Title :
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
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;
Conference_Titel :
Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4244-9211-4
DOI :
10.1109/ICMLA.2010.53
