Crescent: A GPU-based Targeted Nanopore Sequence Selector

With the development of three-generation genome sequencing technology, targeted sequencing has become a fundamental need in virus detection, metagenomics expansion, and human polymorphisms detection. However, previous studies on CPU/GPU platforms have much lower throughput than Nanopore sequencers, and hardware designs have high design cost and flexibility issues. In this paper, we propose Crescent, a GPU-based targeted sequence selector. Specifically, on the algorithm level, we modify the popular-used algorithm without significantly decreasing the accuracy and then propose four key observations. On the architecture level, based on four key observations, we propose two-stage parallelism mechanisms (i.e., inter-task and intra-task) and other optimization strategies (i.e., data reuse and redundant computation elimination) to accelerate our modified algorithm. Our experimental results show that the throughput of Crescent on NVIDIA A100 is $10.84 \times$ and $2.30 \times$ higher than a state-of-the-art 16-thread and 72-thread CPU baseline, respectively.