The overwhelming data produced everyday and the increasing
performance and cost requirements of applicationsare transversal to
a wide range of activities in society, from science to industry. In
particular, the magnitude and complexity of the tasks that Machine
Learning (ML) algorithms have to solve are driving the need to
devise adaptive many-core machines that scale well with the volume
of data, or in other words, can handle Big Data.
This book gives a concise view on how to extend the
applicability of well-known ML algorithms in Graphics Processing
Unit (GPU) with data scalability in mind. It presents a series of
new techniques to enhance, scale and distribute data in a Big
Learning framework. It is not intended to be a comprehensive survey
of the state of the art of the whole field of machine learning for
Big Data. Its purpose is less ambitious and more practical: to
explain and illustrate existing and novel GPU-based ML algorithms,
not viewed as a universal solution for the Big Data challenges but
rather as part of the answer, which may require the use of
different strategies coupled together."
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