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We present a parallel algorithm and scalable implementation for genomeanalysis, specifically the problem of finding overlaps and alignments for datafrom "third generation" long read sequencers. While long sequences of DNA offerenormous advantages for biological analysis and insight, current long readsequencing instruments have high error rates and therefore require differentapproaches to analysis than their short read counterparts. Our work focuses onan efficient distributed-memory parallelization of an accurate single-nodealgorithm for overlapping and aligning long reads. We achieve scalability ofthis irregular algorithm by addressing the competing issues of increasingparallelism, minimizing communication, constraining the memory footprint, andensuring good load balance. The resulting application, diBELLA, is the firstdistributed memory overlapper and aligner specifically designed for long readsand parallel scalability. We describe and present analyses for high leveldesign trade-offs and conduct an extensive empirical analysis that comparesperformance characteristics across state-of-the-art HPC systems as well as acommercial cloud architectures, highlighting the advantages of state-of-the-artnetwork technologies.

diBELLA: Distributed Long Read to Long Read Alignment