Generalized linear mixed-effects models in the context of genome-wide
association studies (GWAS) represent a formidable computational challenge: the
solution of millions of correlated generalized least-squares problems, and the
processing of terabytes of data. We present high performance in-core and
out-of-core shared-memory algorithms for GWAS: By taking advantage of
domain-specific knowledge, exploiting multi-core parallelism, and handling data
efficiently, our algorithms attain unequalled performance. When compared to
GenABEL, one of the most widely used libraries for GWAS, on a 12-core processor
we obtain 50-fold speedups. As a consequence, our routines enable genome
studies of unprecedented size
