1,000x Faster than PLINK: Combined FPGA and GPU Accelerators for Logistic Regression-based Detection of Epistasis

Lars Wienbrandt, Jan Christian Kässens, Matthias Hübenthal, David Ellinghaus
Year of publication:
Journal title abbreviated:
Journal of Computational Science
Journal title long:
Journal of Computational Science
Impact factor:
Logistic regression as implemented in PLINK is a powerful and commonly used framework for assessing gene-gene interactions. However, fitting regression models for each pair of markers in a genome-wide dataset is a computationally intensive task, for which reason pre-filtering techniques and fast epistasis screenings are applied to reduce the computational burden. We demonstrate that employing a combination of a Xilinx UltraScale FPGA with an Nvidia Tesla GPU leads to runtimes of only minutes for logistic regression tests on a genome-wide scale, resulting in a speedup of more than 1000 up to 1600 when compared to multi-threaded PLINK on a server-grade computing platform. This article is an extended version of our conference paper [1].