The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Authors: Beatrice Branchini, Alberto Zeni, and Marco Santambrogio (Polytechnic University of Milan)
Abstract: Pointing out genetic mutations is pivotal to enable clinicians to prescribe personalized therapies to their patients. Genome Analysis ToolKit's HaplotypeCaller, relying on the Pair Hidden Markov Model (PairHMM) algorithm, is one of the most used applications to identify such variants. However, the PairHMM represents the bottleneck for this tool. Deploying such an algorithm on hardware accelerators represents a valuable solution. Nevertheless, State-of-the-Art designs do not have the flexibility to support the length variability of the input sequences and are not usable in real-life applicative scenarios. For these reasons, this work presents a GPU accelerator for the PairHMM capable of supporting sequences of any length, thanks to a dynamic memory swap methodology, overcoming the limitation of literature solutions. Our accelerator achieves an 8154× speedup over the software baseline, surpassing the most-performant State-of-the-Art design up to 1.6×.
Best Poster Finalist (BP): no
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