Support Vector Machine (SVM) has been widely used in data-mining and Big Data applications as modern commercial databases start to attach an increasing importance to the analytic capabilities. In recent years, SVM was adapted to the ?eld of High Performance Computing for power/performance prediction, auto-tuning, and runtime scheduling. However, even at the risk of losing prediction accuracy due to insuf?cient runtime information, researchers can only afford to apply of?ine model training to avoid signi?cant runtime training overhead. To address the challenges above, we designed and implemented MICSVM, a highly efficient parallel SVM for x86 based multi-core and many core architectures, such as the Intel Ivy Bridge CPUs and Intel Xeon Phi coprocessor (MIC).
Revised: September 22, 2014 |
Published: August 16, 2014
Citation
You Y., S. Song, H. Fu, A. Marquez, M. Mehri Dehanavi, K.J. Barker, and K. Cameron, et al. 2014.MIC-SVM: Designing A Highly Efficient Support Vector Machine For Advanced Modern Multi-Core and Many-Core Architectures. In IEEE 28th International Parallel and Distributed Processing Symposium (IPDPS 2014), May 19-23, 2014, Phoenix, Arizona, 809-818. Los Alamitos, California:IEEE Computer Society.PNNL-SA-102817.doi:10.1109/IPDPS.2014.88