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Mars: Accelerating MapReduce with Graphics Processors
April 2011 (vol. 22 no. 4)
pp. 608-620
ASCII Text
x 
 
Wenbin Fang, Bingsheng He, Qiong Luo, Naga K. Govindaraju, "Mars: Accelerating MapReduce with Graphics Processors," IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 4, pp. 608-620, April, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TPDS.2010.158,
author = {Wenbin Fang and Bingsheng He and Qiong Luo and Naga K. Govindaraju},
title = {Mars: Accelerating MapReduce with Graphics Processors},
journal ={IEEE Transactions on Parallel and Distributed Systems},
volume = {22},
number = {4},
issn = {1045-9219},
year = {2011},
pages = {608-620},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPDS.2010.158},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Parallel and Distributed Systems
TI - Mars: Accelerating MapReduce with Graphics Processors
IS - 4
SN - 1045-9219
SP608
EP620
EPD - 608-620
A1 - Wenbin Fang,
A1 - Bingsheng He,
A1 - Qiong Luo,
A1 - Naga K. Govindaraju,
PY - 2011
KW - MapReduce
KW - graphics processor
KW - parallel computing
KW - multicore processor
KW - many-core architecture.
VL - 22
JA - IEEE Transactions on Parallel and Distributed Systems
ER -
 
Wenbin Fang, University of Wisconsin-Madison, Madison
Bingsheng He, Nanyang Technological University, Singapore
Qiong Luo, Hong Kong University of Science and Technology, Hong Kong
Naga K. Govindaraju, Microsoft Corp., Redmond
We design and implement Mars, a MapReduce runtime system accelerated with graphics processing units (GPUs). MapReduce is a simple and flexible parallel programming paradigm originally proposed by Google, for the ease of large-scale data processing on thousands of CPUs. Compared with CPUs, GPUs have an order of magnitude higher computation power and memory bandwidth. However, GPUs are designed as special-purpose coprocessors and their programming interfaces are less familiar than those on the CPUs to MapReduce programmers. To harness GPUs' power for MapReduce, we developed Mars to run on NVIDIA GPUs, AMD GPUs as well as multicore CPUs. Furthermore, we integrated Mars into Hadoop, an open-source CPU-based MapReduce system. Mars hides the programming complexity of GPUs behind the simple and familiar MapReduce interface, and automatically manages task partitioning, data distribution, and parallelization on the processors. We have implemented six representative applications on Mars and evaluated their performance on PCs equipped with GPUs as well as multicore CPUs. The experimental results show that, the GPU-CPU coprocessing of Mars on an NVIDIA GTX280 GPU and an Intel quad-core CPU outperformed Phoenix, the state-of-the-art MapReduce on the multicore CPU with a speedup of up to 72 times and 24 times on average, depending on the applications. Additionally, integrating Mars into Hadoop enabled GPU acceleration for a network of PCs.

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Index Terms:
MapReduce, graphics processor, parallel computing, multicore processor, many-core architecture.
Citation:
Wenbin Fang, Bingsheng He, Qiong Luo, Naga K. Govindaraju, "Mars: Accelerating MapReduce with Graphics Processors," IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 4, pp. 608-620, April 2011, doi:10.1109/TPDS.2010.158
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