GPUMLib: A new Library to combine Machine Learning algorithms with Graphics Processing Units. The Graphics Processing Unit (GPU) is a highly parallel, many-core device with enormous computational power, especially well-suited to address Machine Learning (ML) problems that can be expressed as data-parallel computations. As problems become increasingly demanding, parallel implementations of ML algorithms become critical for developing hybrid intelligent real-world applications. The relative low cost of GPUs combined with the unprecedent computational power they offer, make them particularly well-positioned to automatically analyze and capture relevant information from large amounts of data. In this paper, we propose the creation of an open source GPU Machine Learning Library (GPUMLib) that aims to provide the building blocks for the scientific community to develop GPU ML algorithms. Experimental results on benchmark datasets demonstrate that the GPUMLib components already implemented achieve significant savings over the counterpart CPU implementations.
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References in zbMATH (referenced in 2 articles )
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- Baharani, Mohammadreza; Noori, Hamid; Aliasgari, Mohammad; Navabi, Zain: High-level design space exploration of locally linear neuro-fuzzy models for embedded systems (2014)
- Lopes, Noel; Ribeiro, Bernardete: Towards adaptive learning with improved convergence of deep belief networks on graphics processing units (2014)