Optimizing MapReduce for Multicore Architectures
MapReduce is a programming model for data-parallel programs originally intended for data centers. MapReduce simplifies parallel programming, hiding synchronization and task management. These properties make it a promising programming model for future processors with many cores, and existing MapReduce libraries such as Phoenix have demonstrated that applications written with MapReduce perform competitively with those written with Pthreads. This paper explores the design of the MapReduce data structures for grouping intermediate key/value pairs, which is often a performance bottleneck on multicore processors.