GPUMLib: An Efficient Open-Source GPU Machine Learning Library



Graphics Processing Units (GPUs) placed at our disposal an unprecedented computational-power that largely surpasses the performance of cutting-edge CPUs (Central Processing Units). The high-parallelism inherent to the GPU makes this device especially well-suited to address Machine Learning (ML) problems with prohibitively computational intensive tasks. Nevertheless, few ML algorithms have been implemented on the GPU and most are not openly shared, posing difficulties to researchers and engineers aiming to develop GPU-based systems. To
mitigate this problem, we propose the creation of an open source GPU Machine Learning Library (GPUMLib) that aims to provide the building blocks for the development of efficient GPU ML software. Experimental results on benchmark datasets show that the algorithms already implemented yield significant time savings over CPU counterparts.


International Journal of Computer Information Systems and Industrial Management Applications, Vol. 3, pp. 355-362, K. Saeed, A. Abraham, February 2011

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Year 2016 : 2 citations

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Year 2015 : 4 citations

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Year 2014 : 3 citations

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Year 2013 : 1 citations

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Year 2012 : 4 citations

 Comparative Study on Use of Mobile Videos in Elementary and Middle School
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 A Distributed Data Mining Framework Accelerated with Graphics Processing Units
NL Tran, Q Dugauthier, S Skhiri -

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