EPIClear: Exploiting Domain-Specific Features for Epistasis Detection Acceleration on Tensor Cores
Ricardo Nobre, Miguel Graça, Leonel A. Sousa, Aleksandar D. Ilic · 2025
High-order epistasis detection is challenging, making it important to efficiently leverage today's supercomputers.The fastest approaches are those relying on binary precision tensorized operations on modern GPUs.This paper presents a novel approach that significantly surpasses the state-of-theart in high-order epistasis detection by leveraging previously unexplored domain-specific features on the genotype distribution patterns in the dataset.It accelerates time-to-solution with a computational step that reduces the volume of data that needs to be processed to count genotypes.The proposed approach achieves 4× higher performance on a A100 GPU than the previously fastest approach when processing balanced genotype distributions.Evaluation on datasets with unbalanced genotype distributions, which is something that is bound to happen in real datasets, results in significantly higher performance.The proposed accelerating scheme exhibits high scalability.Epistasis detection searches on the MeluXina supercomputer with 32 A100 GPUs resulted in a speedup of up to 30× in comparison to a single GPU, and in achieving a performance scaled to sample size of up to 13 Peta SNP combinations per second for the genotype distribution most unfavorable to the proposed accelerating scheme.