Accelerating 3-Way Epistasis Detection with CPU+GPU Processing

A Single Nucleotide Polymorphism (SNP) is a DNA variation occurring when a single nucleotide differs between individuals of a species. Some conditions can be explained with a single SNP. However, the combined effect of multiple SNPs, known as epistasis, allows to better correlate genotype with a number of complex traits. We propose a highly optimized GPU+CPU based approach for epistasis detection. The GPU portion of the approach relies only on CUDA cores to score sets of SNPs, based on the copresence of genetic variants and a specific outcome (case or control), making it suitable for a large number of computing devices. Considering datasets with different shapes (more SNPs than patients, or vice versa) and sizes, combining an analytical analysis and an experimental evaluation with five CPU+GPU configurations covering different GPU architectures from the last five years, we show that the performance achieved by our proposal is close to what is theoretically possible on the targeted GPUs. Comparing, in 3-way epistasis detection, with a state-of-the-art GPU-based approach which also does not rely on specialized hardware cores, MPI3SNP, the proposal is on average \(3.83\times \), \(2.72\times \), \(2.44\times \) and \(2.71\times \) faster on systems with a Titan X (Maxwell 2.0), a Titan XP (Pascal), a Titan V (Volta) and a GeForce 2070 SUPER (Turing) GPU, respectively.