Change search
ReferencesLink to record
Permanent link

Direct link
CudaRF: A CUDA-based Implementation of Random Forests
Responsible organisation
2011 (English)Conference paper (Refereed) 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 Random Forests algorithms (FastRF and LibRF) shows that CudaRF outperforms both FastRF and LibRF for the studied classification task.

Place, publisher, year, edition, pages
Sharm El-Sheikh, Egypt: IEEE , 2011.
Keyword [en]
Random forests, Machine learning, Parallel computing, Graphics processing units, GPGPU
National Category
Computer Science
URN: urn:nbn:se:bth-7343Local ID: diva2:834950
9th ACS/IEEE Int'l Conference on Computer Systems And Applications (AICCSA 2011)
Available from: 2012-09-18 Created: 2012-01-05 Last updated: 2015-06-30Bibliographically approved

Open Access in DiVA

fulltext(436 kB)55 downloads
File information
File name FULLTEXT01.pdfFile size 436 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Grahn, HåkanLavesson, Niklas
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 55 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 43 hits
ReferencesLink to record
Permanent link

Direct link