UC Berkeley

Aligning a set of query sequences to a set of target sequences is an important task in bioinformatics. In this work we present merAligner, a highly parallel sequence aligner that implements a seed-and-extend algorithm and employs parallelism in all of its components. MerAligner relies on a high performance distributed hash table (seed index) and uses onesided communication capabilities of the Unified Parallel C to facilitate a fine-grained parallelism. We leverage communication optimizations at the construction of the distributed hash table and software caching schemes to reduce communication during the aligning phase. Additionally, merAligner preprocesses the target sequences to extract properties enabling exact sequence matching with minimal communication. Finally, we efficiently parallelize the I/O intensive phases and implement an effective load balancing scheme. Results show that merAligner exhibits efficient scaling up to thousands of cores on a Cray XC30 supercomputer using real human and wheat genome data while significantly outperforming existing parallel alignment tools.