Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6421231 | Applied Mathematics and Computation | 2014 | 12 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Diego Fabregat-Traver, Yurii S. Aulchenko, Paolo Bientinesi,