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GPU Computing for Machine Learning Algorithms

Mauro Garofalo
University Federico II, Naples, Italy
University Federico II, 2012

@article{garofalo2012gpu,

   title={GPU Computing for Machine Learning Algorithms},

   author={Garofalo, M.},

   year={2012}

}

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Computing has rapidly established itself as essential and important to many branches of science, to the point where computational science is a commonly used term. Indeed, the application and importance of computing is set to grow dramatically across almost all the sciences. Computing has started to change how science is done, enabling new scientific advances through enabling new kinds of experiments. These experiments are also generating new kinds of data of increasingly exponential complexity and volume. Achieving the goal of being able to use, exploit and share these data most effectively is a huge challenge. It is necessary to merge the capabilities of a file system to store and transmit bulk data from experiments, with logical organization of files into indexed data collections, allowing efficient query and analytical operations. It is also necessary to incorporate extensive metadata describing each experiment and the produced data. Rather than flat files traditionally used in scientific data processing, the full power of relational databases is needed to allow effective interactions with the data, and an interface which can be exploited by the extensive scientific toolkits available, for purposes such as visualization and plotting.
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