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 alg...

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Bibliographic Details
Published in2011 9th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) pp. 95 - 101
Main Authors Grahn, H., Lavesson, N., Lapajne, M. H., Slat, D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2011
Subjects
Accuracy
GPGPU
Graphics processing unit
Graphics processing units
Instruction sets
Kernel
Machine learning
Memory management
Parallel computing
Radio frequency
Random forests
Vegetation
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ISBN9781457704758
1457704757
ISSN2161-5322
DOI10.1109/AICCSA.2011.6126612

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Summary: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.
ISBN:9781457704758
1457704757
ISSN:2161-5322
DOI:10.1109/AICCSA.2011.6126612