The substitution of nucleotides at specific positions in the genome of a population, known as single-nucleotide polymorphisms (SNPs), has been correlated with a number of important diseases. Complex conditions such as Alzheimer's disease or Crohn's disease are significantly linked to genetics when the impact of multiple SNPs is considered. SNPs often interact in an epistatic manner, where the joint effect of multiple SNPs may not be simply mapped to a linear additive combination of individual effects. Genome-wide association studies considering epistasis are computationally challenging, especially when performing triplet searches is required. Some contemporary computer architectures support fused XOR and population count as the highest throughput operations as part of tensor operations. This article presents a new approach for efficiently repurposing this capability to accelerate 2-way (pairs) and 3-way (triplets) epistasis detection searches. Experimental evaluation targeting the Turing GPU architecture resulted in previously unattainable levels of performance, with the proposal being able to evaluate up to 108.1 and 54.5 tera unique sets of SNPs per second, scaled to the sample size, in 2-way and 3-way searches, respectively.

中文翻译：

---

重新定位张量加速器以进行上位性检测

人群基因组中特定位置的核苷酸取代，被称为单核苷酸多态性（SNP），已与许多重要疾病相关。当考虑多个SNP的影响时，诸如阿尔茨海默氏病或​​克罗恩氏病等复杂疾病与遗传学有显着联系。SNP通常以上位性的方式相互作用，其中多个SNP的联合效应可能不能简单地映射到单个效应的线性累加组合。考虑到上位性的全基因组关联研究在计算上具有挑战性，特别是在需要执行三联体搜索时。一些现代计算机体系结构支持融合的XOR和填充计数，这是张量操作的一部分，是吞吐量最高的操作。本文介绍了一种新方法，可以有效地重新利用此功能来加速2向（对）和3向（三胞胎）上皮检测搜索。针对Turing GPU架构的实验评估导致了以前无法达到的性能水平，该提案能够每秒评估多达108.1和54.5 tera独特的SNP集，并按2和3方式搜索缩放到样本大小， 分别。