RapidGKC: GPU-Accelerated K-Mer Counting

Many bioinformatics applications, e.g., genome assembly, genome profiling, and sequence alignment, break biological sequences into k-mers, or length-k substrings, for sub-sequent processing. In these applications, counting the number of occurrences of distinct k-mers is a common but expensive step due to the data and computation intensity. As such, prior work proposed to parallelize this task and utilize GPUs for further acceleration. However, these solutions under-utilize the GPU parallelism because the encoding format of intermediate data forces sequential decoding. To address this problem, we design a new encoding scheme for variable-length genomic data to support parallel encoding and decoding. Furthermore, we propose a novel rule to select common substrings among k-mers for partitioning, reducing the space cost as well as facilitating efficient parallel processing. Finally, we parallelize the entire workflow of partitioning and counting through pipelining, CPU-GPU co-processing, and work stealing. As a result, RapidGKC, our end-to-end GPU-accelerated k-mer counting system, outperforms state-of-the-art CPU-based and GPU-accelerated methods on real-world datasets.