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Approximating the recent phylogeny of $N$ phased haplotypes at a set ofvariants along the genome is a core problem in modern population genomics andcentral to performing genome-wide screens for association, selection,introgression, and other signals. The Li & Stephens (LS) model provides asimple yet powerful hidden Markov model for inferring the recent ancestry at agiven variant, represented as an $N \times N$ distance matrix based onposterior decodings. However, existing posterior decoding implementations forthe LS model cannot scale to modern datasets with tens or hundreds of thousandsof genomes. This work focuses on providing a high-performance engine to computethe LS model, enabling users to rapidly develop a range of variant-specificancestral inference pipelines on top, exposed via an easy to use package,kalis, in the statistical programming language R. kalis exploits bothmulti-core parallelism and modern CPU vector instruction sets to enable scalingto problem sizes that would previously have been prohibitively slow to workwith. The resulting distance matrices enable local ancestry, selection, andassociation studies in modern large scale genomic datasets.

kalis: A Modern Implementation of the Li & Stephens Model for Local Ancestry Inference in R