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 one-sided 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.

De novo whole genome assembly reconstructs genomic sequences from short, overlapping, and potentially erroneous DNA segments and is one of the most important computations in modern genomics. This work presents HipMer, the first high-quality end-to-end de novo assembler designed for extreme scale analysis, via efficient parallelization of the Meraculous code. First, we significantly improve scalability of parallel k-mer analysis for complex repetitive genomes that exhibit skewed frequency distributions. Next, we optimize the traversal of the de Bruijn graph of k-mers by employing a novel communication-avoiding parallel algorithm in a variety of use-case scenarios. Finally, we parallelize the Meraculous scaffolding modules by leveraging the one-sided communication capabilities of the Unified Parallel C while effectively mitigating load imbalance. Large-scale results on a Cray XC30 using grand-challenge genomes demonstrate efficient performance and scalability on thousands of cores. Overall, our pipeline accelerates Meraculous performance by orders of magnitude, enabling the complete assembly of the human genome in just 8.4 minutes on 15K cores of the Cray XC30, and creating unprecedented capability for extreme-scale genomic analysis.

High-throughput “next generation” genome sequencing technologies are producing a flood of inexpensive genetic information that is invaluable to genomics research. Sequences of millions of genetic markers are being produced, providing genomics researchers with the opportunity to construct highresolution genetic maps for many complicated genomes. However, the current generation of genetic mapping tools were designed for the small data setting, and are now limited by the prohibitively slow clustering algorithms they employ in the genetic marker-clustering stage. In this work, we present a new approach to genetic mapping based on a fast clustering algorithm that exploits the geometry of the data. Our theoretical and empirical analysis shows that the algorithm can correctly recover linkage groups. Using synthetic and real-world data, including the grand-challenge wheat genome, we demonstrate that our approach can quickly process orders of magnitude more genetic markers than existing tools while retaining - and in some cases even improving - the quality of genetic marker clusters.

De novo whole genome assembly reconstructs genomic sequence from short, overlapping, and potentially erroneous fragments called reads. We study optimized parallelization of the most time-consuming phases of Meraculous, a state of-the-art production assembler. First, we present a new parallel algorithm for k-mer analysis, characterized by intensive communication and I/O requirements, and reduce the memory requirements by 6.93×. Second, we efficiently parallelize de Bruijn graph construction and traversal, which necessitates a distributed hash table and is a key component of most de novo assemblers. We provide a novel algorithm that leverages one-sided communication capabilities of the Unified Parallel C (UPC) to facilitate the requisite fine-grained parallelism and avoidance of data hazards, while analytically proving its scalability properties. Overall results show unprecedented performance and efficient scaling on up to 15,360 cores of a Cray XC30, on human genome as well as the challenging wheat genome, with performance improvement from days to seconds.