publication . Conference object . Other literature type . Preprint . 2013

optimizing the mapreduce framework on intel xeon phi coprocessor

Lu, Mian; Zhang, Lei; Huynh, Huynh Phung; Ong, Zhongliang; Liang, Yun; He, Bingsheng; Goh, Rick Siow Mong; Huynh, Richard;
Open Access
  • Published: 01 Oct 2013
  • Publisher: IEEE
Abstract
With the ease-of-programming, flexibility and yet efficiency, MapReduce has become one of the most popular frameworks for building big-data applications. MapReduce was originally designed for distributed-computing, and has been extended to various architectures, e,g, multi-core CPUs, GPUs and FPGAs. In this work, we focus on optimizing the MapReduce framework on Xeon Phi, which is the latest product released by Intel based on the Many Integrated Core Architecture. To the best of our knowledge, this is the first work to optimize the MapReduce framework on the Xeon Phi. In our work, we utilize advanced features of the Xeon Phi to achieve high performance. In order...
Subjects
free text keywords: Xeon Phi, Parallel computing, Vectorization (mathematics), Multithreading, MIMD, SIMD, Vector processor, Coprocessor, Scalability, Computer science, Computer Science - Distributed, Parallel, and Cluster Computing
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