high performance computing on graphics processing units: hgpu.org

MOTIVATION: Quantification of the contribution of genetic variation to phenotypic variation for complex traits becomes increasingly computationally demanding with increasing numbers of SNPs and individuals. To meet the challenges in making feasible large scale studies, we present the REACTA software. Adapted from ACTA (and, in turn, GCTA), it is tailored to exploit the parallelism present in modern traditional and GPU-accelerated machines, from workstations to supercomputers. RESULTS: We adapt the GRM estimation algorithm to remove limitations on memory, allowing the analysis of large datasets. We build on this to develop a version of the code able to efficiently exploit GPU-accelerated systems for both the GRM and REML parts of the analysis, offering substantial speedup over the traditional CPU version. We develop the ability to analyse multiple small regions of the genome across multiple compute nodes in parallel, following the "regional heritability" approach (Nagamine et al., 2012). We demonstrate the new software using 1,024 GPUs in parallel on one of the world’s fastest supercomputers. AVAILABILITY: The code is freely available at http://www.epcc.ed.ac.uk/software-products/