Binghamton, Binghamton, NY, USA
fYear :
2013
fDate :
3-5 Dec. 2013
Firstpage :
1279
Lastpage :
1286
Abstract :
MapReduce greatly decrease the complexity of developing applications for parallel data processing. To considerably improve the performance of MapReduce applications, we design a new MapReduce framework, called Moim, which 1) effectively utilizes both CPUs and GPUs (general purpose Graphics Processing Units), 2) overlaps CPU and GPU computations, 3) enhances load balancing in the map and reduce phases, and 4) efficiently handles not only fixed but also variable size data. We have implemented Moim and compared its performance with an advanced multi-GPU MapReduce framework. Moim achieves 20% - 90% speedup for different data sizes and numbers of the GPUs used for data processing.
Keywords :
data handling; graphics processing units; multiprocessing systems; parallel programming; resource allocation; CPU computation; GPU computation; MapReduce application; Moim; data handling; general purpose graphics processing units; load balancing; multiGPU MapReduce framework; parallel data processing; Clustering algorithms; Delays; Graphics processing units; Load management; Pipeline processing; Sorting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Science and Engineering (CSE), 2013 IEEE 16th International Conference on
Conference_Location :
Sydney, NSW
Type :
conf
DOI :
10.1109/CSE.2013.190
Filename :
6755372
Link To Document :
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