HadoopCL: MapReduce on Distributed Heterogeneous Platforms Through Seamless Integration of Hadoop and OpenCL

Max Grossman, Mauricio Breternitz, Vivek Sarkar
Department of Computer Science, Rice University
International Workshop on High Performance Data Intensive Computing, 2013

   title={HadoopCL: MapReduce on Distributed Heterogeneous Platforms Through Seamless Integration of Hadoop and OpenCL},

   author={Grossman, Max and Breternitz, Mauricio and Sarkar, Vivek},



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As the scale of high performance computing systems grows, three main challenges arise: the programmability, reliability, and energy efficiency of those systems. Accomplishing all three without sacrificing performance requires a rethinking of legacy distributed programming models and homogeneous clusters. In this work, we integrate Hadoop MapReduce with OpenCL to enable the use of heterogeneous processors in a distributed system. We do this by exploiting the implicit data-parallelism of mappers and reducers in a MapReduce system. Combining Hadoop and OpenCL provides 1) an easy-to-learn and flexible application programming interface in a high level and popular programming language, 2) the reliability guarantees and distributed filesystem of Hadoop, and 3) the low power consumption and performance acceleration of heterogeneous processors. This paper presents HadoopCL: an extension to Hadoop which supports execution of user-written Java kernels on heterogeneous devices, optimizes communication through asynchronous transfers and dedicated I/O threads, automatically generates OpenCL kernels from Java bytecode using the open source tool APARAPI, and achieves nearly 3x overall speedup and better than 55x speedup of the computational sections for example MapReduce applications, relative to Hadoop.
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