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A CUDA Implementation of Random Forests: Early Results
Ansvarlig organisasjon
2010 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert) Published
Abstract [en]

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 parallel (FastRF) and sequential (LibRF) Random forests algorithms shows that CudaRF outperforms both FastRF and LibRF for the studied classification task.

sted, utgiver, år, opplag, sider
Göteborg: Chalmers Institute of Technology , 2010.
Emneord [en]
machine learning, graphics processing unit, random forests
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-7705Lokal ID: oai:bth.se:forskinfo7C7A825038DE6570C12577E3004FFB7COAI: oai:DiVA.org:bth-7705DiVA, id: diva2:835353
Konferanse
Third Swedish Workshop on Multi-core Computing
Tilgjengelig fra: 2012-09-18 Laget: 2010-11-22 Sist oppdatert: 2018-02-02bibliografisk kontrollert

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Grahn, HåkanLavesson, Niklas

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