CudaRF: a CUDA-based implementation of random forests. 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 (Fas-tRF and LibRF) shows that CudaRF outperforms both FastRF and LibRF for the studied classification task.

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 1 article )

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  1. Nasridinov, Aziz; Lee, Yangsun; Park, Young-Ho: Decision tree construction on GPU: ubiquitous parallel computing approach (2014)