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CudaRF: A CUDA-based Implementation of Random Forests

Hakan Grahn, Niklas Lavesson, Mikael Hellborg Lapajne, Daniel Slat
School of Computing, Blekinge Institute of Technology, SE-371 39 Karlskrona, Sweden
9th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA), 2011

@article{grahn2011cudarf,

   title={CudaRF: A CUDA-based Implementation of Random Forests},

   author={Grahn, H. and Lavesson, N. and Lapajne, M.H. and Slat, D.},

   year={2011}

}

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Machine learning algorithms are frequently applied in data mining applications. Many of the tasks in this domain concern high-dimensional data. Consequently, these tasks are often complex and computationally expensive. This paper presents a GPU-based parallel implementation of the Random Forests algorithm. In contrast to previous work, the proposed algorithm is based on the compute unified device architecture (CUDA). An experimental comparison between the CUDA-based algorithm (CudaRF), and state-of-the-art Random Forests algorithms (FastRF and LibRF) shows that CudaRF outperforms both FastRF and LibRF for the studied classification task.
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