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Minesh B Amin
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In this tutorial we shall review three different and distinct approaches to parallel computing which can be used to solve problems in all manner of domains, including machine learning, natural language processing, finance, and computer vision. The first two approaches to be reviewed will be embarrassingly parallel in nature while the third approach will leverage fine-grain parallelism.
Traditional solutions for data and graph analytics tend to be highly fragmented, and take the form of stand-alone frameworks. In this poster session, we shall describe our approach that is centered around a suite of advanced parallel primitives embedded within SPM.Python. These primitives augment the serial Python language with concepts like parallel generators, emitters and exceptions.