High-Performance Incremental SVM Learning on Intel® Xeon Phi™ Processors

Support vector machines (SVMs) are conventionally batch trained. Such implementations can be very inefficient for online streaming applications demanding real-time guarantees, as the inclusion of each new data point requires retraining of the model from scratch. This paper focuses on the high-performance implementation of an accurate incremental SVM algorithm on Intel\(^{{\tiny \textregistered }}\) Xeon Phi\(^{\small {\tiny \textsc {tm}}}\) processors that efficiently updates the trained SVM model with streaming data. We propose a novel cycle break heuristic to fix an inherent drawback of the algorithm that leads to a deadlock scenario which is not acceptable in real-world applications. We further employ intelligent caching of dynamically changing data as well as other programming optimization ideas to speed up the incremental SVM algorithm. Experiments on a number of real-world datasets show that our implementation achieves high performance on Intel\(^{{\tiny \textregistered }}\) Xeon Phi\(^{\small {\tiny \textsc {tm}}}\) processors (\(1.1-2.1{\times }\) faster than Intel\(^{{\tiny \textregistered }}\) Xeon\(^{{\tiny \textregistered }}\) processors) and is up to \(2.1{\times }\) faster than existing high-performance incremental algorithms while achieving comparable accuracy.