Support Vector Machines on GPU with Sparse Matrix Format

Author

Lin, Tsung-Kai ; Chien, Shao-Yi

Author_Institution

Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan

fYear

2010

fDate

12-14 Dec. 2010

Firstpage

313

Lastpage

318

Abstract

Emerging general-purpose Graphics Processing Unit (GPU) provides a multi-core platform for wide applications, including machine learning algorithms. In this paper, we proposed several techniques to accelerate Support Vector Machines (SVM) on GPUs. Sparse matrix format is introduced into parallel SVM to achieve better performance. Experimental results show that the speedup of 55x-133.8x over LIBSVM can be achieved in training process on NVIDIA GeForce GTX470.

Keywords

coprocessors; learning (artificial intelligence); multiprocessing systems; sparse matrices; support vector machines; GPU; LIBSVM; NVIDIA GeForce GTX470; emerging general-purpose graphics processing unit; machine learning; multicore platform; parallel SVM; sparse matrix format; support vector machines; Computer architecture; Graphics processing unit; Instruction sets; Kernel; Sparse matrices; Support vector machines; Training; GPGPU; Support Vector Machines; sparse matrix multiplication;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on

Conference_Location

Washington, DC

Print_ISBN

978-1-4244-9211-4

Type

conf

DOI

10.1109/ICMLA.2010.53

Filename

5708850

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2453953