Optimizing the MapReduce framework on Intel Xeon Phi coprocessor

MapReduce has become one of the most popular framework for building big-data applications. It was originally designed for distributed-computing, and has been extended to various hardware architectures, e.g., multi-core CPUs, GPUs and FPGAs. In this work, we develop the first MapReduce framework on the recently released Intel Xeon Phi coprocessor. We utilize advanced features of the Xeon Phi to achieve high performance. In order to take advantage of the SIMD vector processing units, we propose a vectorization friendly technique to assist the auto-vectorization as well as develop SIMD hash computation algorithms. Furthermore, we utilize MIMD hyper-threading to pipeline the map and reduce phases to improve the resource utilization. We also eliminate multiple local arrays but use low cost atomic operations on the global array for some applications, which can improve the thread scalability and data locality. We conduct comprehensive experiments to compare our optimized MapReduce framework with a state-of-the-art multi-core based MapReduce framework (Phoenix++). By evaluating six real-world applications, the experimental results show that our optimized framework is 1.2X to 38X faster than Phoenix++ for various applications on the Xeon Phi.