PLSSVM-Parallel Least Squares Support Vector Machine

Support Vector Machines are used in supervised learning. For large dense data sets, however, even optimized implementations like LIBSVM or ThunderSVM do not scale well on massively parallel hardware: They are algorithmically based on Sequential Minimal Optimization, and we are not aware of a performance portable implementation supporting CPUs and GPUs from different vendors. Our Parallel Least Squares Support Vector Machine (PLSSVM) solves both of these issues. First, PLSSVM resorts to the least squares formulation, and thus to an algorithm that is well-suited for massive parallelism. Second, PLSSVM provides a hardware-independent efficient implementation using OpenMP, CUDA, HIP, OpenCL, and SYCL.