Solving Sequences of Generalized Least-Squares Problems on Multi-threaded Architectures
Mathematical Software
2013-05-02 v1 Computational Engineering, Finance, and Science
Genomics
Abstract
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.
Keywords
Cite
@article{arxiv.1210.7325,
title = {Solving Sequences of Generalized Least-Squares Problems on Multi-threaded Architectures},
author = {Diego Fabregat-Traver and Yurii Aulchenko and Paolo Bientinesi},
journal= {arXiv preprint arXiv:1210.7325},
year = {2013}
}