Solving Sequences of Generalized Least-Squares Problems on   Multi-threaded Architectures
  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. 
Diego Fabregat-Traver (1), Yurii Aulchenko (2), Paolo Bientinesi (1),   ((1) AICES, RWTH Aachen, (2) Institute of Cytology a...
cs.MS cs.CE q-bio.GN