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- W15155067 abstract "The amount of data available is ever-increasing. At the same time, the available time to learn from the available data is decreasing in many applications, especially on the web. These two trends together with limited improvements in per-cpu speed and hard disk bandwidth lead to the need for parallel machine learning algorithms. Numerous have been proposed in the past (including [1, 3, 4]). Many of them make use of frameworks like MapReduce [2], as it facilitates easy parallelization and provides fault tolerance and data local computation at the framework level. However, MapReduce also introduces some inherent inefficiencies when compared to message passing systems like MPI." @default.
- W15155067 created "2016-06-24" @default.
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- W15155067 date "2010-01-01" @default.
- W15155067 modified "2023-09-27" @default.
- W15155067 title "A Convenient Framework for Efficient Parallel Multipass Algorithms" @default.
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