Accelerating Machine Learning Applications on Graphics Processors

Recent developments in programmable, highly par-allel Graphics Processing Units (GPUs) have enabled high performance implementations of machine learn-ing algorithms. We describe a solver for Support Vec-tor 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 LIB-SVM running on a traditional processor. We also present a GPU-based system for SVM classification which achieves speedups of 63-133 × over LIBSVM.