Parallel Gene Upstream Comparison via Multi-Level Hash Tables on GPU

The region of DNA immediately in front of a gene body (also called upstream region) contains short (8-20 base) sequence motifs that help to control when that gene is turned on and off. Unfortunately, these motifs are generally unknown and commonly degenerate. In this work, we propose a motif-finding framework that, given a set of gene upstream regions, performs their all-to-all pairwise comparison and identifies all the motifs of length k (k-mers) that are common to any pair of upstream regions or differ in at most d characters. Our framework stores the k-mers found in each gene in a multi-level hash table. Our hash table design optimizes hash table comparison (rather than hash table insertion or lookup), is highly parallelizable and easily maps onto GPU. We propose four GPU kernels for pairwise hash table comparison, each leveraging a distinct parallelization approach. We study how different factors (the hash function, the number of buckets and the settings of other implementation-specific parameters) affect the performance of our implementation. Experimental results performed using an average-size yeast genome show that our fastest GPU kernel outperforms an 8-thread, cache-efficient CPU implementation by a factor of ~52x.