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Performance Evaluation of the NVIDIA GeForce 8800 GTX GPU for Machine Learning

Date

Authors

El Zein, Ahmed

McCreath, Eric

Rendell, Alistair

Smola, Alexander

Publisher

Institute of Electrical and Electronics Engineers (IEEE Inc)

Abstract

NVIDIA have released a new platform (CUDA) for general purpose computing on their graphical processing units (GPU). This paper evaluates use of this platform for statistical machine learning applications. The transfer rates to and from the GPU are measured, as is the performance of matrix vector operations on the GPU. An implementation of a sparse matrix vector product on the GPU is outlined and evaluated. Performance comparisons are made with the host processor.

Keywords

Keywords: Artificial intelligence; Matrix algebra; Robot learning; Vectors; Computational sciences; General purpose computing; Graphical processing units (GPU); Heidelberg (CO); International conferences; Machine-learning; Matrix-vector; Perfor mance comparison; Pe

URI

http://hdl.handle.net/1885/39255

Collections

ANU Research Publications

Source

Proceedings of the 2008 IEEE International Conference on Communication Systems

Type

Conference paper

DOI

10.1007/978-3-540-69384-0_52

Restricted until

2037-12-31

Downloads

File

Description

01_El Zein_Performance_Evaluation_of_the_2008.pdf (424.38 KB)

02_El Zein_Performance_Evaluation_of_the_2008.pdf (493.31 KB)

03_El Zein_Performance_Evaluation_of_the_2008.pdf (411.88 KB)

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