Accelerating Machine Learning Applications on Graphics Processors

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 Platt’s 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 63-133× over LIBSVM.