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Grex: An efficient MapReduce framework for graphics processing units

Can Basaran, Kyoung-Don Kang
Department of Computer Science, Binghamton University, United States
Journal of Parallel and Distributed Computing, 2013
@article{basaran2013grex,

   title={Grex: An efficient MapReduce framework for graphics processing units},

   author={Basaran, C. and Kang, K.D.},

   journal={Journal of Parallel and Distributed Computing},

   year={2013},

   publisher={Elsevier}

}

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In this paper, we present a new MapReduce framework, called Grex, designed to leverage general purpose graphics processing units (GPUs) for parallel data processing. Grex provides several new features. First, it supports a parallel split method to tokenize input data of variable sizes, such as words in e-books or URLs in web documents, in parallel using GPU threads. Second, Grex evenly distributes data to map/reduce tasks to avoid data partitioning skews. In addition, Grex provides a new memory management scheme to enhance the performance by exploiting the GPU memory hierarchy. Notably, all these capabilities are supported via careful system design without requiring any locks or atomic operations for thread synchronization. The experimental results show that our system is up to 12.4x and 4.1x faster than two state-of-the-art GPU-based MapReduce frameworks for the tested applications.
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