Accelerating the XGBoost algorithm using GPU computing
Accelerating the XGBoost algorithm using GPU computing
About this item
Full title
Author / Creator
Publisher
San Diego: PeerJ, Inc
Journal title
Language
English
Formats
Publication information
Publisher
San Diego: PeerJ, Inc
Subjects
More information
Scope and Contents
Contents
We present a CUDA-based implementation of a decision tree construction algorithm within the gradient boosting library XGBoost. The tree construction algorithm is executed entirely on the graphics processing unit (GPU) and shows high performance with a variety of datasets and settings, including sparse input matrices. Individual boosting iterations...
Alternative Titles
Full title
Accelerating the XGBoost algorithm using GPU computing
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_27b08a1fb70d4f51a60d0e1a3e3433f0
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_27b08a1fb70d4f51a60d0e1a3e3433f0
Other Identifiers
ISSN
2376-5992
E-ISSN
2376-5992
DOI
10.7717/peerj-cs.127
