Master-worker model for MapReduce paradigm on the TILE64 many-core platform
Master-worker model for MapReduce paradigm on the TILE64 many-core platform
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DOI：
10.1016/j.future.2013.05.001
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
Xuan-Yi Lin;Yeh-Ching Chung
中科院分区：
文献类型：
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作者：
Xuan-Yi Lin;Yeh-Ching Chung
MapReduce is a popular programming paradigm for processing big data. It uses the master–worker model, which is widely used on distributed and loosely coupled systems such as clusters, to solve large problems with task parallelism. With the ubiquity of many-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 a many-core platform are viewed as a multifaceted 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 investigatemaster shareandworker shareschemes for implementation of a MapReduce library on the TILE64. The theoretical analysis shows that theworker sharescheme is inherently better for implementation of MapReduce library on the TILE64 many-core platform.
