IPU-EpiDet: Identifying Gene Interactions on Massively Parallel Graph-Based AI Accelerators

Epistasis detection is a bioinformatics application that searches for associations between sets of single nucleotide polymorphisms (SNPs) and a given trait of a population. Epistasis detection is a computationally complex problem, especially when tackling interaction orders above two. This paper presents a pioneering approach for performing third-order epistasis searches that has been devised around the bulk-synchronous parallel (BSP) model of execution, which is used in processor designs that target artificial intelligence (AI) workloads, such as Graphcore’s Intelligence Processing Unit (IPU). We propose a parallelization approach and a set of optimizations to efficiently exploit the computation and communication resources of these novel AI processors to perform precise bioinformatics searches. The proposed approach achieves a performance of 3.9 Tera SNP combinations evaluated per second, scaled to sample size, on an IPU-M2000 AI accelerator, while close to a linear speedup (up to 15.01×) is achieved with 16 IPU-M2000 accelerators.