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.

Speaker 

Marcel Breyer is a PhD student at the University of Stuttgart, Germany. His main field of research is on performance portability on heterogeneous hardware, which includes a GPU-accelerated PLSSVM using SYCL.