A GPU-accelerated Parallel Least Squares Support Vector Machine (PLSSVM) was developed to classify dense datasets with hundreds of thousands data points and more than a thousand features. It beats the state-of-the-art sequential minimal optimization (SMO) implementations like LIBSVM.

PLSSVM supports many different hardware architectures that include any Intel CPU and GPUs, and NVIDIA* and AMD* GPUs that use different back ends written in OpenMP*, CUDA*, HIP, OpenCL™ code, and SYCL*. This talk compares these back ends on different architectures in relation to their implementation and performance characteristics.