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The emergence of Next Generation Sequencing (NGS) platforms has increased thethroughput of genomic sequencing and in turn the amount of data that needs tobe processed, requiring highly efficient computation for its analysis. In thiscontext, modern architectures including accelerators and non-volatile memoryare essential to enable the mass exploitation of these bioinformaticsworkloads. This paper presents a redesign of the main component of astate-of-the-art reference-free method for variant calling, SMUFIN, which hasbeen adapted to make the most of GPUs and NVM devices. SMUFIN relies oncounting the frequency of \textit{k-mers} (substrings of length $k$) in DNAsequences, which also constitutes a well-known problem for many bioinformaticsworkloads, such as genome assembly. We propose techniques to improve theefficiency of k-mer counting and to scale-up workloads like \sm that used torequire 16 nodes of \mn to a single machine with a GPU and NVM drives. Resultsshow that although the single machine is not able to improve the time tosolution of 16 nodes, its CPU time is 7.5x shorter than the aggregate CPU timeof the 16 nodes, with a reduction in energy consumption of 5.5x.

Accelerating K-mer Frequency Counting with GPU and Non-Volatile Memory