Master-worker model for MapReduce paradigm on the TILE64 many-core platform

MapReduce is a popular programming paradigm for processing big data. It uses themaster–workermodel, which is widely used on distributed and loosely coupled systems such as clusters, to solve large problems with task parallelism.With the ubiquity ofmany-core architectures in recent years and foreseeable future, the many-core platform will be one of the main computing platforms to execute MapReduce programs. Therefore, it is essential to optimize MapReduce programs on many-core platforms. Optimizations of parallel programs for amany-core platform are viewed as amultifaceted problem,where both system and architectural factors should be taken into account. In this paper, we look into the problem by constructing a master–worker model for MapReduce paradigm on the TILE64 many-core platform. We investigate master share and worker share schemes for implementation of a MapReduce library on the TILE64. The theoretical analysis shows that the worker share scheme is inherently better for implementation of MapReduce library on the TILE64 many-core platform. © 2013 Elsevier B.V. All rights reserved.