Recent developments in programmable, highly parallel Graphics Processing Units (GPUs) have enabled high performance implementations of machine learning algorithms. We describe a solver for Support Vector Machine training running on a GPU, using the Sequential Minimal Optimization algorithm and an adaptive first and second order working set selection heuristic, which achieves speedups of 9-35× over LIBSVM running on a traditional processor. We also present a GPU-based system for SVM classification which achieves speedups of 81-138× over LIBSVM (5-24× over our own CPU based SVM classifier).
Another approach utilizes graphics processing units (GPU) for SVM speedup REF .
Fast support vector machine training and classification on graphics processors
ICML '08
Computer Science