BSP vs MapReduce
Copyright policy )BSP vs MapReduce
The MapReduce framework has been generating a lot of interest in a wide range of areas. It has been widely adopted in industry and has been used to solve a number of non-trivial problems in academia. Putting MapReduce on strong theoretical foundations is crucial in understanding its capabilities. This work links MapReduce to the BSP model of computation, underlining the relevance of BSP to modern parallel algorithm design and defining a subclass of BSP algorithms that can be efficiently implemented in MapReduce.
13 pages, appeared at ICCS 2012
FOS: Computer and information sciences, BSP, Computer Science - Distributed, Parallel, and Cluster Computing, Parallel Algorithms, MapReduce, Distributed, Parallel, and Cluster Computing (cs.DC)
FOS: Computer and information sciences, BSP, Computer Science - Distributed, Parallel, and Cluster Computing, Parallel Algorithms, MapReduce, Distributed, Parallel, and Cluster Computing (cs.DC)
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).32 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!