GPU Acceleration of Advanced k-mer Counting for Computational Genomics

Huiren Li; Anand Ramachandran; Deming Chen

doi:10.1109/ASAP.2018.8445084

2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP)

GPU Acceleration of Advanced k-mer Counting for Computational Genomics

Year: 2018, Pages: 1-4

DOI Bookmark: 10.1109/ASAP.2018.8445084

Authors

Huiren Li, Department of Electrical and Computer Engineering, University of Illinois, Urbana-Champaign, USA
Anand Ramachandran, Department of Electrical and Computer Engineering, University of Illinois, Urbana-Champaign, USA
Deming Chen, Department of Electrical and Computer Engineering, University of Illinois, Urbana-Champaign, USA

Abstract

k-mer counting is a popular pre-processing step in many bioinformatic algorithms. KMC2 is one of the most popular tools for k-mer counting. In this work, we leverage the computational power of the GPU to accelerate KMC2. Our goal is to reduce the overall runtime of many genome analysis tasks that use k-mer counting as an essential step. Compared to KMC2 running on a single CPU thread, our implementation using the GPU achieved 4.03x speedup when using one CPU thread, and 5.88x speedup when using four CPU threads. This speedup is significant because accelerating k-mer counting is challenging due to reasons like serialized portions of code and overhead of disk operations.

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GPU Acceleration of Advanced k-mer Counting for Computational Genomics

Authors

Abstract

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