Sparse grids have already been successfully used in various high-performance computing (HPC) applications, including data mining. In this article, we take a legacy classification kernel previously optimized for the AVX2 instruction set and investigate the benefits of using the newer AVX-512-based multi-and many-core architectures. In particular, the Knights Landing (KNL) processor is used to study the possible performance gains of the code. Not all kernels benefit equally from such architectures, therefore choices in optimization steps and KNL cluster and memory modes need to be filtered through the lens of the code implementation at hand. With a less traditional approach of manual vectorization through instruction-level intrinsics, our kernel provides a differently faceted look into the optimization process. Observations stem from results obtained for node-and cluster-level classification simulations with up to 2^{^}28 multidimensional training data points, using the CooLMUC-3cluster of the Leibniz Supercomputing Center (LRZ) in Garching, Germany.