Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem
- Resource Type
- Conference
- Authors
- Hay Bin Sulaiman, M.A.; Suliman, A.; Ahmad, A.R.
- Source
- Proceedings of the 6th International Conference on Information Technology and Multimedia Information Technology and Multimedia (ICIMU), 2014 International Conference on. :299-302 Nov, 2014
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Graphics processing units
Support vector machines
Machine learning algorithms
Data mining
Training
Information technology
Multimedia communication
Support Vector Machines
Graphics Processing Unit
parallel computing
performance measurement
- Language
This paper presents performance evaluation of GPU-accelerated Support Vector Machines (SVMs) using large datasets. Although SVMs algorithm is popular among machine learning researchers and data mining practitioners, its computational time is too long and impractical for large datasets due to its complex Quadratic Programming (QP) solver. The result shows that using GPU-accelerated SVMs can significantly reduce computational time for training phase of SVMs and it can be a viable solution for any project that require real-time forecasting output.